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The main topic of slides is building high availability high throughput system for receiveing and saving different kind of information with horizontal scalling possibility using HBase, Flume and Grizzly hosted on Amazon EC2 low cost instances. Talk describes HBase HA cluster setup process with useful hints and EC2 pitfalls, Flume setup process with providing comparasion between standalone and embedded Flume versions and show difference and usecases of both versions. A lot of attention payed to Flume2Hbase streaming features with tweaks and different approaches for speeding up this process.
Jee conf
Jee conf
Valerii Moisieienko
You run your SQL-centric infrastructure for 10 years and slowly starting to note you can’t do this way anymore – everything is getting too expensive but your business requires things which are simply impossible without radical changes. This is exact situation we had 2 years before. So we’d like to show our experience: - Why and how we came into Big Data? - Why we choose Apache and Hadoop? - What to do and what is already done? - What lessons were learned? - Hadoop and relational databases: fight or synergy? - Reactive Big Data manifest.
BIG DATA: From mammoth to elephant
BIG DATA: From mammoth to elephant
Roman Nikitchenko
Moskva Architecture Highload
Moskva Architecture Highload
Ontico
MongoDB can be used in the Nuxeo Platform as a replacement for more traditional SQL databases. Nuxeo's content repository, which is the cornerstone of this open source enterprise content management platform, integrates completely with MongoDB for data storage. This presentation will explain the motivation for using MongoDB and will emphasize the different implementation choices driven by the very nature of a NoSQL datastore like MongoDB. Learn how Nuxeo integrated MongoDB into the platform which resulted in increased performance (including actual benchmarks) and better response to some use cases.
MongoDB Europe 2016 - Using MongoDB to Build a Fast and Scalable Content Repo...
MongoDB Europe 2016 - Using MongoDB to Build a Fast and Scalable Content Repo...
MongoDB
In any enterprise or cloud application, Task scheduling is a key requirement. A highly available and fault-tolerant task scheduling will help us to improve our business goals. A classic task scheduling infrastructure is typically backed by databases. The instances/service that performs the scheduling, loads the task definitions from the database into memory and performs the task scheduling. This kind of infrastructure creates issues like stateful services, inability to scale the services horizontally, being prone to frequent failures, etc., If the state of these kinds of services is not maintained well, it may lead to inconsistent and integrity issues. To mitigate these issues, we will explore a high available and fault-tolerant task scheduling infrastructure using Kafka, Kafka Streams, and State Store.
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
HostedbyConfluent
At LinkedIn, we ingest more than 1 Trillion events per day pertaining to user behavior, application and system health etc. into our pub-sub system (Kafka). Another source of events are the updates that are happening on our SQL and No-SQL databases. For e.g. every time a user changes their linkedIn profile, a ton of downstream applications need to know what happened and need to react to it. We have a system (DataBus) which listens to changes in the database transaction logs and makes them available for down stream processing. We process ~2.1 Trillion of such database change events per week. We use Apache Samza for processing these event-streams in real time. In this presentation we will discuss some of challenges we faced and the various techniques we used to overcome them. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.bigdataspain.org/program/thu/slot-3.html
Essential ingredients for real time stream processing @Scale by Kartik pParam...
Essential ingredients for real time stream processing @Scale by Kartik pParam...
Big Data Spain
It is common for consumer Internet companies to start off with popular third-party tools for analytics needs. Then, when the user base and the company grows, they end up building their own analytics data pipeline and query engine to cope with their data scale, satisfy custom data enrichment and reporting needs and achieve high quality of their data. That’s exactly the path that was taken at Grammarly, the popular online proofreading service. In this session, Grammarly will share how they improved business and marketing analytics, previously done with Mixpanel, by building their own in-house analytics engine and application on top of Apache Spark. Chernetsov wil touch upon several Spark tweaks and gotchas that they experienced along the way: – Outputting data to several storages in a single Spark job – Dealing with Spark memory model, building a custom spillable data-structure for your data traversal – Implementing a custom query language with parser combinators on top of Spark sql parser – Custom query optimizer and analyzer when you want not exactly sql – Flexible-schema storage and query against multi-schema data with schema conflicts – Custom aggregation functions in Spark SQL
Building a Versatile Analytics Pipeline on Top of Apache Spark with Mikhail C...
Building a Versatile Analytics Pipeline on Top of Apache Spark with Mikhail C...
Databricks
Core banking systems are batch oriented: typically with heavy overnight batch cycles before business opens each morning. In this talk I will explain some of the common interface points between core-banking infrastructure and event streaming systems. Then I will focus on how to do stream processing using ksqlDB for core-banking shaped data: showing how to do common operation using various ksqlDB functions. The key features are avro-record keys and multi-key joins (ksqlDB 0.15), schema management and state store planning.
Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...
Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...
HostedbyConfluent
Recommandé
The main topic of slides is building high availability high throughput system for receiveing and saving different kind of information with horizontal scalling possibility using HBase, Flume and Grizzly hosted on Amazon EC2 low cost instances. Talk describes HBase HA cluster setup process with useful hints and EC2 pitfalls, Flume setup process with providing comparasion between standalone and embedded Flume versions and show difference and usecases of both versions. A lot of attention payed to Flume2Hbase streaming features with tweaks and different approaches for speeding up this process.
