Data processing and analysis is where big data is most often consumed, driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing, and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing, and interactive analytics with AWS services, such as, Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
Created by: Jason Morris, Solutions Architect
Data collection and storage is a primary challenge for any big data architecture. This session will focus on the different types of data that customers are handling to drive high-scale workloads on AWS. Our goal is to help you choose the best approach for your workload. We will dive into optimization techniques that improve performance and reduce the cost of data ingestion and AWS services including Amazon S3, DynamoDB, and Kinesis.
Created by: Mark Korver, Senior Solutions Architect
Getting Started with Amazon Redshift - AWS July 2016 Webinar SeriesAmazon Web Services
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for as low as $1000/TB/year. This webinar will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
Learning Objectives:
• Get an introduction to Amazon Redshift's massively parallel processing, columnar, scale-out architecture
• Learn how to configure your data warehouse cluster, optimize schema, and load data efficiently
• Get an overview of all the latest features including interleaved sorting and user-defined functions
During a Big Data Warehousing Meetup in NYC, Elliott Cordo, Chief Architect at Caserta Concepts discussed emerging trends in real time data processing. The presentation included processing frameworks such as Spark and Storm, as well datastore technologies ranging from NoSQL to Hadoop. He also discussed exciting new AWS services such as Lambda, Kenesis, and Kenesis Firehose.
Amazon DynamoDB is a fully managed NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. This talk explores DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.
Join us for a for a Amazon Kinesis tutorial webinar. In this session we will provide a reference architecture and instructions for building a system that performs real-time sliding-windows analysis over streaming clickstream data. We will use Amazon Kinesis for managed ingestion of streaming data at scale with the ability to replay past data, and run sliding-window computation using Apache Storm. We’ll demonstrate in the webinar on how to build the system and deploy on AWS and walkthrough all the steps from ingestion, processing, and storing to visualizing of the data in real-time.
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs. We’ll also cover the recently announced Redshift Spectrum, which allows you to query unstructured data directly from Amazon S3.
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
Data collection and storage is a primary challenge for any big data architecture. This session will focus on the different types of data that customers are handling to drive high-scale workloads on AWS. Our goal is to help you choose the best approach for your workload. We will dive into optimization techniques that improve performance and reduce the cost of data ingestion and AWS services including Amazon S3, DynamoDB, and Kinesis.
Created by: Mark Korver, Senior Solutions Architect
Getting Started with Amazon Redshift - AWS July 2016 Webinar SeriesAmazon Web Services
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for as low as $1000/TB/year. This webinar will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
Learning Objectives:
• Get an introduction to Amazon Redshift's massively parallel processing, columnar, scale-out architecture
• Learn how to configure your data warehouse cluster, optimize schema, and load data efficiently
• Get an overview of all the latest features including interleaved sorting and user-defined functions
During a Big Data Warehousing Meetup in NYC, Elliott Cordo, Chief Architect at Caserta Concepts discussed emerging trends in real time data processing. The presentation included processing frameworks such as Spark and Storm, as well datastore technologies ranging from NoSQL to Hadoop. He also discussed exciting new AWS services such as Lambda, Kenesis, and Kenesis Firehose.
Amazon DynamoDB is a fully managed NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. This talk explores DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.
Join us for a for a Amazon Kinesis tutorial webinar. In this session we will provide a reference architecture and instructions for building a system that performs real-time sliding-windows analysis over streaming clickstream data. We will use Amazon Kinesis for managed ingestion of streaming data at scale with the ability to replay past data, and run sliding-window computation using Apache Storm. We’ll demonstrate in the webinar on how to build the system and deploy on AWS and walkthrough all the steps from ingestion, processing, and storing to visualizing of the data in real-time.
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs. We’ll also cover the recently announced Redshift Spectrum, which allows you to query unstructured data directly from Amazon S3.
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing and scale-out architecture to ensure compute resources grow with your dataset size, and columnar, direct-attached storage to dramatically reduce I/O time. Learn how top online retailer RetailMeNot moved their largest Vertica cluster on Amazon EC2 to Amazon Redshift. See how they gain insights from clickstream, location, merchant, marketing, and operational data across desktop and mobile properties.
Build Your Web Analytics with node.js, Amazon DynamoDB and Amazon EMR (BDT203...Amazon Web Services
Want to learn how to build your own Google Analytics? Learn how to build a scalable architecture using node.js, Amazon DynamoDB, and Amazon EMR. This architecture is used by ScribbleLive to track billions of engagement minutes per month. In this session, we go over the code in node.js, how to store the data in Amazon DynamoDB, and how to roll-up the data using Hadoop and Hive. Attend this session to learn how to move data quickly at any scale as well as how to use genomic analysis tools and pipelines for next generation sequencers using Globus on AWS.
