Soumettre la recherche
Mettre en ligne
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
•
Télécharger en tant que PPT, PDF
•
2 j'aime
•
1,137 vues
Cloudera, Inc.
Suivre
Technologie
Signaler
Partager
Signaler
Partager
1 sur 37
Télécharger maintenant
Recommandé
Hw09 Hadoop Applications At Yahoo!
Hw09 Hadoop Applications At Yahoo!
Cloudera, Inc.
Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
Luke Han
Machine Learning with H2O, Spark, and Python at Strata SJ 2015-by Cliff Click and Michal Malohlava - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning with H2O, Spark, and Python at Strata 2015
Machine Learning with H2O, Spark, and Python at Strata 2015
Sri Ambati
Presto in the cloud
Presto in the cloud
Qubole
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure. GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Databricks
http://flink-forward.org/kb_sessions/multi-tenant-flink-as-a-service-on-yarn/ Since June 2016, Flink-as-a-service has been available to researchers and companies in Sweden from the Swedish ICT SICS Data Center at www.hops.site using the HopsWorks platform. Flink applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin on YARN. Flink applications are run within a project on a YARN cluster with the novel property that Flink applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics that are protected from access by users that are not members of the project. Hopsworks is entirely UI-driven, is open-source, and Flink applications that include Kafka topics can be created in a few mouse clicks. In this talk we will discuss the challenges in building a metered version of Flink-as-a-Service for YARN, experiences with Flink-on-YARN, and some of the possibilities that Hopsworks opens up for building secure, multi-ten
Jim Dowling - Multi-tenant Flink-as-a-Service on YARN
Jim Dowling - Multi-tenant Flink-as-a-Service on YARN
Flink Forward
Learn how to deploy a managed Presto environment to interactively query log data on AWS Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR. Learning Objectives: • Learn how to deploy a managed Presto environment running on Amazon EMR • Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances • Learn how other customers are using Presto to analyze large data sets
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Amazon Web Services
Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp. In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
Rental Cars and Industrialized Learning to Rank with Sean Downes
Rental Cars and Industrialized Learning to Rank with Sean Downes
Databricks
Recommandé
Hw09 Hadoop Applications At Yahoo!
Hw09 Hadoop Applications At Yahoo!
Cloudera, Inc.
Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
Luke Han
Machine Learning with H2O, Spark, and Python at Strata SJ 2015-by Cliff Click and Michal Malohlava - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning with H2O, Spark, and Python at Strata 2015
Machine Learning with H2O, Spark, and Python at Strata 2015
Sri Ambati
Presto in the cloud
Presto in the cloud
Qubole
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure. GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Databricks
http://flink-forward.org/kb_sessions/multi-tenant-flink-as-a-service-on-yarn/ Since June 2016, Flink-as-a-service has been available to researchers and companies in Sweden from the Swedish ICT SICS Data Center at www.hops.site using the HopsWorks platform. Flink applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin on YARN. Flink applications are run within a project on a YARN cluster with the novel property that Flink applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics that are protected from access by users that are not members of the project. Hopsworks is entirely UI-driven, is open-source, and Flink applications that include Kafka topics can be created in a few mouse clicks. In this talk we will discuss the challenges in building a metered version of Flink-as-a-Service for YARN, experiences with Flink-on-YARN, and some of the possibilities that Hopsworks opens up for building secure, multi-ten
Jim Dowling - Multi-tenant Flink-as-a-Service on YARN
Jim Dowling - Multi-tenant Flink-as-a-Service on YARN
Flink Forward
Learn how to deploy a managed Presto environment to interactively query log data on AWS Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR. Learning Objectives: • Learn how to deploy a managed Presto environment running on Amazon EMR • Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances • Learn how other customers are using Presto to analyze large data sets
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Amazon Web Services
Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp. In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
Rental Cars and Industrialized Learning to Rank with Sean Downes
Rental Cars and Industrialized Learning to Rank with Sean Downes
Databricks
This talk was given by Joel Koshy (Senior Software Engineer at LinkedIn) at the Hadoop Summit (June 2013).
