Amazon EMR is one of the largest Hadoop operators in the world. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features. This session will feature Asurion, a provider of device protection and support services for over 280 million smartphones and other consumer electronics devices. Asurion will share how they architected their petabyte-scale data platform using Apache Hive, Apache Spark, and Presto on Amazon EMR.
Tune your Big Data Platform to Work at Scale: Taking Hadoop to the Next Level...Amazon Web Services
Learn how to set up a highly scalable, robust, and secure Hadoop platform using Amazon EMR. We'll perform a demonstration using a 100-node Amazon EMR cluster and take you through the best practices and performance tuning required for different workloads to ensure they are production ready.
Speaker: Amo Abeyaratne, Big Data Consultant, Amazon Web Services
Featured Customer - Ambidata
Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; Deployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively.
BDA 302 Deep Dive on Migrating Big Data Workloads to Amazon EMRAmazon Web Services
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to Amazon EMR in order to save costs, increase availability, and improve performance. Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. This session will focus on identifying the components and workflows in your current environment and providing the best practices to migrate these workloads to Amazon EMR. We will explain how to move from HDFS to Amazon S3 as a durable storage layer, and how to lower costs with Amazon EC2 Spot instances and Auto Scaling. Additionally, we will go over common security recommendations and tuning tips to accelerate the time to production.
Introduction to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of Spot EC2 instances to reduce costs, and other Amazon EMR architectural best practices.
This is 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.
Accelerate your Business with SAP on AWS - AWS Summit Cape Town 2017 Amazon Web Services
From dev and test to large-scale HANA production deployments, enterprise usage of SAP on AWS is rapidly growing across all verticals and geographies. Gain insights on the AWS SAP offering and partnership and why a cloud first approach makes business, technical and financial sense for the numerous SAP solutions that are certified and ready to be deployed today.
AW Speaker: Michael Needham, Sr Mgr, Solutions Architecture - Amazon Web Services
Strategic Uses for Cost Efficient Long-Term Cloud StorageAmazon Web Services
Compared to storing long-term datasets on-premises, archiving in the cloud is a smart alternative whether you’re looking for an active archive solution, tape replacement, or to fulfill a compliance requirement. Learn how AWS customers are simplifying their archiving strategy and meeting compliance needs using Amazon Glacier. Hear how customers have evolved their backup and disaster recovery architectures and replaced tape solutions by turning to AWS for a more cost efficient, durable and agile solution. We will showcase Sony DADC's active archive deployment on Glacier and demo how some of our financial service customers have set up compliant archives to meet their regulatory objectives.
Tune your Big Data Platform to Work at Scale: Taking Hadoop to the Next Level...Amazon Web Services
Learn how to set up a highly scalable, robust, and secure Hadoop platform using Amazon EMR. We'll perform a demonstration using a 100-node Amazon EMR cluster and take you through the best practices and performance tuning required for different workloads to ensure they are production ready.
Speaker: Amo Abeyaratne, Big Data Consultant, Amazon Web Services
Featured Customer - Ambidata
Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; Deployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively.
BDA 302 Deep Dive on Migrating Big Data Workloads to Amazon EMRAmazon Web Services
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to Amazon EMR in order to save costs, increase availability, and improve performance. Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. This session will focus on identifying the components and workflows in your current environment and providing the best practices to migrate these workloads to Amazon EMR. We will explain how to move from HDFS to Amazon S3 as a durable storage layer, and how to lower costs with Amazon EC2 Spot instances and Auto Scaling. Additionally, we will go over common security recommendations and tuning tips to accelerate the time to production.
Introduction to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of Spot EC2 instances to reduce costs, and other Amazon EMR architectural best practices.
This is 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.
Accelerate your Business with SAP on AWS - AWS Summit Cape Town 2017 Amazon Web Services
From dev and test to large-scale HANA production deployments, enterprise usage of SAP on AWS is rapidly growing across all verticals and geographies. Gain insights on the AWS SAP offering and partnership and why a cloud first approach makes business, technical and financial sense for the numerous SAP solutions that are certified and ready to be deployed today.