(BDT320) New! Streaming Data Flows with Amazon Kinesis FirehoseAmazon Web Services
Amazon Kinesis Firehose is a fully-managed, elastic service to deliver real-time data streams to Amazon S3, Amazon Redshift, and other destinations. In this session, we start with overviews of Amazon Kinesis Firehose and Amazon Kinesis Analytics. We then discuss how Amazon Kinesis Firehose makes it even easier to get started with streaming data, without writing a stream processing application or provisioning a single resource. You learn about the key features of Amazon Kinesis Firehose, including its companion agent that makes emitting data from data producers even easier. We walk through capture and delivery with an end-to-end demo, and discuss key metrics that will help developers and architects understand their streaming data flow. Finally, we look at some patterns for data consumption as the data streams into S3. We show two examples: using AWS Lambda, and how you can use Apache Spark running within Amazon EMR to query data directly in Amazon S3 through EMRFS.
(ARC202) Real-World Real-Time Analytics | AWS re:Invent 2014Amazon Web Services
Working with big volumes of data is a complicated task, but it's even harder if you have to do everything in real time and try to figure it all out yourself. This session will use practical examples to discuss architectural best practices and lessons learned when solving real-time social media analytics, sentiment analysis, and data visualization decision-making problems with AWS. Learn how you can leverage AWS services like Amazon RDS, AWS CloudFormation, Auto Scaling, Amazon S3, Amazon Glacier, and Amazon Elastic MapReduce to perform highly performant, reliable, real-time big data analytics while saving time, effort, and money. Gain insight from two years of real-time analytics successes and failures so you don't have to go down this path on your own.
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Speakers:
Ian Meyers, AWS Solutions Architect
Toby Moore, Chief Technology Officer, Space Ape
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. You¹ll also hear from Dan Wagner, CEO at Civis Analytics, as he discusses why the Civis data science platform was designed on top of Amazon Redshift and the AWS platform in order to help smart organizations bridge their data silos, build 360 degree view of their customer relationships, and identify opportunities for driving their companies forward by leveraging enormous datasets, the power of analytics, and economies of scale on the AWS platform.
Explore DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others.
AWS December 2015 Webinar Series - Design Patterns using Amazon DynamoDBAmazon Web Services
If you’re familiar with relational databases, designing your app to use a NoSQL database like DynamoDB may be new to you. In this webinar, we’ll walk you through common data design patterns for a variety of applications to help you learn how to design a schema, then store and retrieve the data with DynamoDB. We will discuss the benefits of using DynamoDB to develop mobile, web, IoT, and gaming apps.
Learning Objectives:
Learn schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others
Who Should Attend:
Architects, Developers, and SysOps interested in learning how to design NoSQL schemas to support mobile, web, IoT, AdTech, and gaming apps.
Familiarity with DynamoDB is helpful
(GAM301) Real-Time Game Analytics with Amazon Kinesis, Amazon Redshift, and A...Amazon Web Services
Success in free-to-play gaming requires knowing what your players love most. The faster you can respond to players' behavior, the better your chances of success. Learn how mobile game company GREE, with over 150 million users worldwide, built a real-time analytics pipeline for their games using Amazon Kinesis, Amazon Redshift, and Amazon DynamoDB. They walk through their analytics architecture, the choices they made, the challenges they overcame, and the benefits they gained. Also hear how GREE migrated to the new system while keeping their games running and collecting metrics.
NoSQL is an important part of many big data strategies. Attend this session to learn how Amazon DynamoDB helps you create fast ingest and response data sets. We demonstrate how to use DynamoDB for batch-based query processing and ETL operations (using a SQL-like language) through integration with Amazon EMR and Hive. Then, we show you how to reduce costs and achieve scalability by connecting data to Amazon ElasticCache for handling massive read volumes. We’ll also discuss how to add indexes on DynamoDB data for free-text searching by integrating with Elasticsearch using AWS Lambda and DynamoDB streams. Finally, you’ll find out how you can take your high-velocity, high-volume data (such as IoT data) in DynamoDB and connect it to a data warehouse (Amazon Redshift) to enable BI analysis.
(DAT308) Yahoo! Analyzes Billions of Events a Day on Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast, fully managed petabyte-scale data warehouse service that costs less than $1,000 a TB a year, under a tenth the price of most traditional data warehousing solutions. Learn how Yahoo! uses both to build a billion event a day infrastructure that is fast, easy, and cost-effective. Dive into how Yahoo performs advanced user retention and cohort analysis to make near–real time product and marketing decisions.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
"Amgen discovers, develops, manufactures, and delivers innovative human therapeutics, helping millions of people in the fight against serious illnesses. In 2014, Amgen implemented a solution to offload ETL data across a diverse data set (U.S. pharmaceutical prescriptions and claims) using Amazon EMR. The solution has transformed the way Amgen delivers insights and reports to its sales force. To support Amgen’s entry into a much larger market, the ETL process had to scale to eight times its existing data volume. We used Amazon EC2, Amazon S3, Amazon EMR, and Amazon Redshift to generate weekly sales reporting metrics.
This session discusses highlights in Amgen's journey to leverage big data technologies and lay the foundation for future growth: benefits of ETL offloading in Amazon EMR as an entry point for big data technologies; benefits and challenges of using Amazon EMR vs. expanding on-premises ETL and reporting technologies; and how to architect an ETL offload solution using Amazon S3, Amazon EMR, and Impala."