Jee conf
Jee conf
Valerii Moisieienko
You run your SQL-centric infrastructure for 10 years and slowly starting to note you can’t do this way anymore – everything is getting too expensive but your business requires things which are simply impossible without radical changes. This is exact situation we had 2 years before. So we’d like to show our experience: - Why and how we came into Big Data? - Why we choose Apache and Hadoop? - What to do and what is already done? - What lessons were learned? - Hadoop and relational databases: fight or synergy? - Reactive Big Data manifest.
BIG DATA: From mammoth to elephant
BIG DATA: From mammoth to elephant
Roman Nikitchenko
Moskva Architecture Highload
Moskva Architecture Highload
Ontico
MongoDB can be used in the Nuxeo Platform as a replacement for more traditional SQL databases. Nuxeo's content repository, which is the cornerstone of this open source enterprise content management platform, integrates completely with MongoDB for data storage. This presentation will explain the motivation for using MongoDB and will emphasize the different implementation choices driven by the very nature of a NoSQL datastore like MongoDB. Learn how Nuxeo integrated MongoDB into the platform which resulted in increased performance (including actual benchmarks) and better response to some use cases.
MongoDB Europe 2016 - Using MongoDB to Build a Fast and Scalable Content Repo...
MongoDB Europe 2016 - Using MongoDB to Build a Fast and Scalable Content Repo...
MongoDB
In any enterprise or cloud application, Task scheduling is a key requirement. A highly available and fault-tolerant task scheduling will help us to improve our business goals. A classic task scheduling infrastructure is typically backed by databases. The instances/service that performs the scheduling, loads the task definitions from the database into memory and performs the task scheduling. This kind of infrastructure creates issues like stateful services, inability to scale the services horizontally, being prone to frequent failures, etc., If the state of these kinds of services is not maintained well, it may lead to inconsistent and integrity issues. To mitigate these issues, we will explore a high available and fault-tolerant task scheduling infrastructure using Kafka, Kafka Streams, and State Store.
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
HostedbyConfluent
At LinkedIn, we ingest more than 1 Trillion events per day pertaining to user behavior, application and system health etc. into our pub-sub system (Kafka). Another source of events are the updates that are happening on our SQL and No-SQL databases. For e.g. every time a user changes their linkedIn profile, a ton of downstream applications need to know what happened and need to react to it. We have a system (DataBus) which listens to changes in the database transaction logs and makes them available for down stream processing. We process ~2.1 Trillion of such database change events per week. We use Apache Samza for processing these event-streams in real time. In this presentation we will discuss some of challenges we faced and the various techniques we used to overcome them. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.bigdataspain.org/program/thu/slot-3.html
Essential ingredients for real time stream processing @Scale by Kartik pParam...
Essential ingredients for real time stream processing @Scale by Kartik pParam...
Big Data Spain
It is common for consumer Internet companies to start off with popular third-party tools for analytics needs. Then, when the user base and the company grows, they end up building their own analytics data pipeline and query engine to cope with their data scale, satisfy custom data enrichment and reporting needs and achieve high quality of their data. That’s exactly the path that was taken at Grammarly, the popular online proofreading service. In this session, Grammarly will share how they improved business and marketing analytics, previously done with Mixpanel, by building their own in-house analytics engine and application on top of Apache Spark. Chernetsov wil touch upon several Spark tweaks and gotchas that they experienced along the way: – Outputting data to several storages in a single Spark job – Dealing with Spark memory model, building a custom spillable data-structure for your data traversal – Implementing a custom query language with parser combinators on top of Spark sql parser – Custom query optimizer and analyzer when you want not exactly sql – Flexible-schema storage and query against multi-schema data with schema conflicts – Custom aggregation functions in Spark SQL
Building a Versatile Analytics Pipeline on Top of Apache Spark with Mikhail C...
Building a Versatile Analytics Pipeline on Top of Apache Spark with Mikhail C...
Databricks
Core banking systems are batch oriented: typically with heavy overnight batch cycles before business opens each morning. In this talk I will explain some of the common interface points between core-banking infrastructure and event streaming systems. Then I will focus on how to do stream processing using ksqlDB for core-banking shaped data: showing how to do common operation using various ksqlDB functions. The key features are avro-record keys and multi-key joins (ksqlDB 0.15), schema management and state store planning.
Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...
Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...
HostedbyConfluent
January 2011 HUG: Kafka Presentation
January 2011 HUG: Kafka Presentation
Yahoo Developer Network
Introduction of BigQuery at DeNA West
DeNA West & BigQuery
DeNA West & BigQuery
Yoshi Izawa
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...
MongoDB
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
MongoDB .local Toronto 2019: Finding the Right Atlas Cluster Size: Does this ...
MongoDB .local Toronto 2019: Finding the Right Atlas Cluster Size: Does this ...
MongoDB
Intuit uses HBase for storing comprehensive, de-duplicated, canonical merchant information that powers the backend for a Merchant Lookup Service at Intuit. This service enables users and products to look up business details by various parameters like merchant name, location, and business type. It aims at providing a more complete, canonical business profile by bringing together data from across the various information providers including Intuit’s small business customer base. In this talk, we will describe the Hadoop deduping pipeline, our HBase data model, the challenges faced along the way and our plans to have upcoming projects leverage this data in HBase.