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
Amy W. Tang
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2
SSR: Structured Streaming for R and Machine Learning
SSR: Structured Streaming for R and Machine Learning
felixcss
Organizations from small startups to large enterprises are rapidly adopting Apache Spark on Amazon EMR in Amazon Web Services (AWS) to run streaming analytics, data science, machine learning, and batch processing workloads. These customers can quickly create big data architectures within minutes, and decouple compute and storage with Amazon S3 as a highly scalable, durable, and secure data lake, lower costs using Amazon EC2 Spot Instances and Auto Scaling, and utilize a wide range of encryption and access control features. In this session, we discuss how customers are using Spark on AWS and common architectures for easily running performant Spark clusters at scale and low cost with Amazon EMR.
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Databricks
Since mid-2016, Spark-as-a-Service has been available to researchers in Sweden from the Rise SICS ICE Data Center at www.hops.site. In this session, Dowling will discuss the challenges in building multi-tenant Spark structured streaming applications on YARN that are metered and easy-to-debug. The platform, called Hopsworks, is in an entirely UI-driven environment built with only open-source software. Learn how they use the ELK stack (Elasticsearch, Logstash and Kibana) for logging and debugging running Spark streaming applications; how they use Grafana and InfluxDB for monitoring Spark streaming applications; and, finally, how Apache Zeppelin can provide interactive visualizations and charts to end-users. This session will also show how Spark applications are run within a ‘project’ on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In addition, hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.afka topics are protected from access by users that are not members of the project. We will also discuss the experiences of our users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Databricks
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
Amazon Web Services
ApacheCon Big Data Europe 2016 talk. Hopsworks with secure Spark/Flink/Kafka as a service.
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Jim Dowling
Hopsworks Multi-tenant hadoop-as-a-service
Strata Hadoop Hopsworks
Strata Hadoop Hopsworks
Jim Dowling
Spark Summit 2016 talk by Kaarthik Sivashanmugam (Microsoft)
Mobius: C# Language Binding For Spark
Mobius: C# Language Binding For Spark
Spark Summit
We are witnessing a proliferation of big data, which has lead to a zoo of data processing systems. Each system providing a different set of features. For example, Spark provides scalability to analytic tasks, but Java 8 Streams provides low-latency. Furthermore, complex applications, such as ETL and ML, are now requiring a mixture of platforms to perform tasks efficiently. In such complex data analytics pipelines, the use of multiple data processing system is not only for performance reasons, but also because of data diversity. Datasets often natively reside on different data formats and storage engines. Unfortunately, developers are left alone in the challenging tasks of: (1) choosing the right platform for their applications; and (2) performing tedious and costly data migration and integration tasks to obtain the results. In this talk, we will present Rheem, an open source scalable cross-platform system that frees developers from these burdens. Rheem provides an abstraction layer on top of Spark (and other processing platforms) with the aim of enabling cross-platform optimization and interoperability. It automatically selects the best data processing platforms for a given task and also handles the cross-platform execution. In particular, we will discuss how Rheem allows Spark to work in tandem with other platforms in order to achieve higher performance. We will also show how easy a developer can write complex applications on top of Rheem to seamlessly use multiple different data processing platforms according to their tasks at hand. Using Rheem developers do not have to worry about the integration or data migration between Spark and other platforms.
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Databricks
The slides for HadoopCon 2014 in Taiwan.
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
viirya
Big Data Batch Layer implementation with Amazon Web Services Cloud Platform, Apache Spark, Hadoop, Apache Cassandra, AngularJS, Java Restful Web Services. This can be extended to implement real world use cases.
