Seravia in the Cloud

•Télécharger en tant que PPT, PDF•

3 j'aime•550 vues

K

MongoDB Beijing Meetup on May 7th.

Seravia in the Cloud Danny Yang May 7, 2011

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Sample of Data 2.0 Companies

Our data sets

Mongo ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Sphinx ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],sphinxsearch.com

Combining Mongo and Sphinx ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Why cloud? ,[object Object],[object Object],[object Object],[object Object]

Amazon Cloud Advantages ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Amazon Cloud users ,[object Object],[object Object],[object Object]

Amazon Web Services (AWS) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Seravia on AWS WWW Data Crawlware ELB EC2 rails, mongo, mysql, sphinx S3 EC2 parsing, pentaho, ETL S3 EMR hadoop, hive, BI EC2 S3 EC2 rails, mongo, mysql, sphinx

WWW architecture ELB EC2 webserver S3 EC2 webserver EC2 mongo EC2 sphinx EC2 mysql EC2 webserver, rails EC2 mongo EC2 sphinx

Data Architecture EC2 post-processing EC2 Parsing, ETL S3 EC2 Parsing, ETL EMR Hadoop, hive, BI EC2 post-processing 1. Raw data – html, xml, text files 2. Pre-processed – unrelated tsv files 3. Analyzed – related tsv files and reports 4. Post-processed – json documents EC2 post-processing

Crawlware Architecture EC2 Crawler EC2 Crawler EC2 Controller S3 EC2 Crawler EC2 Crawler EC2 Controller

Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Q & A

Contenu connexe

Tendances

Deploying On EC2

Deploying On EC2

Deploying On EC2

Open source data ingestion

Open source data ingestion

Open source data ingestion

Treasure Data, Inc.

213 event processingtalk-deviewkorea.key

213 event processingtalk-deviewkorea.key

213 event processingtalk-deviewkorea.key

Data Science Stack with MongoDB and RStudio

Data Science Stack with MongoDB and RStudio

Data Science Stack with MongoDB and RStudio

Running and managing large scale applications with microservices architectures is hard and often requires operating complex container management infrastructure. Amazon EC2 Container Service (ECS) is a highly scalable, high performance service for running and managing Docker applications. In this webinar, we will walk through a number of patterns used by our customers to run their microservices platforms. We will dive deep into the Amazon ECS Service Scheduler that is optimized for long running applications by monitoring container health, restarting failed containers, and load balancing across containers. Learning Objectives: Understand the basics of Amazon ECS and its scheduler How to run and manage microservices using Amazon ECS Who Should Attend: Developers, system administrators, Docker users, container users

AWS September Webinar Series - Running Microservices with Amazon EC2 Contain...

AWS September Webinar Series - Running Microservices with Amazon EC2 Contain...

AWS September Webinar Series - Running Microservices with Amazon EC2 Contain...

Amazon Web Services

Learn tips for transitioning your data architecture into the cloud. We’ll explore storing data, automating data processing, and delivering data with AWS, with lessons from our experience helping clients deploy their data into Amazon. You’ll discover the standard design patterns we live by, and learn best practices for Amazon services including S3, RDS, Lambda, SNS, and SQS. Plus, get a peek at the future of data delivery as we take a look at AWS API Gateway.

Amazon Web Services: Lessons for Architecting Data in the Cloud

Amazon Web Services: Lessons for Architecting Data in the Cloud

Amazon Web Services: Lessons for Architecting Data in the Cloud

Scaling Analytics with elasticsearch

Scaling Analytics with elasticsearch

Scaling Analytics with elasticsearch

Cascalog at May Bay Area Hadoop User Group

Cascalog at May Bay Area Hadoop User Group

Cascalog at May Bay Area Hadoop User Group

What's new in pandas and the SciPy stack for financial users

What's new in pandas and the SciPy stack for financial users

What's new in pandas and the SciPy stack for financial users

Elasticsearch

Backbone Conf 2014 - Realtime & Firebase

Backbone Conf 2014 - Realtime & Firebase

Backbone Conf 2014 - Realtime & Firebase

Clément Wehrung

Microservices, Continuous Delivery, and Elasticsearch at Capital One

Microservices, Continuous Delivery, and Elasticsearch at Capital One

Microservices, Continuous Delivery, and Elasticsearch at Capital One

Noriaki Tatsumi

Elasticsearch in 15 minutes

Elasticsearch in 15 minutes

Elasticsearch in 15 minutes

Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...

Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...

Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...

Chuan-Yen Chiang

Practical Machine Learning for Smarter Search with Solr and Spark

Practical Machine Learning for Smarter Search with Solr and Spark

Practical Machine Learning for Smarter Search with Solr and Spark

To use Ruby for data processing widely, Apache Arrow support is important. We can do the followings with Apache Arrow: * Super fast large data interchange and processing * Reading/writing data in several famous formats such as CSV and Apache Parquet * Reading/writing partitioned large data on cloud storage such as Amazon S3 This talk describes the followings: * What is Apache Arrow * How to use Apache Arrow with Ruby * How to integrate with Ruby 3.0 features such as MemoryView and Ractor

RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache Arrow

RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache Arrow

RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache Arrow

by Joseph Idziorek, Sr. Product Manager, AWS Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.

Graph and Neptune

Graph and Neptune

Graph and Neptune

Amazon Web Services

Distributed search solutions and comparison

Distributed search solutions and comparison

Distributed search solutions and comparison

Elasticsearch

We are living in the world of abundant data, so called “big data”. The term “big data” is closely associated with unstructured data. They are called “unstructured” or NoSQL data because they do not fit neatly in a traditional row-column relational database. A NoSQL (Not only SQL or Non-relational SQL) database is the type of database that can handle unstructured data. For example, a NoSQL database can store unstructured data such as XML (Extensible Markup Language), JSON (JavaScript Object Notation) or RDF (Resource Description Framework) files. If an enterprise is able to extract unstructured data from NoSQL databases and transfer it to the SAS environment for analysis, this will produce tremendous value, especially from a big data solutions standpoint. This paper will show how unstructured data is stored in the NoSQL databases and ways to transfer it to the SAS environment for analysis. First, the paper will introduce the NoSQL database. For example, NoSQL databases can store unstructured data such as XML, JSON or RDF files. Secondly, the paper will show how the SAS system connects to NoSQL databases using REST (Representational State Transfer) API (Application Programming Interface). For example, SAS programmers can use the PROC HTTP option to extract XML or JSON files through REST API from the NoSQL database. Finally, the paper will show how SAS programmers can convert XML and JSON files to SAS datasets for analysis. For example, SAS programmers can create XMLMap files using XMLV2 LIBNAME engine and convert the extracted XML files to SAS datasets.

SAS integration with NoSQL data

SAS integration with NoSQL data

SAS integration with NoSQL data

Tendances (20)

Deploying On EC2

Deploying On EC2

Deploying On EC2

Open source data ingestion

Open source data ingestion

Open source data ingestion

213 event processingtalk-deviewkorea.key

213 event processingtalk-deviewkorea.key

213 event processingtalk-deviewkorea.key

Data Science Stack with MongoDB and RStudio

Data Science Stack with MongoDB and RStudio

Data Science Stack with MongoDB and RStudio

AWS September Webinar Series - Running Microservices with Amazon EC2 Contain...

AWS September Webinar Series - Running Microservices with Amazon EC2 Contain...

AWS September Webinar Series - Running Microservices with Amazon EC2 Contain...

Amazon Web Services: Lessons for Architecting Data in the Cloud

Amazon Web Services: Lessons for Architecting Data in the Cloud

Amazon Web Services: Lessons for Architecting Data in the Cloud

Scaling Analytics with elasticsearch

Scaling Analytics with elasticsearch

Scaling Analytics with elasticsearch

Cascalog at May Bay Area Hadoop User Group

Cascalog at May Bay Area Hadoop User Group

Cascalog at May Bay Area Hadoop User Group

What's new in pandas and the SciPy stack for financial users

What's new in pandas and the SciPy stack for financial users

What's new in pandas and the SciPy stack for financial users

Elasticsearch

Backbone Conf 2014 - Realtime & Firebase

Backbone Conf 2014 - Realtime & Firebase

Backbone Conf 2014 - Realtime & Firebase

Microservices, Continuous Delivery, and Elasticsearch at Capital One

Microservices, Continuous Delivery, and Elasticsearch at Capital One

Microservices, Continuous Delivery, and Elasticsearch at Capital One

Elasticsearch in 15 minutes

Elasticsearch in 15 minutes

Elasticsearch in 15 minutes

Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...

Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...

Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...

Practical Machine Learning for Smarter Search with Solr and Spark

Practical Machine Learning for Smarter Search with Solr and Spark

Practical Machine Learning for Smarter Search with Solr and Spark

RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache Arrow

RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache Arrow

RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache Arrow

Graph and Neptune

Graph and Neptune

Graph and Neptune

Distributed search solutions and comparison

Distributed search solutions and comparison

Distributed search solutions and comparison

Elasticsearch

SAS integration with NoSQL data

SAS integration with NoSQL data

SAS integration with NoSQL data

Similaire à Seravia in the Cloud

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...

Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...

Amazon Web Services

by Dario Rivera, Solutions Architect, AWS The world is producing an ever-increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.

Big Data Architectural Patterns and Best Practices on AWS

Big Data Architectural Patterns and Best Practices on AWS

Big Data Architectural Patterns and Best Practices on AWS

Amazon Web Services

Amazon Athena Capabilities and Use Cases Overview

Amazon Athena Capabilities and Use Cases Overview

Amazon Athena Capabilities and Use Cases Overview

Amazon Web Services

Database and Analytics on the AWS Cloud - AWS Innovate Toronto

Database and Analytics on the AWS Cloud - AWS Innovate Toronto

Database and Analytics on the AWS Cloud - AWS Innovate Toronto

Amazon Web Services

Big Data, Ingeniería de datos, y Data Lakes en AWS

Big Data, Ingeniería de datos, y Data Lakes en AWS

Big Data, Ingeniería de datos, y Data Lakes en AWS

In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.

Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...

Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...

Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...

Amazon Web Services

Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.

BDA305 Building Data Lakes and Analytics on AWS

BDA305 Building Data Lakes and Analytics on AWS

BDA305 Building Data Lakes and Analytics on AWS

Amazon Web Services

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 session you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using standard 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.

Interactively Querying Large-scale Datasets on Amazon S3

Interactively Querying Large-scale Datasets on Amazon S3

Interactively Querying Large-scale Datasets on Amazon S3

Amazon Web Services

Cloud computing gives you a number of advantages, such as being able to scale your application on demand. As a new business looking to use the cloud, you inevitably ask yourself, "Where do I start?" Join us in this session to understand best practices for scaling your resources from zero to millions of users. We will show you how to best combine different AWS services, make smarter decisions for architecting your application, and best practices for scaling your infrastructure in the cloud. Presenter: Santanu Dutt, Solution Architect, Amazon Internet Services Vinayak Hegde, Vice President – Engineering, Helpshift Sunny Saxena, Product Lead, Sprinklr

Why Scale Matters and How the Cloud is Really Different (at scale)

Why Scale Matters and How the Cloud is Really Different (at scale)

Why Scale Matters and How the Cloud is Really Different (at scale)

Amazon Web Services

Data Analytics Week at the San Francisco Loft Using Data Lakes A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data. Speakers: John Mallory - Principal Business Development Manager Storage (Object), AWS Hemant Borole - Sr. Big Data Consultant, AWS

Using Data Lakes: Data Analytics Week SF

Using Data Lakes: Data Analytics Week SF

Using Data Lakes: Data Analytics Week SF

Amazon Web Services

With Proquest Udini, we have created the worlds largest online article store, and aim to be the center for researchers all over the world. We connect to a 700M solr cluster for search, but have recently also implemented a search component with ElasticSearch. We will discuss how we did this, and how we want to use the 30M index for scientific citation recognition. We will highlight lessons learned in integrating ElasticSearch in our virtualized EC2 environments, and challenges aligning with our continuous deployment processes.

ElasticSearch in Production: lessons learned

ElasticSearch in Production: lessons learned

ElasticSearch in Production: lessons learned

In this presentation, we will demonstrate how to use Amazon Elastic MapReduce as your scalable data warehouse. Amazon EMR supports clusters with thousands of nodes and is used to access petabyte scale data warehouses. Amazon EMR is not only fast, but it is also easy to use for rapid development and adhoc analysis. We will show you how access the large scale data warehouses with emerging tools such as Hue, Hive, low latency SQL applications like Presto, and alternative execution engines like Apache Spark. We will also show you how these tools integrate directly with other AWS big data services such as Amazon S3, Amazon DynamoDB, and Amazon Kinesis.