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing and scale-out architecture to ensure compute resources grow with your dataset size, and columnar, direct-attached storage to dramatically reduce I/O time. Learn how top online retailer RetailMeNot moved their largest Vertica cluster on Amazon EC2 to Amazon Redshift. See how they gain insights from clickstream, location, merchant, marketing, and operational data across desktop and mobile properties.
Build Your Web Analytics with node.js, Amazon DynamoDB and Amazon EMR (BDT203...Amazon Web Services
Want to learn how to build your own Google Analytics? Learn how to build a scalable architecture using node.js, Amazon DynamoDB, and Amazon EMR. This architecture is used by ScribbleLive to track billions of engagement minutes per month. In this session, we go over the code in node.js, how to store the data in Amazon DynamoDB, and how to roll-up the data using Hadoop and Hive. Attend this session to learn how to move data quickly at any scale as well as how to use genomic analysis tools and pipelines for next generation sequencers using Globus on AWS.
(BDT320) New! Streaming Data Flows with Amazon Kinesis FirehoseAmazon Web Services
Amazon Kinesis Firehose is a fully-managed, elastic service to deliver real-time data streams to Amazon S3, Amazon Redshift, and other destinations. In this session, we start with overviews of Amazon Kinesis Firehose and Amazon Kinesis Analytics. We then discuss how Amazon Kinesis Firehose makes it even easier to get started with streaming data, without writing a stream processing application or provisioning a single resource. You learn about the key features of Amazon Kinesis Firehose, including its companion agent that makes emitting data from data producers even easier. We walk through capture and delivery with an end-to-end demo, and discuss key metrics that will help developers and architects understand their streaming data flow. Finally, we look at some patterns for data consumption as the data streams into S3. We show two examples: using AWS Lambda, and how you can use Apache Spark running within Amazon EMR to query data directly in Amazon S3 through EMRFS.
(ARC202) Real-World Real-Time Analytics | AWS re:Invent 2014Amazon Web Services
Working with big volumes of data is a complicated task, but it's even harder if you have to do everything in real time and try to figure it all out yourself. This session will use practical examples to discuss architectural best practices and lessons learned when solving real-time social media analytics, sentiment analysis, and data visualization decision-making problems with AWS. Learn how you can leverage AWS services like Amazon RDS, AWS CloudFormation, Auto Scaling, Amazon S3, Amazon Glacier, and Amazon Elastic MapReduce to perform highly performant, reliable, real-time big data analytics while saving time, effort, and money. Gain insight from two years of real-time analytics successes and failures so you don't have to go down this path on your own.
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Speakers:
Ian Meyers, AWS Solutions Architect
Toby Moore, Chief Technology Officer, Space Ape
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. You¹ll also hear from Dan Wagner, CEO at Civis Analytics, as he discusses why the Civis data science platform was designed on top of Amazon Redshift and the AWS platform in order to help smart organizations bridge their data silos, build 360 degree view of their customer relationships, and identify opportunities for driving their companies forward by leveraging enormous datasets, the power of analytics, and economies of scale on the AWS platform.
Explore DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others.
AWS December 2015 Webinar Series - Design Patterns using Amazon DynamoDBAmazon Web Services
If you’re familiar with relational databases, designing your app to use a NoSQL database like DynamoDB may be new to you. In this webinar, we’ll walk you through common data design patterns for a variety of applications to help you learn how to design a schema, then store and retrieve the data with DynamoDB. We will discuss the benefits of using DynamoDB to develop mobile, web, IoT, and gaming apps.
Learning Objectives:
Learn schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others
Who Should Attend:
Architects, Developers, and SysOps interested in learning how to design NoSQL schemas to support mobile, web, IoT, AdTech, and gaming apps.
Familiarity with DynamoDB is helpful
(GAM301) Real-Time Game Analytics with Amazon Kinesis, Amazon Redshift, and A...Amazon Web Services
Success in free-to-play gaming requires knowing what your players love most. The faster you can respond to players' behavior, the better your chances of success. Learn how mobile game company GREE, with over 150 million users worldwide, built a real-time analytics pipeline for their games using Amazon Kinesis, Amazon Redshift, and Amazon DynamoDB. They walk through their analytics architecture, the choices they made, the challenges they overcame, and the benefits they gained. Also hear how GREE migrated to the new system while keeping their games running and collecting metrics.
NoSQL is an important part of many big data strategies. Attend this session to learn how Amazon DynamoDB helps you create fast ingest and response data sets. We demonstrate how to use DynamoDB for batch-based query processing and ETL operations (using a SQL-like language) through integration with Amazon EMR and Hive. Then, we show you how to reduce costs and achieve scalability by connecting data to Amazon ElasticCache for handling massive read volumes. We’ll also discuss how to add indexes on DynamoDB data for free-text searching by integrating with Elasticsearch using AWS Lambda and DynamoDB streams. Finally, you’ll find out how you can take your high-velocity, high-volume data (such as IoT data) in DynamoDB and connect it to a data warehouse (Amazon Redshift) to enable BI analysis.