Big data Lambda Architecture - Batch Layer Hands On
Big data Lambda Architecture - Batch Layer Hands On
hkbhadraa
Apache Spark has been a great technology for processing and analyzing Big Data. However, it is not accessible to business users, who don’t have technical or programming skills. In this talk, I’ll talk about recent efforts in the space of “Conversational analytics”. This paradigm allows any user to ask text and voice questions, in natural language, of their data to a bot and receive back a natural language and visual result. A key technology is natural language to SQL translation, where we translate natural language queries from a user into Spark SQL queries that can go against a Databricks system, and that can be easily trained on different schemas and databases. This NLP technology needs to be further combined with dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation in order to achieve the goal of “Conversational Analytics”. Using such a technology, a user can ask, in plain English, “How many cases of Covid were there in the last 2 months in states that had no social distancing mandates by type of transmission”, and then dig deeper into the results in a conversational manner to uncover hidden insights from Covid datasets in a Spark instance. We believe that having access to such data and insights at their fingertips can help users make appropriate decisions quickly, improve data literacy and even overcome the scourge of fake news for the general public.
Natural Language Query and Conversational Interface to Apache Spark
Natural Language Query and Conversational Interface to Apache Spark
Databricks
Dask Tutorial at PyConDE / PyData Karlsruhe 2018. These were the introductory slides that mainly contain the link to Matthew Rocklin's Dask workshop at PyData NYC 2018 whereon this workshop was based.
Scalable Scientific Computing with Dask
Scalable Scientific Computing with Dask
Uwe Korn
Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully. Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018 https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Just enough DevOps for Data Scientists (Part II)
Just enough DevOps for Data Scientists (Part II)
Databricks
Slides used for the talk "Developing Apache Spark Jobs in .NET using Mobius" at dotnetfringe 20016 (http://lanyrd.com/2016/netfringe/sfcxpx). Apache Spark is an open source data processing framework built for big data processing and analytics. Ease of programming and high performance relative to the traditional big data tools and platforms and a unified API to solve a diverse set of complex data problems drove the rapid adoption of Spark in the industry. Apache Spark APIs in Scala, Java, Python and R cater to a wide range of big data professionals and a variety of functional roles. Mobius is an open source project that aims to bring Spark's rich set of capabilities to the .NET community. Mobius project added C# as another first-class programming language for Apache Spark and currently supports RDD, DataFrame and Streaming API. With Mobius, developers can build Spark jobs in C# and reuse their existing .NET libraries with Apache Spark. Mobius is open-sourced at http://github.com/Microsoft/Mobius. This project has received great support from the .NET community and positive feedback from the Spark enthusiasts
Developing apache spark jobs in .net using mobius
Developing apache spark jobs in .net using mobius
shareddatamsft
Data Pipeline with Kafka, This slide include Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
Data Pipeline with Kafka
Data Pipeline with Kafka
Peerapat Asoktummarungsri
From Single-Tenant Hadoop to 3000 Tenants in Apache Spark: Experiences from Watson Analytics
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this webinar, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures and best practices to quickly create Spark clusters using Amazon Elastic MapReduce (EMR), and ways to use Spark with Amazon Redshift, Amazon DynamoDB, Amazon Kinesis, and other big data applications in the Apache Hadoop ecosystem. Learning Objectives: Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing How to deploy and tune scalable clusters running Spark on Amazon EMR How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3 Common architectures to leverage Spark with DynamoDB, Redshift, Kinesis, and more
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
Amazon Web Services
Supercharging ETL with Spark Slides from first Spark Meetup London
ETL with SPARK - First Spark London meetup
ETL with SPARK - First Spark London meetup
Rafal Kwasny
Hadoop World 2009 talk on rapid prototyping of data intensive web applications with Hadoop, Hive, Amazon EC2, Python, and Ruby on Rails. Describes the process of building the open source trend tracking site trendingtopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Peter Skomoroch
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Introduction to AWS Glue
Introduction to AWS Glue
Amazon Web Services
Ingesting data from the internet into Azure Data Lake and Visualizing in Power BI
Big Data Analytics from Azure Cloud to Power BI Mobile
Big Data Analytics from Azure Cloud to Power BI Mobile
Roy Kim
Contenu connexe
Tendances
This talk was given by Joel Koshy (Senior Software Engineer at LinkedIn) at the Hadoop Summit (June 2013).