(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...

(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...

(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...

Amazon Web Services

Using Data Lakes

Using Data Lakes

Using Data Lakes

Amazon Web Services

Day of Cloud: Amazon EC2

Day of Cloud: Amazon EC2

Day of Cloud: Amazon EC2

Using Data Lakes

Using Data Lakes

Using Data Lakes

Amazon Web Services

In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.

Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...

Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...

Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...

Amazon Web Services

In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes, and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

Amazon Web Services

Building analytics applications requires more than just one good service. It requires the ability to capture a vast amount of data, and react to data changes in real time. It requires flexible tools which enable end users to work in the way they can be most productive, and which addresses the needs of both data consumers, as well as data scientists. This analysis won't just be about data exploration and reports, but must be able to support the largest scale, complex machine and deep learning models imaginable. Across it all, strong governance, security, and cataloguing is essential. In this session, come to hear about how to build a full stack analytics application using AWS Services. We'll see how to capture static and dynamic data in real time, and react to data changes. We'll see AWS Services which perform analytics from drag-and-drop, through simple query-on-files, and into exascale data science. At the end, we'll have a data lake architecture that will meet the demands of the most sophisticated analytics customers for many years to come. AWS Speaker: Ian Robinson, Specialist Solution Architect, Big Data and Analytics, EMEA - Amazon Web Services

Full Stack Analytics on AWS - AWS Summit Cape Town 2017

Full Stack Analytics on AWS - AWS Summit Cape Town 2017

Full Stack Analytics on AWS - AWS Summit Cape Town 2017

Amazon Web Services

Builders' Day - Building Data Lakes for Analytics On AWS LC

Builders' Day - Building Data Lakes for Analytics On AWS LC

Builders' Day - Building Data Lakes for Analytics On AWS LC

Amazon Web Services LATAM

Semplificare l'analisi dei dati con architetture "Serverless": architetture e...

Semplificare l'analisi dei dati con architetture "Serverless": architetture e...

Semplificare l'analisi dei dati con architetture "Serverless": architetture e...

Amazon Web Services

Similaire à Seravia in the Cloud (20)

Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...

Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...

Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...

Big Data Architectural Patterns and Best Practices on AWS

Big Data Architectural Patterns and Best Practices on AWS

Big Data Architectural Patterns and Best Practices on AWS

Amazon Athena Capabilities and Use Cases Overview

Amazon Athena Capabilities and Use Cases Overview

Amazon Athena Capabilities and Use Cases Overview

Database and Analytics on the AWS Cloud - AWS Innovate Toronto

Database and Analytics on the AWS Cloud - AWS Innovate Toronto

Database and Analytics on the AWS Cloud - AWS Innovate Toronto

Big Data, Ingeniería de datos, y Data Lakes en AWS

Big Data, Ingeniería de datos, y Data Lakes en AWS

Big Data, Ingeniería de datos, y Data Lakes en AWS

Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...

Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...

Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...

BDA305 Building Data Lakes and Analytics on AWS

BDA305 Building Data Lakes and Analytics on AWS

BDA305 Building Data Lakes and Analytics on AWS

Interactively Querying Large-scale Datasets on Amazon S3

Interactively Querying Large-scale Datasets on Amazon S3

Interactively Querying Large-scale Datasets on Amazon S3

Why Scale Matters and How the Cloud is Really Different (at scale)

Why Scale Matters and How the Cloud is Really Different (at scale)

Why Scale Matters and How the Cloud is Really Different (at scale)

Using Data Lakes: Data Analytics Week SF

Using Data Lakes: Data Analytics Week SF

Using Data Lakes: Data Analytics Week SF

ElasticSearch in Production: lessons learned

ElasticSearch in Production: lessons learned

ElasticSearch in Production: lessons learned

(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...

(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...

(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...