AW Speaker: Michael Needham, Sr Mgr, Solutions Architecture - Amazon Web Services
Strategic Uses for Cost Efficient Long-Term Cloud StorageAmazon Web Services
Compared to storing long-term datasets on-premises, archiving in the cloud is a smart alternative whether you’re looking for an active archive solution, tape replacement, or to fulfill a compliance requirement. Learn how AWS customers are simplifying their archiving strategy and meeting compliance needs using Amazon Glacier. Hear how customers have evolved their backup and disaster recovery architectures and replaced tape solutions by turning to AWS for a more cost efficient, durable and agile solution. We will showcase Sony DADC's active archive deployment on Glacier and demo how some of our financial service customers have set up compliant archives to meet their regulatory objectives.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Deploying a Disaster Recovery Site on AWS: Minimal Cost with Maximum EfficiencyAmazon Web Services
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum as well as optimizing your overall capital expense can be challenging. This session presents AWS features and services along with Disaster Recovery architectures that you can leverage when building highly available and disaster resilient applications. We will provide recommendations on how to improve your Disaster Recovery plan and discuss example scenarios showing how to recover from a disaster.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Amazon EMR is one of the largest Hadoop operators in the world. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We will also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features.
Amazon Elastic MapReduce Deep Dive and Best Practices (BDT404) | AWS re:Inven...Amazon Web Services
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch four years ago, our customers have launched more than 5.5 million Hadoop clusters. In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Amazon Aurora is a MySQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. This session introduces you to Amazon Aurora, explains common use cases for the service, and helps you get started with building your first Amazon Aurora–powered application.
Learn how to use Apache Spark on AWS to implement and scale common big data use cases such as Real-time data processing, interactive data science, and more.
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.
AWS re:Invent 2016: How to Scale and Operate Elasticsearch on AWS (DEV307)Amazon Web Services
Elasticsearch has quickly become the leading open source technology for scaling search and building document services on. Many software providers have come to rely on it to serve the needs of high-performance, production applications.
In this talk, we’ll go deep on lessons learned from three years in production scaling from a few shards to more than 100 spread across 100s of nodes on AWS--to serve real-time queries against 100s of millions of documents.
Attendees will learn:
* How to capacity plan for ES on AWS
* How to scale and reshard on AWS with zero downtime
* What AWS and ES metrics to collect and alert on
* Tips on day to day ES operations
Session sponsored by SignalFx.
Amazon RDS enables you to launch an optimally configured, secure, and highly available relational database with just a few clicks. It provides cost-efficient and resizable capacity while managing time consuming administration tasks, freeing you to focus on your applications and business. In this session, we take a closer look at how Amazon RDS works, and we review best practices to achieve performance, flexibility, and cost savings for your MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server databases on Amazon RDS. We also discuss AWS Database Migration Service, a quick and secure means for migrating your existing relational database management system investments to Amazon RDS.
Bursting on-premise analytic workloads to Amazon EMR using AlluxioAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Bursting on-premise analytic workloads to Amazon EMR using Alluxio
Roy Hasson, AWS
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
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.
Database Migration – Simple, Cross-Engine and Cross-Platform MigrationAmazon Web Services
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases.
(BDT322) How Redfin & Twitter Leverage Amazon S3 For Big DataAmazon Web Services
Analyzing large data sets requires significant compute and storage capacity that can vary in size based on the amount of input data and the analysis required. This characteristic of big data workloads is ideally suited to the pay-as-you-go cloud model, where applications can easily scale up and down based on demand. Learn how Amazon S3 can help scale your big data platform. Hear from Redfin and Twitter about how they build their big data platforms on AWS and how they use S3 as an integral piece of their big data platforms.
by Joyjeet Banerjee, Enterprise Solution Architect, AWS
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories. Level 100
Apache Hadoop and Spark on AWS: Getting started with Amazon EMR - Pop-up Loft...Amazon Web Services
Amazon EMR is a managed service that makes it easy for customers to use big data frameworks and applications like Apache Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3, Amazon’s highly scalable object storage service. In this session, we will introduce Amazon EMR and the greater Apache Hadoop ecosystem, and show how customers use them to implement and scale common big data use cases such as batch analytics, real-time data processing, interactive data science, and more. Then, we will walk through a demo to show how you can start processing your data at scale within minutes.