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
Amy W. Tang
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2
SSR: Structured Streaming for R and Machine Learning
SSR: Structured Streaming for R and Machine Learning
felixcss
Organizations from small startups to large enterprises are rapidly adopting Apache Spark on Amazon EMR in Amazon Web Services (AWS) to run streaming analytics, data science, machine learning, and batch processing workloads. These customers can quickly create big data architectures within minutes, and decouple compute and storage with Amazon S3 as a highly scalable, durable, and secure data lake, lower costs using Amazon EC2 Spot Instances and Auto Scaling, and utilize a wide range of encryption and access control features. In this session, we discuss how customers are using Spark on AWS and common architectures for easily running performant Spark clusters at scale and low cost with Amazon EMR.
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Databricks
Since mid-2016, Spark-as-a-Service has been available to researchers in Sweden from the Rise SICS ICE Data Center at www.hops.site. In this session, Dowling will discuss the challenges in building multi-tenant Spark structured streaming applications on YARN that are metered and easy-to-debug. The platform, called Hopsworks, is in an entirely UI-driven environment built with only open-source software. Learn how they use the ELK stack (Elasticsearch, Logstash and Kibana) for logging and debugging running Spark streaming applications; how they use Grafana and InfluxDB for monitoring Spark streaming applications; and, finally, how Apache Zeppelin can provide interactive visualizations and charts to end-users. This session will also show how Spark applications are run within a ‘project’ on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In addition, hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.afka topics are protected from access by users that are not members of the project. We will also discuss the experiences of our users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Databricks
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
Amazon Web Services
ApacheCon Big Data Europe 2016 talk. Hopsworks with secure Spark/Flink/Kafka as a service.
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Jim Dowling
Hopsworks Multi-tenant hadoop-as-a-service
Strata Hadoop Hopsworks
Strata Hadoop Hopsworks
Jim Dowling
Spark Summit 2016 talk by Kaarthik Sivashanmugam (Microsoft)
Mobius: C# Language Binding For Spark
Mobius: C# Language Binding For Spark
Spark Summit
We are witnessing a proliferation of big data, which has lead to a zoo of data processing systems. Each system providing a different set of features. For example, Spark provides scalability to analytic tasks, but Java 8 Streams provides low-latency. Furthermore, complex applications, such as ETL and ML, are now requiring a mixture of platforms to perform tasks efficiently. In such complex data analytics pipelines, the use of multiple data processing system is not only for performance reasons, but also because of data diversity. Datasets often natively reside on different data formats and storage engines. Unfortunately, developers are left alone in the challenging tasks of: (1) choosing the right platform for their applications; and (2) performing tedious and costly data migration and integration tasks to obtain the results. In this talk, we will present Rheem, an open source scalable cross-platform system that frees developers from these burdens. Rheem provides an abstraction layer on top of Spark (and other processing platforms) with the aim of enabling cross-platform optimization and interoperability. It automatically selects the best data processing platforms for a given task and also handles the cross-platform execution. In particular, we will discuss how Rheem allows Spark to work in tandem with other platforms in order to achieve higher performance. We will also show how easy a developer can write complex applications on top of Rheem to seamlessly use multiple different data processing platforms according to their tasks at hand. Using Rheem developers do not have to worry about the integration or data migration between Spark and other platforms.
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Databricks
The slides for HadoopCon 2014 in Taiwan.
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
viirya
Big Data Batch Layer implementation with Amazon Web Services Cloud Platform, Apache Spark, Hadoop, Apache Cassandra, AngularJS, Java Restful Web Services. This can be extended to implement real world use cases.