HBaseCon 2012 | HBase powered Merchant Lookup Service at Intuit
HBaseCon 2012 | HBase powered Merchant Lookup Service at Intuit
Cloudera, Inc.
Breakout Session
RedisConf18 - Redis Memory Optimization
RedisConf18 - Redis Memory Optimization
Redis Labs
As a software adventurer, Charles “Indy” Sarrazin, has brought numerous customers through the MongoDB world, using his extensive knowledge to make sure they always got the most out of their databases. Let us embark on a journey inside the Document Model, where we will identify, analyze and fix anti-patterns. I will also provide you with tools to ease migration strategies towards the Temple of Lost Performance! Be warned, though! You might want to learn about design patterns before, in order to survive this exhilarating trial!
MongoDB World 2019: Raiders of the Anti-patterns: A Journey Towards Fixing Sc...
MongoDB World 2019: Raiders of the Anti-patterns: A Journey Towards Fixing Sc...
MongoDB
2015-01-14 道玄坂LT祭り(ミドル・インフラ) in Japan 『Presto + MySQLで分散SQL』 by Sadayuki Furuhashi
Presto+MySQLで分散SQL
Presto+MySQLで分散SQL
Sadayuki Furuhashi
As the usage of Apache Spark continues to ramp up within the industry, a major challenge has been scaling our development. Too often we find that developers are re-implementing a similar set of cross-cutting concerns, sprinkled with some variance of use-case specific business logic as a concrete Spark App.
Composable Data Processing with Apache Spark
Composable Data Processing with Apache Spark
Databricks
Figuring out analytics in Retail Centers using Streamsets and Spark.
Streamsets and spark in Retail
Streamsets and spark in Retail
Hari Shreedharan
Read these webinar slides to learn how selecting the right shard key can future proof your application. The shard key that you select can impact the performance, capability, and functionality of your database.
Webinar: Choosing the Right Shard Key for High Performance and Scale
Webinar: Choosing the Right Shard Key for High Performance and Scale
MongoDB
#bq_sushi tokyo #1 の発表資料 BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
nagachika t
Benjamin Hopp (Solutions Architect) @ Imply: Druid is an emerging standard in the data infrastructure world, designed for high-performance slice-and-dice analytics (“OLAP”-style) on large data sets. This talk is for you if you’re interested in learning more about pushing Druid’s analytical performance to the limit. Perhaps you’re already running Druid and are looking to speed up your deployment, or perhaps you aren’t familiar with Druid and are interested in learning the basics. Some of the tips in this talk are Druid-specific, but many of them will apply to any operational analytics technology stack. The most important contributor to a fast analytical setup is getting the data model right. The talk will center around various choices you can make to prepare your data to get best possible query performance. We’ll look at some general best practices to model your data before ingestion such as OLAP dimensional modeling (called “roll-up” in Druid), data partitioning, and tips for choosing column types and indexes. We’ll also look at how more can be less: often, storing copies of your data partitioned, sorted, or aggregated in different ways can speed up queries by reducing the amount of computation needed. We’ll also look at Druid-specific optimizations that take advantage of approximations; where you can trade accuracy for performance and reduced storage. You’ll get introduced to Druid’s features for approximate counting, set operations, ranking, quantiles, and more. And we will finish with the latest and greatest Druid news, including details about the latest roadmap and releases.
A Day in the Life of a Druid Implementor and Druid's Roadmap
A Day in the Life of a Druid Implementor and Druid's Roadmap
Itai Yaffe
Companies doing any kind of advertising typically have an attribution process that joins users’ conversions with the impressions that they were served or that they clicked on. The standard workflow is typically a batch job that runs every few hours or once a day. However, as technology gets more sophisticated, advertisers are looking for more real-time reporting and results. This talk presents an example of a foundational architecture for near real-time attribution and advanced analytics against real-time impression and conversion data using Structured Streaming and Databricks Delta.
Real-Time Attribution with Structured Streaming and Databricks Delta with Car...
Real-Time Attribution with Structured Streaming and Databricks Delta with Car...
Databricks
Spark Summit 2016 talk by Liyin Tang and Jingwei Lu
Airstream: Spark Streaming At Airbnb
Airstream: Spark Streaming At Airbnb
Jen Aman
My new industry acronym: PSTL the Parallelized Streaming Transformation Loader (Pron. PiSToL) is an architecture for highly scalable and reliable, data ingestion pipelines While there is guidance on using; Apache Kafka™ for Streaming (or non-Streaming), and Apache Spark™ for Transformations, and Loading data (e.g., COPY) into an HP-Vertica™ columnar Data Warehouse, there is very little prescriptive guidance on how to truly parallelize a unified data pipeline - until now.
HPBigData2015 PSTL kafka spark vertica
HPBigData2015 PSTL kafka spark vertica
Jack Gudenkauf
At Pinterest, hundreds of services and third-party tools that are implemented in various programming languages generate billions of events every day. To achieve scalable and reliable low latency logging, there are several challenges: (1) uploading logs that are generated in various formats from tens of thousands of hosts to Kafka in a timely manner; (2) running Kafka reliably on Amazon Web Services where the virtual instances are less reliable than on-premises hardware; (3) moving tens of terabytes data per day from Kafka to cloud storage reliably and efficiently, and guaranteeing exact one time persistence per message. In this talk, we will present Pinterest’s logging pipeline, and share our experience addressing these challenges. We will dive deep into the three components we developed: data uploading from service hosts to Kafka, data transportation from Kafka to S3, and data sanitization. We will also share our experience in operating Kafka at scale in the cloud.