(DAT308) Yahoo! Analyzes Billions of Events a Day on Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast, fully managed petabyte-scale data warehouse service that costs less than $1,000 a TB a year, under a tenth the price of most traditional data warehousing solutions. Learn how Yahoo! uses both to build a billion event a day infrastructure that is fast, easy, and cost-effective. Dive into how Yahoo performs advanced user retention and cohort analysis to make near–real time product and marketing decisions.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
"Amgen discovers, develops, manufactures, and delivers innovative human therapeutics, helping millions of people in the fight against serious illnesses. In 2014, Amgen implemented a solution to offload ETL data across a diverse data set (U.S. pharmaceutical prescriptions and claims) using Amazon EMR. The solution has transformed the way Amgen delivers insights and reports to its sales force. To support Amgen’s entry into a much larger market, the ETL process had to scale to eight times its existing data volume. We used Amazon EC2, Amazon S3, Amazon EMR, and Amazon Redshift to generate weekly sales reporting metrics.
This session discusses highlights in Amgen's journey to leverage big data technologies and lay the foundation for future growth: benefits of ETL offloading in Amazon EMR as an entry point for big data technologies; benefits and challenges of using Amazon EMR vs. expanding on-premises ETL and reporting technologies; and how to architect an ETL offload solution using Amazon S3, Amazon EMR, and Impala."
With new technology, new frameworks, new models, and new best practices coming at us every day, how can anyone become an expert in everything? This presentation talks about the danger of shiny new web methodologies and how to grow your expertise in a manageable way. Avoid yak shaving and solve real problems!
Data & Analytics - Session 2 - Introducing Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast and powerful, fully managed, petabyte-scale data warehouse service in the cloud. This presentation will give an introduction to the service and its pricing before diving into how it delivers fast query performance on data sets ranging from hundreds of gigabytes to a petabyte or more.
Steffen Krause, Technical Evangelist, AWS
Padraic Mulligan, Architect and Lead Developer and Mike McCarthy, CTO, Skillspage
Amazon Elastic MapReduce (Amazon EMR) is a web service that allows you to easily and securely provision and manage your Hadoop clusters. In this talk, we will introduce you to Amazon EMR design patterns, such as using various data stores like Amazon S3, how to take advantage of both transient and active clusters, and how to work with other Amazon EMR architectural patterns. We will dive deep on how to dynamically scale your cluster and address the ways you can fine-tune your cluster. We will discuss bootstrapping Hadoop applications from our partner ecosystem that you can use natively with Amazon EMR. Lastly, we will share best practices on how to keep your Amazon EMR cluster cost-effective.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
In this session, you get an overview of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service. We'll cover how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also discuss new features, architecture best practices, and share how customers are using Amazon Redshift for their Big Data workloads.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
AWS Summit London 2014 | Uses and Best Practices for Amazon Redshift (200)Amazon Web Services
Interested in finding out more about the AWS data warehouse service, Amazon Redshift? Then join us for this introductory level technical session where you can learn more about the way in which AWS customers are using Redshift and the benefits that they have delivered to their organisations as a result as well as tricks and tips for getting the most from Redshift.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
AWS Webcast - Managing Big Data in the AWS Cloud_20140924Amazon Web Services
This presentation deck will cover specific services such as Amazon S3, Kinesis, Redshift, Elastic MapReduce, and DynamoDB, including their features and performance characteristics. It will also cover architectural designs for the optimal use of these services based on dimensions of your data source (structured or unstructured data, volume, item size and transfer rates) and application considerations - for latency, cost and durability. It will also share customer success stories and resources to help you get started.
In this session, you get an overview of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service. We'll cover how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also discuss new features, architecture best practices, and share how customers are using Amazon Redshift for their Big Data workloads.
AWS June Webinar Series - Getting Started: Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast, fully-managed petabyte-scale data warehouse service, for less than $1,000 per TB per year. In this presentation, you'll get an overview of Amazon Redshift, including how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. Learn how, with just a few clicks in the AWS Management Console, you can set up with a fully functional data warehouse, ready to accept data without learning any new languages and easily plugging in with the existing business intelligence tools and applications you use today. This webinar is ideal for anyone looking to gain deeper insight into their data, without the usual challenges of time, cost and effort. In this webinar, you will learn: • Understand what Amazon Redshift is and how it works • Create a data warehouse interactively through the AWS Management Console • Load some data into your new Amazon Redshift data warehouse from S3 Who Should Attend • IT professionals, developers, line-of-business managers
Antoine Genereux takes us on a detailed overview of the Database solutions available on the AWS Cloud, addressing the needs and requirements of customers at all levels. He also discusses Business Intelligence and Analytics solutions.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
講師: Xiaoyong Han, Solution Architect, AWS
Data collection and storage is a primary challenge for any big data architecture. In this webinar, gain a thorough understanding of AWS solutions for data collection and storage, and learn architectural best practices for applying those solutions to your projects. This session will also include a discussion of popular use cases and reference architectures. In this webinar, you will learn:
• Overview of the different types of data that customers are handling to drive high-scale workloads on AWS, and how to choose the best approach for your workload • Optimization techniques that improve performance and reduce the cost of data ingestion • Leveraging Amazon S3, Amazon DynamoDB, and Amazon Kinesis for storage and data collection
Selecting the Right AWS Database Solution - AWS 2017 Online Tech TalksAmazon Web Services
• Get an overview of managed database services available on AWS
• Learn how to combine them for high-performance cost effective architectures
• Learn how to choose between the AWS database services based on your use case
On AWS you can choose from a variety of managed database services that save effort, save time, and unlock new capabilities and economies. In this session, we make it easy to understand how they differ, what they have in common, and how to choose one or more. We'll explain the fundamentals of Amazon RDS, a managed relational database service in the cloud; Amazon DynamoDB, a fully managed NoSQL database service; Amazon ElastiCache, a fast, in-memory caching service in the cloud; and Amazon Redshift, a fully managed, petabyte-scale data-warehouse solution that can be economical. We will cover how each service might help support your application and how to get started.