Big data Lambda Architecture - Batch Layer Hands On
Big data Lambda Architecture - Batch Layer Hands On
hkbhadraa
Apache Spark has been a great technology for processing and analyzing Big Data. However, it is not accessible to business users, who don’t have technical or programming skills. In this talk, I’ll talk about recent efforts in the space of “Conversational analytics”. This paradigm allows any user to ask text and voice questions, in natural language, of their data to a bot and receive back a natural language and visual result. A key technology is natural language to SQL translation, where we translate natural language queries from a user into Spark SQL queries that can go against a Databricks system, and that can be easily trained on different schemas and databases. This NLP technology needs to be further combined with dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation in order to achieve the goal of “Conversational Analytics”. Using such a technology, a user can ask, in plain English, “How many cases of Covid were there in the last 2 months in states that had no social distancing mandates by type of transmission”, and then dig deeper into the results in a conversational manner to uncover hidden insights from Covid datasets in a Spark instance. We believe that having access to such data and insights at their fingertips can help users make appropriate decisions quickly, improve data literacy and even overcome the scourge of fake news for the general public.
Natural Language Query and Conversational Interface to Apache Spark
Natural Language Query and Conversational Interface to Apache Spark
Databricks
Dask Tutorial at PyConDE / PyData Karlsruhe 2018. These were the introductory slides that mainly contain the link to Matthew Rocklin's Dask workshop at PyData NYC 2018 whereon this workshop was based.
Scalable Scientific Computing with Dask
Scalable Scientific Computing with Dask
Uwe Korn
Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully. Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018 https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Just enough DevOps for Data Scientists (Part II)
Just enough DevOps for Data Scientists (Part II)
Databricks
Slides used for the talk "Developing Apache Spark Jobs in .NET using Mobius" at dotnetfringe 20016 (http://lanyrd.com/2016/netfringe/sfcxpx). Apache Spark is an open source data processing framework built for big data processing and analytics. Ease of programming and high performance relative to the traditional big data tools and platforms and a unified API to solve a diverse set of complex data problems drove the rapid adoption of Spark in the industry. Apache Spark APIs in Scala, Java, Python and R cater to a wide range of big data professionals and a variety of functional roles. Mobius is an open source project that aims to bring Spark's rich set of capabilities to the .NET community. Mobius project added C# as another first-class programming language for Apache Spark and currently supports RDD, DataFrame and Streaming API. With Mobius, developers can build Spark jobs in C# and reuse their existing .NET libraries with Apache Spark. Mobius is open-sourced at http://github.com/Microsoft/Mobius. This project has received great support from the .NET community and positive feedback from the Spark enthusiasts
Developing apache spark jobs in .net using mobius
Developing apache spark jobs in .net using mobius
shareddatamsft
Data Pipeline with Kafka, This slide include Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
Data Pipeline with Kafka
Data Pipeline with Kafka
Peerapat Asoktummarungsri
From Single-Tenant Hadoop to 3000 Tenants in Apache Spark: Experiences from Watson Analytics
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this webinar, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures and best practices to quickly create Spark clusters using Amazon Elastic MapReduce (EMR), and ways to use Spark with Amazon Redshift, Amazon DynamoDB, Amazon Kinesis, and other big data applications in the Apache Hadoop ecosystem. Learning Objectives: Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing How to deploy and tune scalable clusters running Spark on Amazon EMR How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3 Common architectures to leverage Spark with DynamoDB, Redshift, Kinesis, and more
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
Amazon Web Services
Supercharging ETL with Spark Slides from first Spark Meetup London
ETL with SPARK - First Spark London meetup
ETL with SPARK - First Spark London meetup
Rafal Kwasny
Hadoop World 2009 talk on rapid prototyping of data intensive web applications with Hadoop, Hive, Amazon EC2, Python, and Ruby on Rails. Describes the process of building the open source trend tracking site trendingtopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Peter Skomoroch
Tendances
(20)
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
SSR: Structured Streaming for R and Machine Learning