Scalable and Reliable Logging at Pinterest
Scalable and Reliable Logging at Pinterest
Krishna Gade
Lambda Architecture has been a common way to build data pipelines for a long time, despite difficulties in maintaining two complex systems. An alternative, Kappa Architecture, was proposed in 2014, but many companies are still reluctant to switch to Kappa. And there is a reason for that: even though Kappa generally provides a simpler design and similar or lower latency, there are a lot of practical challenges in areas like exactly-once delivery, late-arriving data, historical backfill and reprocessing. In this talk, I want to show how you can solve those challenges by embracing Apache Kafka as a foundation of your data pipeline and leveraging modern stream-processing frameworks like Apache Kafka Streams and Apache Flink.
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, Shopify
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, Shopify
HostedbyConfluent
Presented by Eoin Brazil, Proactive Technical Services Engineer, MongoDB Experience level: Advanced MongoDB offers a flexible, scalable, and easy way to store your large data set. Python provides many useful data science tools (e.g. NumPy, SciPy, Scikit-learn, etc.). This talk will discuss the concerns for creating operational data analytic pipelines, introduce Monary as alternative for loading data into NumPy, and give examples of accessing data with Monary, as well as how to build scalable data analysis pipelines using these open source tools.
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
MongoDB
At DataVisor, we fight online fraud, abuse, and money laundering using unsupervised machine learning approach that clusters millions of users. In order to support the computationally intensive workload, DataVisor uses Spark as the mainstay of its computation infrastructure. The scalability and portability of our Spark infrastructure is critical to our company when we expand our business. In this talk, we will present our story of how we manage our Spark infrastructure at scale. At peak time, we have 2000+ Spark workers online, and we group these workers into ~50 clusters of various size. The benefits of this, on one hand, is data isolation, which is critical to DataVisor as we are processing multi-customer data. On the other hand, this is for cost and performance consideration, as we want to provide just enough resources to each Spark application. When under-provision, Spark application will fail due to out-of-memory or out-of-disk. However we want to avoid unnecessary over-provision as it dramatically increases our cloud cost. Next, we will present our DataVisor SparkGenerator (DSG), which is designed to automatically manage our Spark infrastructure. The responsibility of DSG includes (a) launching and shutting down Spark cluster, to maximize concurrency and minimize cost, (b) assigning Spark applications to the proper clusters intelligently, according to the Spark application profile, and (c) managing the dependency among Spark applications, to make our pipeline run smoothly and efficiently, and (d) running all of the Spark worker on Spot instances, reducing the cloud computation cost versus on-demand by over 80%.
Managing Thousands of Spark Workers in Cloud Environment with Yuhao Zheng and...
Managing Thousands of Spark Workers in Cloud Environment with Yuhao Zheng and...
Databricks
Small Overview of Skype Database Tools
Small Overview of Skype Database Tools
elliando dias
Database Tools by Skype
Database Tools by Skype
elliando dias
Contenu connexe
Tendances
January 2011 HUG: Kafka Presentation
January 2011 HUG: Kafka Presentation
Yahoo Developer Network
Introduction of BigQuery at DeNA West
DeNA West & BigQuery
DeNA West & BigQuery
Yoshi Izawa
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...
MongoDB
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
MongoDB .local Toronto 2019: Finding the Right Atlas Cluster Size: Does this ...
MongoDB .local Toronto 2019: Finding the Right Atlas Cluster Size: Does this ...
MongoDB
Intuit uses HBase for storing comprehensive, de-duplicated, canonical merchant information that powers the backend for a Merchant Lookup Service at Intuit. This service enables users and products to look up business details by various parameters like merchant name, location, and business type. It aims at providing a more complete, canonical business profile by bringing together data from across the various information providers including Intuit’s small business customer base. In this talk, we will describe the Hadoop deduping pipeline, our HBase data model, the challenges faced along the way and our plans to have upcoming projects leverage this data in HBase.
HBaseCon 2012 | HBase powered Merchant Lookup Service at Intuit
HBaseCon 2012 | HBase powered Merchant Lookup Service at Intuit
Cloudera, Inc.
Breakout Session
RedisConf18 - Redis Memory Optimization
RedisConf18 - Redis Memory Optimization
Redis Labs
As a software adventurer, Charles “Indy” Sarrazin, has brought numerous customers through the MongoDB world, using his extensive knowledge to make sure they always got the most out of their databases. Let us embark on a journey inside the Document Model, where we will identify, analyze and fix anti-patterns. I will also provide you with tools to ease migration strategies towards the Temple of Lost Performance! Be warned, though! You might want to learn about design patterns before, in order to survive this exhilarating trial!
MongoDB World 2019: Raiders of the Anti-patterns: A Journey Towards Fixing Sc...
MongoDB World 2019: Raiders of the Anti-patterns: A Journey Towards Fixing Sc...