Using Data Lakes

Using Data Lakes

Using Data Lakes

Day of Cloud: Amazon EC2

Day of Cloud: Amazon EC2

Day of Cloud: Amazon EC2

Using Data Lakes

Using Data Lakes

Using Data Lakes

Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...

Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...

Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

Full Stack Analytics on AWS - AWS Summit Cape Town 2017

Full Stack Analytics on AWS - AWS Summit Cape Town 2017

Full Stack Analytics on AWS - AWS Summit Cape Town 2017

Builders' Day - Building Data Lakes for Analytics On AWS LC

Builders' Day - Building Data Lakes for Analytics On AWS LC

Builders' Day - Building Data Lakes for Analytics On AWS LC

Semplificare l'analisi dei dati con architetture "Serverless": architetture e...

Semplificare l'analisi dei dati con architetture "Serverless": architetture e...

Semplificare l'analisi dei dati con architetture "Serverless": architetture e...

Dernier

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

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

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

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

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

DBX First Quarter 2024 Investor Presentation

DBX First Quarter 2024 Investor Presentation

DBX First Quarter 2024 Investor Presentation

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

"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 ...

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

CNIC Information System with Pakdata Cf In Pakistan

CNIC Information System with Pakdata Cf In Pakistan

CNIC Information System with Pakdata Cf In Pakistan

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...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology 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 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

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...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

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 New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

MS Copilot expands with MS Graph connectors

MS Copilot expands with MS Graph connectors

MS Copilot expands with MS Graph connectors

Nanddeep Nachan

Discover the innovative features and strategic vision that keep WSO2 an industry leader. Explore the exciting 2024 roadmap of WSO2 API management, showcasing innovations, unified APIM/APK control plane, natural language API interaction, and cloud native agility. Discover how open source solutions, microservices architecture, and cloud native technologies unlock seamless API management in today's dynamic landscapes. Leave with a clear blueprint to revolutionize your API journey and achieve industry success!

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2's API Vision: Unifying Control, Empowering Developers

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

The Digital Insurer

Architecting Cloud Native Applications

Architecting Cloud Native Applications

Architecting Cloud Native Applications

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 New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Strategies for Landing an Oracle DBA Job as a Fresher

Strategies for Landing an Oracle DBA Job as a Fresher

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Dernier (20)

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

DBX First Quarter 2024 Investor Presentation

DBX First Quarter 2024 Investor Presentation

DBX First Quarter 2024 Investor Presentation

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

"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 ...

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

CNIC Information System with Pakdata Cf In Pakistan

CNIC Information System with Pakdata Cf In Pakistan

CNIC Information System with Pakdata Cf In Pakistan

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

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 New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

MS Copilot expands with MS Graph connectors

MS Copilot expands with MS Graph connectors

MS Copilot expands with MS Graph connectors

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2's API Vision: Unifying Control, Empowering Developers

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

Architecting Cloud Native Applications

Architecting Cloud Native Applications

Architecting Cloud Native Applications

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Strategies for Landing an Oracle DBA Job as a Fresher

Strategies for Landing an Oracle DBA Job as a Fresher

Strategies for Landing an Oracle DBA Job as a Fresher

Seravia in the Cloud

1. Seravia in the Cloud Danny Yang May 7, 2011

2.

3. Sample of Data 2.0 Companies

4. Our data sets

5.

6.

7.

8.

9.

10.

11.

12. Seravia on AWS WWW Data Crawlware ELB EC2 rails, mongo, mysql, sphinx S3 EC2 parsing, pentaho, ETL S3 EMR hadoop, hive, BI EC2 S3 EC2 rails, mongo, mysql, sphinx

13. WWW architecture ELB EC2 webserver S3 EC2 webserver EC2 mongo EC2 sphinx EC2 mysql EC2 webserver, rails EC2 mongo EC2 sphinx

14. Data Architecture EC2 post-processing EC2 Parsing, ETL S3 EC2 Parsing, ETL EMR Hadoop, hive, BI EC2 post-processing 1. Raw data – html, xml, text files 2. Pre-processed – unrelated tsv files 3. Analyzed – related tsv files and reports 4. Post-processed – json documents EC2 post-processing

15. Crawlware Architecture EC2 Crawler EC2 Crawler EC2 Controller S3 EC2 Crawler EC2 Crawler EC2 Controller

16.