SSR: Structured Streaming for R and Machine Learning
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Strata Hadoop Hopsworks
Strata Hadoop Hopsworks
Mobius: C# Language Binding For Spark
Mobius: C# Language Binding For Spark
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Big data Lambda Architecture - Batch Layer Hands On
Big data Lambda Architecture - Batch Layer Hands On
Natural Language Query and Conversational Interface to Apache Spark
Natural Language Query and Conversational Interface to Apache Spark
Scalable Scientific Computing with Dask
Scalable Scientific Computing with Dask
Just enough DevOps for Data Scientists (Part II)
Just enough DevOps for Data Scientists (Part II)
Developing apache spark jobs in .net using mobius
Developing apache spark jobs in .net using mobius
Data Pipeline with Kafka
Data Pipeline with Kafka
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit EU talk by Ruben Pulido Behar Veliqi
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
ETL with SPARK - First Spark London meetup
ETL with SPARK - First Spark London meetup
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Similaire à Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Introduction to AWS Glue
Introduction to AWS Glue
Amazon Web Services
Ingesting data from the internet into Azure Data Lake and Visualizing in Power BI
Big Data Analytics from Azure Cloud to Power BI Mobile
Big Data Analytics from Azure Cloud to Power BI Mobile
Roy Kim
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
BDA311 Introduction to AWS Glue
BDA311 Introduction to AWS Glue
Amazon Web Services
Hadoop Summit 2010 - Developers Track Honu - A Large Scale Streaming Data Collection and Processing Pipeline Jerome Boulon, Netflix
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
Yahoo Developer Network
Gives an overview of a typical Data Lake Reference Architecture and AWS Big Data Services
AWS Big Data Landscape
AWS Big Data Landscape
Crishantha Nanayakkara
Learn about the only solution to instantly provision a full-featured ETL environment running on AWS for less than your Sunday newspaper!
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Precisely
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions. This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data. In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise. https://airflowsummit.org/sessions/2023/keynote-llm/
Building and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache Airflow
Kaxil Naik
https://github.com/intel-analytics/analytics-zoo (Analytics Zoo for Apache Spark and BigDL)
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Jason Dai
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Cloudera, Inc.
This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source. With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing. In this talk, you will learn about: 1. What is Apache Flink stack and how it fits into the Big Data ecosystem? 2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment? 3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark. 4. Who is using Apache Flink? 5. Where to learn more about Apache Flink?
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Slim Baltagi
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions! We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data. Spark, Flink, Presto and many others. This is just a sample of frameworks which are used in real companies and we will talk about some of them. In the previous episode of this Big Data series, we talked about the basic information concerning Big Data. This presentation, however, will be much more technical as we will be covering the most popular platforms you can use to deal with Big Data 2.0 Systems and learn about the key differences between these platforms. Let’s go! #CHEDTEB www.chedteb.eu
Available platforms for Big Data 2.0
Available platforms for Big Data 2.0
Petr Novotný
The next release of Apache Spark will be 2.0, marking a big milestone for the project. In this talk, I’ll cover how the community has grown to reach this point, and some of the major features in 2.0. The largest additions are performance improvements for Datasets, DataFrames and SQL through Project Tungsten, as well as a new Structured Streaming API that provides simpler and more powerful stream processing. I’ll also discuss a bit of what’s in the works for future versions.
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Databricks
Bored and Frustrated with traditional data warehousing methodologies? Find out the latest agile data ware housing methodologies here.
Agile data warehousing
Agile data warehousing
Sneha Challa
Presented at Open Source India 2016, at a workshop titled: Building a Data Lake using Apache Hadoop: A Proof of Concept
Datalake Architecture
Datalake Architecture
TechYugadi IT Solutions & Consulting
Learning Objectives: - Discover dark data that you are currently not analyzing. - Analyze dark data without moving it into your data warehouse. - Visualize the results of your dark data analytics.