Introduction to Storage on AWS - AWS Summit Cape Town 2017Amazon Web Services
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices that are available to you: Amazon S3, Amazon EBS, Amazon EFS, Amazon Glacier and Cloud Data Migration solutions.
Slide-deck used in Bend Web Design and Development Meetup (http://web.archive.org/web/20150728021205/http://www.meetup.com/Bend-Web-Design-and-Development/events/222592014/)
Amazon Web Services provides startups with the low cost, easy to use infrastructure needed to scale and grow any size business. Attend this session and learn how to migrate your startup to AWS and make the most out of the platform.
(BDT309) Data Science & Best Practices for Apache Spark on Amazon EMRAmazon Web Services
Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Deploying a Disaster Recovery Site on AWS: Minimal Cost with Maximum EfficiencyAmazon Web Services
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum as well as optimizing your overall capital expense can be challenging. This session presents AWS features and services along with Disaster Recovery architectures that you can leverage when building highly available and disaster resilient applications. We will provide recommendations on how to improve your Disaster Recovery plan and discuss example scenarios showing how to recover from a disaster.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Amazon EMR is one of the largest Hadoop operators in the world. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We will also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features.
Amazon Elastic MapReduce Deep Dive and Best Practices (BDT404) | AWS re:Inven...Amazon Web Services
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch four years ago, our customers have launched more than 5.5 million Hadoop clusters. In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Amazon Aurora is a MySQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. This session introduces you to Amazon Aurora, explains common use cases for the service, and helps you get started with building your first Amazon Aurora–powered application.
Learn how to use Apache Spark on AWS to implement and scale common big data use cases such as Real-time data processing, interactive data science, and more.
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.
AWS re:Invent 2016: How to Scale and Operate Elasticsearch on AWS (DEV307)Amazon Web Services
Elasticsearch has quickly become the leading open source technology for scaling search and building document services on. Many software providers have come to rely on it to serve the needs of high-performance, production applications.
In this talk, we’ll go deep on lessons learned from three years in production scaling from a few shards to more than 100 spread across 100s of nodes on AWS--to serve real-time queries against 100s of millions of documents.
Attendees will learn:
* How to capacity plan for ES on AWS
* How to scale and reshard on AWS with zero downtime
* What AWS and ES metrics to collect and alert on
* Tips on day to day ES operations
Session sponsored by SignalFx.
Amazon RDS enables you to launch an optimally configured, secure, and highly available relational database with just a few clicks. It provides cost-efficient and resizable capacity while managing time consuming administration tasks, freeing you to focus on your applications and business. In this session, we take a closer look at how Amazon RDS works, and we review best practices to achieve performance, flexibility, and cost savings for your MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server databases on Amazon RDS. We also discuss AWS Database Migration Service, a quick and secure means for migrating your existing relational database management system investments to Amazon RDS.
Bursting on-premise analytic workloads to Amazon EMR using AlluxioAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Bursting on-premise analytic workloads to Amazon EMR using Alluxio
Roy Hasson, AWS
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
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.
Database Migration – Simple, Cross-Engine and Cross-Platform MigrationAmazon Web Services
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases.
(BDT322) How Redfin & Twitter Leverage Amazon S3 For Big DataAmazon Web Services
Analyzing large data sets requires significant compute and storage capacity that can vary in size based on the amount of input data and the analysis required. This characteristic of big data workloads is ideally suited to the pay-as-you-go cloud model, where applications can easily scale up and down based on demand. Learn how Amazon S3 can help scale your big data platform. Hear from Redfin and Twitter about how they build their big data platforms on AWS and how they use S3 as an integral piece of their big data platforms.
by Joyjeet Banerjee, Enterprise Solution Architect, AWS
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories. Level 100
Apache Hadoop and Spark on AWS: Getting started with Amazon EMR - Pop-up Loft...Amazon Web Services
Amazon EMR is a managed service that makes it easy for customers to use big data frameworks and applications like Apache Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3, Amazon’s highly scalable object storage service. In this session, we will introduce Amazon EMR and the greater Apache Hadoop ecosystem, and show how customers use them to implement and scale common big data use cases such as batch analytics, real-time data processing, interactive data science, and more. Then, we will walk through a demo to show how you can start processing your data at scale within minutes.