Getting Started with Big Data and HPC in the Cloud - August 2015Amazon Web Services
How can you use Big Data to grow your business and discover new opportunities? When organizations effectively capture, analyze, visualize and apply big data insights to their business goals, they differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line. With Amazon Web Services, businesses and researchers can easily fulfill their high performance computing (HPC) requirements with the added benefit of ad-hoc provisioning, pay-as-you-go pricing and faster time-to-results. Join this session to understand how to run HPC applications in AWS cloud, and about different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Getting Started with Managed Database Services on AWS - September 2016 Webina...Amazon Web Services
On AWS you can choose from a variety of managed database services that save effort, save time, and unlock new capabilities and economies. In this session, we make it easy to understand how they differ, what they have in common, and how to choose one or more. We'll explain the fundamentals of Amazon RDS, a managed relational database service in the cloud; Amazon DynamoDB, a fully managed NoSQL database service; Amazon ElastiCache, a fast, in-memory caching service in the cloud; and Amazon Redshift, a fully managed, petabyte-scale data-warehouse solution that can be surprisingly economical. We will cover how each service might help support your application, how much each service costs, and how to get started.
Learning Objectives:
• Overview of managed database services available on AWS
• How to combine them for high-performance cost effective architectures
• Learn how to choose between the AWS database services based on the use case
Who Should Attend:
• IT Managers, DBAs, Enterprise and Solution Architects, IT Managers, DBAs, Enterprise and Solution Architects, Devops Engineers and Developers
A quick overview of Redshift and common use-cases. Followed by tools and links to performance tuning. How Redshift fits in the AWS data services. A list of key new features since last meetup in September 2016, including Redshift Spectrum that allows one to run SQL directly on your data sitting on Amazon S3. It also includes Redshift echosystem with data integration, bi, consultancy and data modelling partners.
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools.
This webinar will provide an overview of Redshift with an emphasis on the many changes we recently introduced. In particular, we will address the newly released DW2 instance types and what you can do with them.
This content is designed for database developers and architects interested in Amazon Redshift.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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 notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
2. Agenda Overview
10:00 AM Registration
10:30 AM Introduction to Big Data @ AWS
12:00 PM Lunch + Registration for Technical Sessions
12:30 PM Data Collection and Storage
1:45PM Real-time Event Processing
3:00PM Analytics (incl Machine Learning)
4:30 PM Open Q&A Roundtable
3. Collect Process Analyze
Store
Data Collection
and Storage
Data
Processing
Event
Processing
Data
Analysis
Primitive Patterns
EMR Redshift
Machine
Learning
5. Why Amazon EMR?
Easy to Use
Launch a cluster in minutes
Low Cost
Pay an hourly rate
Elastic
Easily add or remove capacity
Reliable
Spend less time monitoring
Secure
Manage firewalls
Flexible
Control the cluster
7. Try different configurations to find your optimal architecture
CPU
c3 family
cc1.4xlarge
cc2.8xlarge
Memory
m2 family
r3 family
Disk/IO
d2 family
i2 family
General
m1 family
m3 family
Choose your instance types
Batch Machine Spark and Large
process learning interactive HDFS
8. Easy to add/remove compute capacity to your cluster
Match compute
demands with
cluster sizing
Resizable clusters
9. Spot Instances
for task nodes
Up to 90%
off Amazon EC2
on-demand
pricing
On-demand for
core nodes
Standard
Amazon EC2
pricing for
on-demand
capacity
Easy to use Spot Instances
Meet SLA at predictable cost Exceed SLA at lower cost
10. Amazon S3 as your persistent data store
• Separate compute and storage
• Resize and shut down Amazon
EMR clusters with no data loss
• Point multiple Amazon EMR
clusters at same data in Amazon
S3
EMR
EMR
Amazon
S3
11. EMRFS makes it easier to leverage S3
• Better performance and error handling options
• Transparent to applications – Use “s3://”
• Consistent view
For consistent list and read-after-write for new puts
• Support for Amazon S3 server-side and client-side
encryption
• Faster listing using EMRFS metadata
12. Amazon S3 EMRFS metadata
in Amazon DynamoDB
• List and read-after-write consistency
• Faster list operations
Number
of objects
Without
Consistent
Views
With Consistent
Views
1,000,000 147.72 29.70
100,000 12.70 3.69
Fast listing of S3 objects using
EMRFS metadata
*Tested using a single node cluster with a m3.xlarge instance.