MongoDB
2015-01-14 道玄坂LT祭り(ミドル・インフラ) in Japan 『Presto + MySQLで分散SQL』 by Sadayuki Furuhashi
Presto+MySQLで分散SQL
Presto+MySQLで分散SQL
Sadayuki Furuhashi
As the usage of Apache Spark continues to ramp up within the industry, a major challenge has been scaling our development. Too often we find that developers are re-implementing a similar set of cross-cutting concerns, sprinkled with some variance of use-case specific business logic as a concrete Spark App.
Composable Data Processing with Apache Spark
Composable Data Processing with Apache Spark
Databricks
Figuring out analytics in Retail Centers using Streamsets and Spark.
Streamsets and spark in Retail
Streamsets and spark in Retail
Hari Shreedharan
Read these webinar slides to learn how selecting the right shard key can future proof your application. The shard key that you select can impact the performance, capability, and functionality of your database.
Webinar: Choosing the Right Shard Key for High Performance and Scale
Webinar: Choosing the Right Shard Key for High Performance and Scale
MongoDB
#bq_sushi tokyo #1 の発表資料 BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
nagachika t
Benjamin Hopp (Solutions Architect) @ Imply: Druid is an emerging standard in the data infrastructure world, designed for high-performance slice-and-dice analytics (“OLAP”-style) on large data sets. This talk is for you if you’re interested in learning more about pushing Druid’s analytical performance to the limit. Perhaps you’re already running Druid and are looking to speed up your deployment, or perhaps you aren’t familiar with Druid and are interested in learning the basics. Some of the tips in this talk are Druid-specific, but many of them will apply to any operational analytics technology stack. The most important contributor to a fast analytical setup is getting the data model right. The talk will center around various choices you can make to prepare your data to get best possible query performance. We’ll look at some general best practices to model your data before ingestion such as OLAP dimensional modeling (called “roll-up” in Druid), data partitioning, and tips for choosing column types and indexes. We’ll also look at how more can be less: often, storing copies of your data partitioned, sorted, or aggregated in different ways can speed up queries by reducing the amount of computation needed. We’ll also look at Druid-specific optimizations that take advantage of approximations; where you can trade accuracy for performance and reduced storage. You’ll get introduced to Druid’s features for approximate counting, set operations, ranking, quantiles, and more. And we will finish with the latest and greatest Druid news, including details about the latest roadmap and releases.
A Day in the Life of a Druid Implementor and Druid's Roadmap
A Day in the Life of a Druid Implementor and Druid's Roadmap
Itai Yaffe
Companies doing any kind of advertising typically have an attribution process that joins users’ conversions with the impressions that they were served or that they clicked on. The standard workflow is typically a batch job that runs every few hours or once a day. However, as technology gets more sophisticated, advertisers are looking for more real-time reporting and results. This talk presents an example of a foundational architecture for near real-time attribution and advanced analytics against real-time impression and conversion data using Structured Streaming and Databricks Delta.
Real-Time Attribution with Structured Streaming and Databricks Delta with Car...
Real-Time Attribution with Structured Streaming and Databricks Delta with Car...
Databricks
Spark Summit 2016 talk by Liyin Tang and Jingwei Lu
Airstream: Spark Streaming At Airbnb
Airstream: Spark Streaming At Airbnb
Jen Aman
My new industry acronym: PSTL the Parallelized Streaming Transformation Loader (Pron. PiSToL) is an architecture for highly scalable and reliable, data ingestion pipelines While there is guidance on using; Apache Kafka™ for Streaming (or non-Streaming), and Apache Spark™ for Transformations, and Loading data (e.g., COPY) into an HP-Vertica™ columnar Data Warehouse, there is very little prescriptive guidance on how to truly parallelize a unified data pipeline - until now.
HPBigData2015 PSTL kafka spark vertica
HPBigData2015 PSTL kafka spark vertica
Jack Gudenkauf
At Pinterest, hundreds of services and third-party tools that are implemented in various programming languages generate billions of events every day. To achieve scalable and reliable low latency logging, there are several challenges: (1) uploading logs that are generated in various formats from tens of thousands of hosts to Kafka in a timely manner; (2) running Kafka reliably on Amazon Web Services where the virtual instances are less reliable than on-premises hardware; (3) moving tens of terabytes data per day from Kafka to cloud storage reliably and efficiently, and guaranteeing exact one time persistence per message. In this talk, we will present Pinterest’s logging pipeline, and share our experience addressing these challenges. We will dive deep into the three components we developed: data uploading from service hosts to Kafka, data transportation from Kafka to S3, and data sanitization. We will also share our experience in operating Kafka at scale in the cloud.
Scalable and Reliable Logging at Pinterest
Scalable and Reliable Logging at Pinterest
Krishna Gade
Lambda Architecture has been a common way to build data pipelines for a long time, despite difficulties in maintaining two complex systems. An alternative, Kappa Architecture, was proposed in 2014, but many companies are still reluctant to switch to Kappa. And there is a reason for that: even though Kappa generally provides a simpler design and similar or lower latency, there are a lot of practical challenges in areas like exactly-once delivery, late-arriving data, historical backfill and reprocessing. In this talk, I want to show how you can solve those challenges by embracing Apache Kafka as a foundation of your data pipeline and leveraging modern stream-processing frameworks like Apache Kafka Streams and Apache Flink.