Introduction to Storage on AWS - AWS Summit Cape Town 2017Amazon Web Services
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices that are available to you: Amazon S3, Amazon EBS, Amazon EFS, Amazon Glacier and Cloud Data Migration solutions.
Slide-deck used in Bend Web Design and Development Meetup (http://web.archive.org/web/20150728021205/http://www.meetup.com/Bend-Web-Design-and-Development/events/222592014/)
Amazon Web Services provides startups with the low cost, easy to use infrastructure needed to scale and grow any size business. Attend this session and learn how to migrate your startup to AWS and make the most out of the platform.
(BDT309) Data Science & Best Practices for Apache Spark on Amazon EMRAmazon Web Services
Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Monitoring Large-Scale Apache Spark Clusters at DatabricksAnyscale
At Databricks, we manage Apache Spark clusters for customers to run various production workloads. In this talk, we share our experiences in building a real-time monitoring system for thousands of Spark nodes, including the lessons we learned and the value we’ve seen from our efforts so far.
The was part of the talk presented at #monitorSF Meetup held at Databricks HQ in SF.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
Apache Spark is an excellent tool to accelerate your analytics, whether you’re doing ETL, Machine Learning, or Data Warehousing. However, to really make the most of Spark it pays to understand best practices for data storage, file formats, and query optimization. This talk will cover best practices I’ve applied over years in the field helping customers write Spark applications as well as identifying what patterns make sense for your use case.
by Mamoon Chowdry, Solutions Architect
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics 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 Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Sid Chauhan, Solutions architect, AWS
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.
by Avijit Goswami, Sr. Solutions Architect, AWS
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.
Esta sesión está enfocada en mostrar cómo las empresas pueden optimizar sus recursos a través de las soluciones basadas en la nube, poniendo foco en la diferenciación, la innovación y reducción de riesgos en la infraestructura.
Por Ricardo Rentería de Amazon
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
Scaling your Analytics with Amazon Elastic MapReduce (BDT301) | AWS re:Invent...Amazon Web Services
Big data technologies let you work with any velocity, volume, or variety of data in a highly productive environment. Join the General Manager of Amazon EMR, Peter Sirota, to learn how to scale your analytics, use Hadoop with Amazon EMR, write queries with Hive, develop real world data flows with Pig, and understand the operational needs of a production data platform.
We will introduce key concepts for a data lake and present aspects related to its implementation. Also discussing critical success factors, pitfalls to avoid operational aspects, and insights on how AWS enables a server-less data lake architecture.
Speaker: Sebastien Menant, Solutions Architect, Amazon Web Services
If you could not be one of the 60,000+ in attendance at Amazon AWS re:Invent, the yearly Amazon Cloud Conference, get the 411 on what major announcements that were made in Las Vegas. This presentation covers new AWS services & products, exciting announcements, and updated features.
Best Practices Using Big Data on AWS | AWS Public Sector Summit 2017Amazon Web Services
Join us for this general session where AWS big data experts present an in-depth look at the current state of big data. Learn about the latest big data trends and industry use cases. Hear how other organizations are using the AWS big data platform to innovate and remain competitive. Take a look at some of the most recent AWS big data developments. Learn More: https://aws.amazon.com/government-education/
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
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.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Understanding AWS Managed Databases and Analytic Services - AWS Innovate Otta...Amazon Web Services
• Overview of database services to elevate your applications, analytic services to engage your data, and migration services to help you reach database freedom.
• Survey of how Canadian and other organizations are using the cloud to make data scalable, reliable, and secure.