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Amazon Web Services
Talk from Dremio's Subsurface conference on March 3, 2022. Discusses applications of Arrow and Voltron Data's new Enterprise Subscription offering
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
Wes McKinney
Learn how to use API Platform and Symfony to create super easily rich web and mobile applications relying on React (JS) for their presentational layer. In just a few minutes, we will create a hypermedia API thanks to API Platform, Symfony and Doctrine. We will do it step by step, and the API will be 100% functional with support for pagination, validation, filters, resources embedding. The API will be automatically documented using Swagger and Hydra and beautiful user interface for developers will be available. HTTP cache, authorization and authentication can then be added in a breath. Then, we will introduce all new client-side tools for API Platform: * A fully featured JavaScript (Single Page App) administration system with a modern user interface (Material Design) ; built on top of Admin On Rest (React and Redux). This admin is builded dynamically thanks to the API discoverability (Hydra). * A raw React, Redux and React Router code generator to bootstrap fully-featured Single Page Applications and native mobile apps thanks to the API documentation exposed by API Platform (client-side and server-side validation, on fields error, Twitter Bootstrap compatibility, a11y support...)
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
Les-Tilleuls.coop
Understanding hadoop ecosystem.
Hadoop Big Data A big picture
Hadoop Big Data A big picture
J S Jodha
Describe the Hadoop features provided in Windows Azure HDInsight
Windows Azure HDInsight Service
Windows Azure HDInsight Service
Neil Mackenzie
We will explore the strengths and limitations of Hadoop for analyzing large data sets and review the growing ecosystem of tools for augmenting, extending, or replacing Hadoop MapReduce. We will introduce the Amazon Elastic MapReduce (EMR) platform as the big data foundation for Hadoop and beyond by providing specific examples of running Machine Learning (Mahout), Graph Analytics (Giraph), and Statistical Analysis (R) on EMR. We will discuss also big data analytics and visualization of results with Amazon Redshift + third party business intelligence tools, as well as typical end-to-end Big Data workflow on AWS. We will conclude with real-world examples from ICAO of Big Data analytics for aviation safety data on AWS. The integrated Safety Trend Analysis and Reporting System (iSTARS) is a web based system linking a collection of safety datasets and related web application to perform online safety and risk analysis. It uses AWS EC2, S3, EMR and related partner tools for continuous data aggregation and filtering.
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
Amazon Web Services
Similaire à Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
(20)
Introduction to AWS Glue
Introduction to AWS Glue
Big Data Analytics from Azure Cloud to Power BI Mobile
Big Data Analytics from Azure Cloud to Power BI Mobile
BDA311 Introduction to AWS Glue
BDA311 Introduction to AWS Glue
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
AWS Big Data Landscape
AWS Big Data Landscape
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Building and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache Airflow
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Available platforms for Big Data 2.0
Available platforms for Big Data 2.0
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Agile data warehousing
Agile data warehousing
Datalake Architecture
Datalake Architecture
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
Hadoop Big Data A big picture
Hadoop Big Data A big picture
Windows Azure HDInsight Service
Windows Azure HDInsight Service
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
Plus de Cloudera, Inc.
Partner Webinar for updates and news January 25th 2022
Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
Cloudera, Inc.
This annual program recognizes organizations who are moving swiftly towards the future and building innovative solutions by making what was impossible yesterday, possible today. The winning organizations' implementations demonstrate outstanding achievements in fulfilling their mission, technical advancement, and overall impact. The 2021 Data Impact Awards recognize organizations' achievements with the Cloudera Data Platform in seven categories: Data Lifecycle Connection Data for Enterprise AI Cloud Innovation Security & Governance Leadership People First Data for Good Industry Transformation
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
Cloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
Cloudera, Inc.