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, Shopify
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, Shopify
HostedbyConfluent
Presented by Eoin Brazil, Proactive Technical Services Engineer, MongoDB Experience level: Advanced MongoDB offers a flexible, scalable, and easy way to store your large data set. Python provides many useful data science tools (e.g. NumPy, SciPy, Scikit-learn, etc.). This talk will discuss the concerns for creating operational data analytic pipelines, introduce Monary as alternative for loading data into NumPy, and give examples of accessing data with Monary, as well as how to build scalable data analysis pipelines using these open source tools.
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
MongoDB
At DataVisor, we fight online fraud, abuse, and money laundering using unsupervised machine learning approach that clusters millions of users. In order to support the computationally intensive workload, DataVisor uses Spark as the mainstay of its computation infrastructure. The scalability and portability of our Spark infrastructure is critical to our company when we expand our business. In this talk, we will present our story of how we manage our Spark infrastructure at scale. At peak time, we have 2000+ Spark workers online, and we group these workers into ~50 clusters of various size. The benefits of this, on one hand, is data isolation, which is critical to DataVisor as we are processing multi-customer data. On the other hand, this is for cost and performance consideration, as we want to provide just enough resources to each Spark application. When under-provision, Spark application will fail due to out-of-memory or out-of-disk. However we want to avoid unnecessary over-provision as it dramatically increases our cloud cost. Next, we will present our DataVisor SparkGenerator (DSG), which is designed to automatically manage our Spark infrastructure. The responsibility of DSG includes (a) launching and shutting down Spark cluster, to maximize concurrency and minimize cost, (b) assigning Spark applications to the proper clusters intelligently, according to the Spark application profile, and (c) managing the dependency among Spark applications, to make our pipeline run smoothly and efficiently, and (d) running all of the Spark worker on Spot instances, reducing the cloud computation cost versus on-demand by over 80%.
Managing Thousands of Spark Workers in Cloud Environment with Yuhao Zheng and...
Managing Thousands of Spark Workers in Cloud Environment with Yuhao Zheng and...
Databricks
Tendances
(20)
January 2011 HUG: Kafka Presentation
January 2011 HUG: Kafka Presentation
DeNA West & BigQuery
DeNA West & BigQuery
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...
MongoDB .local Toronto 2019: Finding the Right Atlas Cluster Size: Does this ...
MongoDB .local Toronto 2019: Finding the Right Atlas Cluster Size: Does this ...
HBaseCon 2012 | HBase powered Merchant Lookup Service at Intuit
HBaseCon 2012 | HBase powered Merchant Lookup Service at Intuit
RedisConf18 - Redis Memory Optimization
RedisConf18 - Redis Memory Optimization
MongoDB World 2019: Raiders of the Anti-patterns: A Journey Towards Fixing Sc...
MongoDB World 2019: Raiders of the Anti-patterns: A Journey Towards Fixing Sc...
Presto+MySQLで分散SQL
Presto+MySQLで分散SQL
Composable Data Processing with Apache Spark
Composable Data Processing with Apache Spark
Streamsets and spark in Retail
Streamsets and spark in Retail
Webinar: Choosing the Right Shard Key for High Performance and Scale
Webinar: Choosing the Right Shard Key for High Performance and Scale
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
A Day in the Life of a Druid Implementor and Druid's Roadmap
A Day in the Life of a Druid Implementor and Druid's Roadmap
Real-Time Attribution with Structured Streaming and Databricks Delta with Car...
Real-Time Attribution with Structured Streaming and Databricks Delta with Car...
Airstream: Spark Streaming At Airbnb
Airstream: Spark Streaming At Airbnb
HPBigData2015 PSTL kafka spark vertica
HPBigData2015 PSTL kafka spark vertica
Scalable and Reliable Logging at Pinterest
Scalable and Reliable Logging at Pinterest
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, Shopify
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, Shopify
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
Managing Thousands of Spark Workers in Cloud Environment with Yuhao Zheng and...
Managing Thousands of Spark Workers in Cloud Environment with Yuhao Zheng and...
Similaire à Asko Oja Moskva Architecture Highload
Small Overview of Skype Database Tools
Small Overview of Skype Database Tools
elliando dias
Database Tools by Skype
Database Tools by Skype
elliando dias
Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.
Introduction to GCP Data Flow Presentation
Introduction to GCP Data Flow Presentation
Knoldus Inc.
In this session, we will learn about how Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.
Introduction to GCP DataFlow Presentation
Introduction to GCP DataFlow Presentation
Knoldus Inc.
DATA BASE SERVER ACCELERATIOB
DB Turbo v6.0 2023.pdf
DB Turbo v6.0 2023.pdf
DalportoBaldo
Handling Data in Mega Scale Systems by Vineet Gupta, GM Software Engineer.
Handling Data in Mega Scale Systems
Handling Data in Mega Scale Systems
Directi Group
Apresentação para a Agência Nacional de Aviação Civil sobre Otimzações de Projetos de Big Data, Dw e AI
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Luan Moreno Medeiros Maciel
Hw09 Production Deep Dive With High Availability
Hw09 Production Deep Dive With High Availability
Cloudera, Inc.
Um pouco sobre a plataforma de dados e suas opções no Windows Azure. Apresentação feita durante o G
GWAB 2015 - Data Plaraform
GWAB 2015 - Data Plaraform
Marcelo Paiva
Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis. Thousands of transaction requests are handled and processed every day by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog analysis comes to play a crucial role in such situations where Hadoop can be suggested as an efficient solution for processing the Netflow data collected from switches as well as website access-logs during fixed intervals.