14. Optimize to leverage HDFS
• Iterative workloads
If you’re processing the same dataset more than once
• Disk I/O intensive workloads
Persist data on Amazon S3 and use S3DistCp to
copy to HDFS for processing
15. Pattern #1: Batch processing
GBs of logs pushed
to Amazon S3 hourly
Daily Amazon EMR
cluster using Hive to
process data
Input and output
stored in Amazon S3
Load subset into
Redshift DW
16. Pattern #2: Online data-store
Data pushed to
Amazon S3
Daily Amazon EMR cluster
Extract, Transform, and Load
(ETL) data into database
24/7 Amazon EMR cluster
running HBase holds last 2
years’ worth of data
Front-end service uses
HBase cluster to power
dashboard with high
concurrency
17. Pattern #3: Interactive query
TBs of logs sent
daily
Logs stored in S3
Transient EMR
clusters
Hive Metastore
18. Example: Log Processing using Amazon EMR
• Aggregating small files using s3distcp
• Defining Hive tables with data on Amazon S3
• Interactive querying using Hue
Amazon S3
Log Bucket
Amazon
EMR
Processed and
structured log data
19. Months of user history Common misspellings
Data Analyzed Using EMR:
Westen
Wistin
Westan
Whestin
Automatic spelling corrections
22. Clickstream Analysis for Amazon.com
• Redshift runs web log analysis for Amazon.com
100 node Redshift Cluster
Over one petabyte workload
Largest table: 400TB
2TB of data per day
• Understand customer behavior
Who is browsing but not buying
Which products / features are winners
What sequence led to higher customer conversion
23. Redshift Performance Realized
• Scan 15 months of data: 14 minutes
2.25 trillion rows
• Load one day worth of data: 10 minutes
5 billion rows
• Backfill one month of data: 9.75 hours
150 billion rows
• Pig Amazon Redshift: 2 days to 1 hr
10B row join with 700M rows
• Oracle Amazon Redshift: 90 hours to 8 hrs
Reduced number of SQLs by a factor of 3
24. Amazon Redshift Architecture
• Leader Node
SQL endpoint
Stores metadata
Coordinates query execution
• Compute Nodes
Local, columnar storage
Execute queries in parallel
Load, backup, restore via
Amazon S3; load from
Amazon DynamoDB or SSH
• Two hardware platforms
Optimized for data processing
DW1: HDD; scale from 2TB to 2PB
DW2: SSD; scale from 160GB to 325TB
10 GigE
(HPC)
Ingestion
Backup
Restore
JDBC/ODBC
25. Amazon Redshift Node Types
• Optimized for I/O intensive workloads
• High disk density
• On demand at $0.85/hour
• As low as $1,000/TB/Year
• Scale from 2TB to 1.6PB
DW1.XL: 16 GB RAM, 2 Cores
3 Spindles, 2 TB compressed storage
DW1.8XL: 128 GB RAM, 16 Cores, 24 Spindles
16 TB compressed, 2 GB/sec scan rate
• High performance at smaller storage
size
• High compute and memory density
• On demand at $0.25/hour
• As low as $5,500/TB/Year
• Scale from 160GB to 256TB
DW2.L: 16 GB RAM, 2 Cores,
160 GB compressed SSD storage
DW2.8XL: 256 GB RAM, 32 Cores,
2.56 TB of compressed SSD storage
26. Amazon Redshift dramatically reduces I/O
Column storage
Data compression
Zone maps
Direct-attached storage
• With row storage you do
unnecessary I/O
• To get total amount, you have
to read everything
ID Age State Amount
123 20 CA 500
345 25 WA 250
678 40 FL 125
957 37 WA 375
27. Amazon Redshift dramatically reduces I/O
Column storage
Data compression
Zone maps
Direct-attached storage
With column storage, you
only read the data you need
ID Age State Amount
123 20 CA 500
345 25 WA 250
678 40 FL 125
957 37 WA 375
28. analyze compression listing;
Table | Column | Encoding
---------+----------------+----------
listing | listid | delta
listing | sellerid | delta32k
listing | eventid | delta32k
listing | dateid | bytedict
listing | numtickets | bytedict
listing | priceperticket | delta32k
listing | totalprice | mostly32
listing | listtime | raw
Amazon Redshift dramatically reduces I/O
Column storage
Data compression
Zone maps
Direct-attached storage
• COPY compresses
automatically
• You can analyze and override
• More performance, less cost
29. Amazon Redshift dramatically reduces I/O
Column storage
Data compression
Zone maps
Direct-attached storage
• Track the minimum and
maximum value for each block
• Skip over blocks that don’t
contain relevant data
10 | 13 | 14 | 26 |…
… | 100 | 245 | 324
375 | 393 | 417…
… 512 | 549 | 623
637 | 712 | 809 …
… | 834 | 921 | 959
10
324
375
623
637
959
30. Amazon Redshift dramatically reduces I/O
Column storage
Data compression
Zone maps
Direct-attached storage
• Use local storage for
performance
• Maximize scan rates
• Automatic replication
and continuous
backup
• HDD & SSD platforms
32. Amazon Redshift parallelizes and
distributes everything
Query
Load
Backup/Restore
Resize
• Load in parallel from Amazon S3
or DynamoDB or any SSH
connection
• Data automatically distributed