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
AWS re:Invent 2016: Discovery Channel's Broadcast Workflows and Channel Origi...Amazon Web Services
Media delivery requirements are continually changing, driven by accelerating mobile, tablet, smart TV, and set-top technology advances. Broadcasters need agile solutions to the changing media and entertainment landscape that don't require multiyear projects with large upfront investments. In this session, we walk through Discovery Communications' migration of its broadcast playout and channel origination to AWS. Discovery Communications is a leader in nonfiction media, reaching more than 3 billion cumulative viewers in 220 countries and territories. Traditionally, broadcast origination for content delivered to telecommunications companies, cable TV, and satellite has existed only in on-premises data centers. In this session, we walk through Discovery's migration of broadcast playout supporting hundreds of channels worldwide to AWS. We show how Discovery has not only reduced their TCO but also has improved their agility by launching new channels on demand. We also walk through how channel origination is being deployed in a secure, automated fashion, and with a level of high availability that exceeds what is possible in a traditional data center.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
2. What to expect from the session
• Overview of Apache ecosystem on EMR
• Using EMR with S3 and other AWS services
• Securing your EMR application stack
• Lowering costs with Auto Scaling and Spot Instances
• Building a data lake with EMR at Asurion
• Q&A
5. YARN overview
• Dynamically share and
centrally configure the same
pool of cluster resources
across engines
• Schedulers for categorizing,
isolating, and prioritizing
workloads
• Spark dynamic allocation of
executors
• Kerberos authentication
6. YARN schedulers - CapacityScheduler
• Default scheduler specified in Amazon EMR
• Queues
• Single queue is set by default
• Can create additional queues for workloads based on
multitenancy requirements
• Capacity guarantees
• set minimal resources for each queue
• Programmatically assign free resources to queues
• Adjust these settings using the classification capacity-
scheduler in an EMR configuration object
7. On-cluster UIs
Manage applications
SQL editor, Workflow designer,
Metastore browser
Notebooks
Design and execute
queries and workloads
And more using
bootstrap actions!
8. Many storage layers to choose from
Amazon DynamoDB
Amazon RDS Amazon Kinesis
Amazon Redshift
Amazon S3
Amazon EMR
10. Use RDS/Aurora for an external Hive metastore
Amazon Aurora
Hive Metastore for
external tables on S3
Amazon S3Set metastore
location in hive-site
11. S3 tips: Partitions, compression, and file formats
• Avoid key names in lexicographical order
• Improve throughput and S3 list performance
• Use hashing/random prefixes or reverse the date-time
• Compress data set to minimize bandwidth from S3 to
EC2
• Make sure you use splittable compression or have each file
be the optimal size for parallelization on your cluster
• Columnar file formats like Parquet can give increased
performance on reads
14. Security - Configuring VPC subnets
• Use Amazon S3 endpoints in VPC for
connectivity to S3
• Use managed NAT for connectivity to
other services or the Internet
• Control the traffic using security groups
• ElasticMapReduce-Master-Private
• ElasticMapReduce-Slave-Private
• ElasticMapReduce-ServiceAccess
15. Access control by cluster tag and IAM roles
Tag: user = MyUserIAM user: MyUser
EMR role
EC2 role
SSH key
16. Fine-grained access control with Apache Ranger
• Plug-ins for Hive, HBase,
YARN, and HDFS
• Configure Hue and Zeppelin
authentication with LDAP/AD
• Row-level authorization for Hive
(with data-masking)
• Full auditing capabilities with
embedded search
• Run Ranger on an edge node –
visit the AWS Big Data Blog
17. Encryption – Use security configurations
Supported encryption features vary by EMR release
18. Nasdaq uses Presto on Amazon EMR and
Amazon Redshift as a tiered data lake
Full Presentation: https://www.youtube.com/watch?v=LuHxnOQarXU
20. Coming Soon: Advanced Spot provisioning
Master Node Core Instance Fleet Task Instance Fleet
• Provision from a list of instance types with Spot and On-Demand
• Launch in the most optimal Availability Zone based on capacity/price
• Spot Block support