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
cscpconf
plProxy, pgBouncer, pgBalancer
plProxy, pgBouncer, pgBalancer
elliando dias
Hi,
Datastage parallell jobs vs datastage server jobs
Datastage parallell jobs vs datastage server jobs
shanker_uma
This talk was given at the Cloud Native Aarhus meetup at May 30rd 2017. Focus is on how we use Cloud Native projects at Lunar Way.
Lunar Way and the Cloud Native "stack"
Lunar Way and the Cloud Native "stack"
Kasper Nissen
The traditional lambda architecture has been a popular solution for joining offline batch operations with real time operations. This setup incurs a lot of developer and operational overhead since it involves maintaining code that produces the same result in two, potentially different distributed systems. In order to alleviate these problems, we need a unified framework for processing and building data pipelines across batch and stream data sources. Based on our experiences running and developing Apache Samza at LinkedIn, we have enhanced the framework to support: a) Pluggable data sources and sinks; b) A deployment model supporting different execution environments such as Yarn or VMs; c) A unified processing API for developers to work seamlessly with batch and stream data. In this talk, we will cover how these design choices in Apache Samza help tackle the overhead of lambda architecture. We will use some real production use-cases to elaborate how LinkedIn leverages Apache Samza to build unified data processing pipelines. Speaker Navina Ramesh, Sr. Software Engineer, LinkedIn
Unified Batch & Stream Processing with Apache Samza
Unified Batch & Stream Processing with Apache Samza
DataWorks Summit
See what's new in #Serverless and #Data at GCP. Our guest, Guillaume Blaquiere - Stack Overflow contributor & #GCP #Developer Expert from France, covered the best #GoogleCloudNext announcements, practically demoed how to benefit from #BigQuery Remote Functions and answered many questions. The meetup recording with TOC for easy navigation is at https://youtu.be/AuZZTwHIcdY P.S. For more interactive lectures like this, go to http://youtube.serverlesstoronto.org/ or sign up for our upcoming live events at https://www.meetup.com/Serverless-Toronto/events/
Google Cloud Next '22 Recap: Serverless & Data edition
Google Cloud Next '22 Recap: Serverless & Data edition
Daniel Zivkovic
We have seen tremendous growth in near real-time ("nearline") processing at LinkedIn in recent years. LinkedIn now uses Apache Samza to process well over a Trillion messages every day across thousands of applications. Apache Samza serves as the foundation for several application platforms at LinkedIn, spanning a wide variety of use cases like security, notifications, machine learning, monitoring, search, and more. In this talk we will explore various features of Apache Samza that provide the flexibility and scalability to we need to power stream processing at massive scale.
Scalable Stream Processing with Apache Samza
Scalable Stream Processing with Apache Samza
Prateek Maheshwari
Co-presented with Will Perry (@willpe). Real-world experiences using CouchDB inside Microsoft, and also how to get started with CouchDB on Microsoft Azure.
Experiences using CouchDB inside Microsoft's Azure team
Experiences using CouchDB inside Microsoft's Azure team
Brian Benz
Equnix Business Solutions (Equnix) is an IT Solution provider in Indonesia, providing comprehensive solution services especially on the infrastructure side for corporate business needs based on research and Open Source. Equnix has 3 (three) main services known as the Trilogy of Services: Support (Maintenance/Managed), World class level of Software Development, and Expert Consulting and Assessment for High Performance Transactions System. Equnix is customer oriented, not product or principal. Equal opportunity based on merit is our credo in managing HR development.
EQUNIX - PPT 11DB-Postgres™.pdf
EQUNIX - PPT 11DB-Postgres™.pdf
Equnix Business Solutions
Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis. Thousands of transaction requests are handled and processed everyday by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog analysis comes to play a crucial role in such situations where Hadoop can be suggested as an efficient solution for processing the Netflow data collected from switches as well as website access-logs during fixed intervals.
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
csandit
Madrid June 8th 2017
La creación de una capa operacional con MongoDB
La creación de una capa operacional con MongoDB
MongoDB
Similaire à Asko Oja Moskva Architecture Highload
(20)
Small Overview of Skype Database Tools
Small Overview of Skype Database Tools
Database Tools by Skype
Database Tools by Skype
Introduction to GCP Data Flow Presentation
Introduction to GCP Data Flow Presentation
Introduction to GCP DataFlow Presentation
Introduction to GCP DataFlow Presentation
DB Turbo v6.0 2023.pdf
DB Turbo v6.0 2023.pdf
Handling Data in Mega Scale Systems
Handling Data in Mega Scale Systems
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Hw09 Production Deep Dive With High Availability
Hw09 Production Deep Dive With High Availability
GWAB 2015 - Data Plaraform
GWAB 2015 - Data Plaraform
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
plProxy, pgBouncer, pgBalancer
plProxy, pgBouncer, pgBalancer
Datastage parallell jobs vs datastage server jobs
Datastage parallell jobs vs datastage server jobs
Lunar Way and the Cloud Native "stack"
Lunar Way and the Cloud Native "stack"
Unified Batch & Stream Processing with Apache Samza
Unified Batch & Stream Processing with Apache Samza
Google Cloud Next '22 Recap: Serverless & Data edition
Google Cloud Next '22 Recap: Serverless & Data edition
Scalable Stream Processing with Apache Samza
Scalable Stream Processing with Apache Samza
Experiences using CouchDB inside Microsoft's Azure team
Experiences using CouchDB inside Microsoft's Azure team
EQUNIX - PPT 11DB-Postgres™.pdf
EQUNIX - PPT 11DB-Postgres™.pdf
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
La creación de una capa operacional con MongoDB
La creación de una capa operacional con MongoDB
Plus de Ontico
Или как посчитать себестоимость проекта?