and sorted according to DDL
• Scales linearly with the number of
nodes in the cluster
33. Amazon Redshift parallelizes and
distributes everything
Query
Load
Backup/Restore
Resize
• Backups to Amazon S3 are automatic,
continuous and incremental
• Configurable system snapshot retention
period. Take user snapshots on-
demand
• Cross region backups for disaster
recovery
• Streaming restores enable you to
resume querying faster
34. Amazon Redshift parallelizes and
distributes everything
Query
Load
Backup/Restore
Resize
• Resize while remaining online
• Provision a new cluster in the
background
• Copy data in parallel from node to
node
• Only charged for source cluster
35. Amazon Redshift parallelizes and
distributes everything
Query
Load
Backup/Restore
Resize
• Automatic SQL endpoint
switchover via DNS
• Decommission the source
cluster
• Simple operation via Console or
API
37. Table Distribution Styles
Distribution Key All
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
All data on
every node
Same key to
same location
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
Even
Round robin
distribution
38. Sorting Data
• In the slices (on disk), the data is sorted by a sort key
If no sort key exists Redshift uses the data insertion order
• Choose a sort key that is frequently used in your queries
As a query predicate (date, identifier, …)
As a join parameter (it can also be the hash key)
• The sort key allows Redshift to avoid reading entire
blocks based on predicates
For example, a table containing a timestamp sort key where only recent
data is accessed, will skip blocks containing “old” data
39. Interleaved Multi Column Sort
• Compound Sort Keys
Optimized for applications that filter data by one leading column
• Interleaved Sort Keys (new)
Optimized for filtering data by up to eight columns
No storage overhead unlike an index
Lower maintenance penalty compared to indexes
40. Compound Sort Keys Illustrated
• Records in Redshift are
stored in blocks.
• For this illustration, let’s
assume that four records
fill a block
• Records with a given
cust_id are all in one block
• However, records with a
given prod_id are spread
across four blocks
1
1
1
1
2
3
4
1
4
4
4
2
3
4
4
1
3
3
3
2
3
4
3
1
2
2
2
2
3
4
2
1
1 [1,1] [1,2] [1,3] [1,4]
2 [2,1] [2,2] [2,3] [2,4]
3 [3,1] [3,2] [3,3] [3,4]
4 [4,1] [4,2] [4,3] [4,4]
1 2 3 4
prod_id
cust_id
cust_id prod_id other columns blocks
41. 1 [1,1] [1,2] [1,3] [1,4]
2 [2,1] [2,2] [2,3] [2,4]
3 [3,1] [3,2] [3,3] [3,4]
4 [4,1] [4,2] [4,3] [4,4]
1 2 3 4
prod_id
cust_id
Interleaved Sort Keys Illustrated
• Records with a given
cust_id are spread
across two blocks
• Records with a given
prod_id are also
spread across two
blocks
• Data is sorted in equal
measures for both
keys
1
1
2
2
2
1
2
3
3
4
4
4
3
4
3
1
3
4
4
2
1
2
3
3
1
2
2
4
3
4
1
1
cust_id prod_id other columns blocks
43. Custom ODBC and JDBC Drivers
• Up to 35% higher performance than open source
drivers
• Supported by Informatica, Microstrategy, Pentaho,
Qlik, SAS, Tableau, Tibco, and others
• Will continue to support PostgreSQL open source
drivers
• Download drivers from console
44. User Defined Functions
• We’re enabling User Defined Functions (UDFs)
so you can add your own
Scalar and Aggregate Functions supported
• You’ll be able to write UDFs using Python 2.7
Syntax is largely identical to PostgreSQL UDF Syntax
System and network calls within UDFs are prohibited
• Comes with Pandas, NumPy, and SciPy pre-
installed
You’ll also be able import your own libraries for even
more flexibility
46. Operational Reporting with Redshift
Amazon S3
Log Bucket
Amazon
EMR
Processed and
structured log
data
Amazon
Redshift
Operational
Reports
47. Amazon Web Services’ global
customer and partner conference
Learn more and register:
reinvent.awsevents.com
October 6-9, 2015 | The Venetian - Las Vegas, NV
Amazon EMR is more than just MapReduce.
Bootstrap actions available on GitHub
In the next few slides, we’ll talk about data persistence models with Amazon EMR. The first pattern is Amazon S3 as HDFS. With this data persistence model, data gets stored on Amazon S3. HDFS does not play any role in storing data. As a matter of fact, HDFS is only there for temporary storage. Another common thing I hear is that storing data on Amazon S3 instead of HDFS slows my job down a lot because data has to get copied to the HDFS/disk first before processing starts. That’s incorrect. If you tell Hadoop that your data is on Amazon S3, Hadoop reads directly from Amazon S3 and streams data to Mappers without toughing the disk. Not to be completely correct, data does touch HDFS when data has to shuffle from mappers to reducers, but as I mentioned, HDFS acts as the temp space and nothing more.