Cloudera Enterprise Data Cloud Event Vienna 1 Oct. 2019
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as - -Powerful data ingestion powered by Apache NiFi -Edge data collection by Apache MiNiFi -IoT-scale streaming data processing with Apache Kafka -Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Cloudera, Inc.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
Cloudera, Inc.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
Cloudera, Inc.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
Cloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
Cloudera, Inc.
Plus de Cloudera, Inc.
(20)
Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
Dernier
The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
apidays
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
The Digital Insurer
We present an architecture of embedding models, vector databases, LLMs, and narrow ML for tracking global news narratives across a variety of countries/languages/news sources. As an example, we explore the real-time application of this architecture for tracking the news narrative surrounding the death of Russian opposition leader Alexei Navalny coming from Russian, French, and English sources.
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Zilliz
The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
How to get Oracle DBA Job as fresher.
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Remote DBA Services
Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
apidays
Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
The CNIC Information System is a comprehensive database managed by the National Database and Registration Authority (NADRA) of Pakistan. It serves as the primary source of identification for Pakistani citizens and residents, containing vital information such as name, date of birth, address, and biometric data.
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
danishmna97
Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
Explore how multimodal embeddings work with Milvus. We will see how you can explore a popular multimodal model - CLIP - on a popular dataset - CIFAR 10. You use CLIP to create the embeddings of the input data, Milvus to store the embeddings of the multimodal data (sometimes termed “multimodal embeddings”), and we will then explore the embeddings.
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
Zilliz
Workshop Build With AI - Google Developers Group Rio Verde
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
Sandro Moreira
Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Angeliki Cooney
Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
apidays
In this talk, we are going to cover the use-case of food image generation at Delivery Hero, its impact and the challenges. In particular, we will present our image scoring solution for filtering out inappropriate images and elaborate on the models we are using.
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Zilliz
Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Orbitshub
The action of the next cyber saga takes place in the mystical lands of the Asia-Pacific region, where the main characters began their digital activities in the middle of 2021 and qualitatively strengthened it in 2022. Corporate espionage, document theft, audio recordings, and data leaks from messaging platforms were all a matter of one day for Dark Pink. Their geographical focus may have started in the Asia-Pacific region, but their ambitions knew no bounds, targeting a European government ministry in a bold move to expand their portfolio. Their victim profile was as diverse as a UN meeting, targeting military organizations, government agencies, and even a religious organization. Because discrimination is not a fashionable agenda. In the world of cybercrime, they serve as a reminder that sometimes the most serious threats come in the most unassuming packages with a pink bow.
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
Overkill Security
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
Nanddeep Nachan
Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
Rustici Software
Dernier
(20)
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
1.
Prototyping Data Intensive
Apps: TrendingTopics.org 09/29/09 1 Pete Skomoroch Research Scientist at LinkedIn Consultant at Data Wrangling @peteskomoroch
2.
3.
4.
TrendingTopics.org
5.
Daily Pageview Timeline
Charts
6.
Detects Rising Trends
with Hadoop
7.
8.
Technology Stack
9.
Application Data Flow
10.
11.
12.
13.
14.
Python Streaming for
Daily Timelines
15.
Streaming: Filter &
Aggregate Logs
16.
17.
Fixing Wiki Redirects
with Hive JOIN
18.
Trend Detection With
Hadoop
19.
Hourly Data: “Java”
vs. “Hangover”
20.
21.
Call Python Trend
Mapper from Hive
22.
Simple Trend Calculation
in Python
23.
Use Trend Scores
to Rank Articles
24.
25.
Hooking It All
Together
26.
27.
28.
29.
30.
31.
32.
33.
LinkedIn A
nalytics Team We’re Hiring
34.
35.
Backup slides
36.
Wikipedia Views ~
Google Searches “ Dwight Howard” on TrendingTopics “ Dwight Howard” on Google Trends
37.
Télécharger maintenant