Риски, которые необходимо учесть при разработке сложного проекта (Олег Бунин)
Риски, которые необходимо учесть при разработке сложного проекта (Олег Бунин)
Ontico
Встреча докладчиков, Программного комитета и активистов конференции разработчиков высоконагруженных систем HighLoad++. Обсудили результаты 2014 года и наметили планы на 2015-й.
Встреча докладчиков HL++ 2015
Встреча докладчиков HL++ 2015
Ontico
Рассказ о новых возможностях конференции разработчиков высоконагруженных систем HighLoad++: экспертной зоне, домашних заданиях, новом подходе к спонсорству и так далее!
Вебинар о конференции HighLoad++
Вебинар о конференции HighLoad++
Ontico
Информация для докладчиков конференций РИТ++, HighLoad++ и Whale Rider.
Call for papers (2014) ru
Call for papers (2014) ru
Ontico
Учебный день конференции HighLoad++ 2013
Учебный день конференции HighLoad++ 2013
Ontico
Как разработать социальную сеть, Олег Бунин
Как разработать социальную сеть, Олег Бунин
Ontico
Конференции Онтико (2011)
Конференции Онтико (2011)
Ontico
Встреча Программного комитета и активистов HighLoad++ 2010
Программный комитет HighLoad++, 6 октября
Программный комитет HighLoad++, 6 октября
Ontico
Конференции 2010 / описание
Конференции 2010 / описание
Ontico
Отчет о деятельности компании Онтико за 2009 год
Онтико, 2009
Онтико, 2009
Ontico
Конференции 2010
Конференции 2010
Ontico
Economy of project development
Economy of project development
Ontico
Ok2009 Пленарка
Ok2009 Пленарка
Ontico
Highload sites, master-class, OK-2009
Highload sites, master-class, OK-2009
Ontico
HighLoad Sites, Oleg Bunin
HighLoad Sites, Oleg Bunin
Ontico
I Safety 1c Bitrix
I Safety 1c Bitrix
Ontico
I Safety 1c Bitrix
I Safety 1c Bitrix
Ontico
Gmr Highload Presentation Revised
Gmr Highload Presentation Revised
Ontico
Wonderful World Of Mysql Storage Engines Hl2008 Rus
Wonderful World Of Mysql Storage Engines Hl2008 Rus
Ontico
Scaling Web Sites By Sharding And Replication Hl2008 Rus
Scaling Web Sites By Sharding And Replication Hl2008 Rus
Ontico
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Риски, которые необходимо учесть при разработке сложного проекта (Олег Бунин)
Риски, которые необходимо учесть при разработке сложного проекта (Олег Бунин)
Встреча докладчиков HL++ 2015
Встреча докладчиков HL++ 2015
Вебинар о конференции HighLoad++
Вебинар о конференции HighLoad++
Call for papers (2014) ru
Call for papers (2014) ru
Учебный день конференции HighLoad++ 2013
Учебный день конференции HighLoad++ 2013
Как разработать социальную сеть, Олег Бунин
Как разработать социальную сеть, Олег Бунин
Конференции Онтико (2011)
Конференции Онтико (2011)
Программный комитет HighLoad++, 6 октября
Программный комитет HighLoad++, 6 октября
Конференции 2010 / описание
Конференции 2010 / описание
Онтико, 2009
Онтико, 2009
Конференции 2010
Конференции 2010
Economy of project development
Economy of project development
Ok2009 Пленарка
Ok2009 Пленарка
Highload sites, master-class, OK-2009
Highload sites, master-class, OK-2009
HighLoad Sites, Oleg Bunin
HighLoad Sites, Oleg Bunin
I Safety 1c Bitrix
I Safety 1c Bitrix
I Safety 1c Bitrix
I Safety 1c Bitrix
Gmr Highload Presentation Revised
Gmr Highload Presentation Revised
Wonderful World Of Mysql Storage Engines Hl2008 Rus
Wonderful World Of Mysql Storage Engines Hl2008 Rus
Scaling Web Sites By Sharding And Replication Hl2008 Rus
Scaling Web Sites By Sharding And Replication Hl2008 Rus
Dernier
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
Scalable LLM APIs for AI and Generative AI Application Development Ettikan Karuppiah, Director/Technologist - NVIDIA Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 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 Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
apidays
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
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
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving. A report by Poten & Partners as part of the Hydrogen Asia 2024 Summit in Singapore. Copyright Poten & Partners 2024.
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
Nanddeep Nachan
Architecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
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
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
As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
The Digital Insurer
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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
wesley chun
MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Miguel Araújo
Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe 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 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
apidays
Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
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In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
Overkill Security
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
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Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
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Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
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Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
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