EMRFS is an implementation of HDFS used for reading and writing regular files from Amazon EMR directly to Amazon S3. EMRFS provides the convenience of storing persistent data in Amazon S3 for use with Hadoop while also providing features like Amazon S3 server-side encryption, read-after-write consistency, and list consistency.
And every other feature that comes with Amazon S3. Features such as SSE, LifeCycle, etc. And again keep in mind that Amazon S3 as the storage is the main reason why we can’t build elastic clusters where nodes get added and removed dynamically without any data loss.
In the next few slides, we’ll talk about data persistence models with EMR. The first pattern is Amazon S3 as HDFS. With this data persistence model, data gets stored on Amazon S3. HDFS does not play any role in storing data. As a matter of fact, HDFS is only there for temporary storage. Another common thing I hear is that storing data on Amazon S3 instead of HDFS slows my job down a lot because data has to get copied to HDFS/disk first before processing starts. That’s incorrect. If you tell Hadoop that your data is on Amazon S3, Hadoop reads directly from Amazon S3 and streams data to Mappers without toughing the disk. Not to be completely correct, data does touch HDFS when data has to shuffle from mappers to reducers, but as I mentioned, HDFS acts as the temp space and nothing more.
EMRFS is an implementation of HDFS used for reading and writing regular files from Amazon EMR directly to Amazon S3. EMRFS provides the convenience of storing persistent data in Amazon S3 for use with Hadoop while also providing features like Amazon S3 server-side encryption, read-after-write consistency, and list consistency.
In the next few slides, we’ll talk about data persistence models with EMR. The first pattern is Amazon S3 as HDFS. With this data persistence model, data gets stored on Amazon S3. HDFS does not play any role in storing data. As a matter of fact, HDFS is only there for temporary storage. Another common thing I hear is that storing data on Amazon S3 instead of HDFS slows my job down a lot because data has to get copied to HDFS/disk first before processing starts. That’s incorrect. If you tell Hadoop that your data is on Amazon S3, Hadoop reads directly from Amazon S3 and streams data to Mappers without toughing the disk. Not to be completely correct, data does touch HDFS when data has to shuffle from mappers to reducers, but as I mentioned, HDFS acts as the temp space and nothing more.
EMRFS is an implementation of HDFS used for reading and writing regular files from Amazon EMR directly to Amazon S3. EMRFS provides the convenience of storing persistent data in Amazon S3 for use with Hadoop while also providing features like Amazon S3 server-side encryption, read-after-write consistency, and list consistency.
EMR example #3: EMR for ETL and query engine for investigations which require all raw data
CloudFront logs arrive out of order.
200 node cluster
spin it up daily, shut it down
Nasdaq security, HasOffers loads 60M rows per day in 2 min intervals, Desk: high concurrency user facing portal (read/write cluster), Amazon.com/NTT PB scale. Pinterest saw 50-100x speed ups when moved 300TB from Hadoop to Redshift. Nokia saw 50% reduction in costs.
Today we will over the role of Amazon Redshift in addressing the Web Log Analysis problem for one of the largest online retailer, Amazon.com
<go over the slide with restated language>
Read only the data you need
Read only the data you need
Read only the data you need
Read only the data you need
Read only the data you need
Comments on next slide.
Redshift is a distributed system:
A cluster contains a leader node and compute nodes
A compute node contains slices (one per core) that contain data
Data is distributed among slices in 3 ways:
Even – Rows distributed in Round Robin fashion (default)
Key – Rows distributed based on a distribution key (hash of a defined column)
All - Rows distributed to all slices
Queries run on all slices in parallel
Optimal query throughput can be achieved when data is evenly spread across slices
Redshift leverages sorting in storage.
Redshift stores column data in blocks, for the sort key, the data blocks are “marked” with the min and max value of this columns, allowing Redshift to skip reading the blocks that are not relevant to the current query.
Check that join parameter statement is true (best practices on designing tables)
Redshift works with customer’s BI tool of choice through Postgres drivers and a JDBC, ODBC connection. A number of partners shown here have certified integration with Redshift, meaning they have done testing to validate/build Redshift integration and make using Redshift easy from a UI perspective. If there are tools customer’s use not shown we can work with Redshift on getting them integrated.
So, we started with our MySQL server. But this time we would run directly on the server itself SQL statements that would dump the data out to local files. Then using s3cmd we copied the flat files into our S3 bucket.
Select data from MySQL and use the S3cmd to copy these flat files to S3.
Use BCP to export data into an EC2 instance, which generates and copies flat files to S3.
And then instead of using EMR, we just run some crazy SQL statements to transform the data into the Production version of Redshift.
Copy data into a staging schema in Redshift where it can be transformed via SQL to the final table structure and loaded into the production schema.
Use standard tools, like Microstrategy and Tableau, to provide business views into the data.
And then of course we need a good way for business users to look at the data, and that’s where MicroStrategy and Tableau come into play.