AWS Data Modernization Week

Track Description:

This track is intended to help attendees learn how to move their data infrastructure to the cloud and leverage AWS database and analytics services that will help them deliver value from their data, while keeping it secure. Attendees will learn best practices for data movement, eliminating data silos, and analyzing diverse datasets easily and securely. Learn how AWS cloud databases can help you meet your distinct use cases all while delivering operational efficiency, performance, availability, scalability, security, and compliance.

Session 1: Build a Serverless Analytics Framework

Level 200

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speaker: Bob Maus

This session will describe customer pain points and an overview of the Analytics serverless capabilities (Amazon Redshift, Amazon EMR, Amazon MSK, and Amazon Kinesis), how they are helping AWS customers overcome these pain points, and typical use cases where the Analytics serverless capabilities are deployed.

Session 2: Best Practices for Migrating from SQL Server to Amazon Aurora Part 1

Level 300-400

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speakers: Anuja Malik, Cedrick Hoodye

In this session, you will learn best practices and how to avoid common pitfalls when migrating from SQL Server to Amazon Aurora. We’ll also discuss how to use Aurora PostgreSQL Babelfish to accelerate migrations, as well as resources like AWS Database Migration Service (DMS), AWS Schema Conversion Tool (SCT), and more. This session will continue with a live demo and partner case study in Part 2.

Session 3: Best Practices for Migrating from SQL Server to Amazon Aurora Part 2

Level 300-400

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speakers: Anuja Malik, Cedrick Hoodye, Trevor Banks (Datavail)

Reduce your dependency on commercial databases by migrating to Amazon Aurora to increase agility and lower cost. In this continuation of the previous session, you’ll see a live demo of how to leverage SCT and DMS to complete a heterogeneous database migration. AWS partner Datavail will also share a real-world modernization journey and migration to Aurora PostgreSQL. Then join us on April 6 in Lab track 1 to apply what you learned in the Assess and Migrate from SQL Server to Amazon Aurora hands-on lab.

Track Description:

This track is intended to help attendees learn the approaches, tools, and frameworks to break down data silos and make data more accessible to everyone who needs it to enable secure and governed discovery, access, and analysis. Learn how AWS purpose-built databases and purpose-built analytics services empower organizations to use purpose-built data stores to get the best performance, scale, and cost advantages for their use cases.

Session 1: Turbocharge Amazon RDS Performance with Amazon ElastiCache for Redis In-Memory Caching

Level 300

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speaker: Damon LaCaille

Learn how Amazon ElastiCache can deliver high throughput and low latency for your data-driven Amazon RDS applications, providing massive scale with sub-millisecond response times. Review a standard caching strategy, see a demonstration of caching in action, and learn how to quickly complement RDS with ElastiCache.

Session 2: Amazon Redshift Data Sharing to Build a Scalable Multi-tenant Architecture

Level 300

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speaker: Milind Oke

With Amazon Redshift data sharing, organizations can now have instant, granular, and fast data access across Amazon Redshift clusters without the need to copy or move it. This session will illustrate how to effectively use data sharing through real-world use cases and discuss multi-tenant architecture patterns to meet their requirements.

Session 3: What's New in AWS Lake Formation?

Level 200

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speaker: Adnan Hasan

Building data lakes and securing and sharing data can be challenging for many organizations. Learn how AWS Lake Formation can simplify data ingestion and enable a database-like permissions model for your data lakes. We will discuss recently launched AWS Lake Formation Erie features such as Row/Cell level security, transactions and storage optimization in Governed tables, and a new Storage API to improve integration with other AWS analytics and 3rd party services.

Track Description:

This track is intended to help attendees discover the various database, analytics, and machine learning integration services available on AWS that can help build, deploy, and innovate at scale. Whether you want to enhance your customer experience, improve productivity and optimize business processes, or speed up and scale up innovation, you can access the most complete set of integrated data and ML services to meet your business needs.

Session 1: Integrating Amazon RDS MySQL with Amazon Redshift, AWS Glue and Amazon QuickSight for Analytics and Reporting

Level 300

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speakers: Anuja Malik, Phil Bates

Customers are building more and more modern architectures by integrating different purpose-built data services to gain faster insights from their data. Join this session to learn how you can integrate a relational database like RDS MySQL with Redshift, Glue, and QuickSight for faster analytics and reporting.

Session 2: Evolve from Delivering Transactional to Continuous Real-time Intelligence for Next-level Customer Experiences

Level 200

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speaker: Jeremy Ber

Speed is a key characteristic businesses need for a competitive edge in today’s world. Consumers increasingly want experiences that are timely, targeted, and tailored to their specific needs, whether they are applying for a loan, checking health alerts, online shopping, or monitoring systems. Join us in this session to understand how you can use AWS data streaming platforms to tap in to real-time insights that can help take your customer experiences to the next level.

Session 3: Democratize Machine Learning using SQL and Amazon Redshift ML

Level 300

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speaker: Srikanth Sopirala

Experience how Redshift can help you gain insights to the metrics needed to run your business. Redshift ML allows Data Analysts and Data Scientists to easily train machine learning models using SQL without having to move data. Data Engineers will learn how the Data API simplifies access and allows for the easy integration of applications to build event-driven applications systems.

Track Description:

In this track, attendees will gain hands-on experience with Amazon Aurora through interactive labs guided by AWS experts. Labs in this track are intended as companions to our Aurora-focused sessions, allowing you to apply what you learn, explore Aurora features, and migrate from legacy systems. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of each workshop.

Lab: Maximize Resiliency with Amazon Aurora Advanced Features

Level 400

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speakers: Surendar Munimohan, David Ilaboya/Adarsha Kuthuru

Learn how to maximize resiliency and survive regional resource or service failure issues by operating across regions. In this hands-on lab, you'll set up and manage cross-region disaster recovery and perform planned and unplanned failover using Amazon Aurora Global Database.

Track Description:

In this track, attendees will gain hands-on experience with AWS Analytics and Relational Database services through interactive labs guided by AWS experts. Labs in this track are intended as companions to related sessions throughout the agenda, allowing you to apply what you learn, explore service features, and migrate from legacy systems. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of each workshop.

Lab: Migrating Self-managed SQL Server to Amazon RDS SQL Server with Minimal Downtime

Level 300

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speakers: Mesgana Gormley, Julius Sacramento

In this lab, you'll get hands-on experience moving your database from a self-managed SQL Server to an Amazon RDS SQL Server. Learn how to seed data using native backup/restore tools and use AWS Database Migration Service (AWS DMS) to set up continuous replication with minimal downtime.

Track Description:

In this track, attendees will gain hands-on experience with AWS Analytics and Nonrelational Database services through interactive labs guided by AWS experts. Labs in this track are intended as companions to related sessions throughout the agenda, allowing you to apply what you learn and explore features of these services more deeply. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of the workshop.

Lab: Introduction to DocumentDB (with MongoDB compatibility)

Level 300-400

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speaker: Karthik Vijayraghavan

During this lab you will learn how to create a DocumentDB cluster, scale the cluster, and do simple create/read/update/delete (CRUD) operations. We will then move to more advanced topics such as monitoring and running globally distributed applications.

Track Description:

This track is intended to help attendees learn how to move their data infrastructure to the cloud and leverage AWS database and analytics services that will help them deliver value from their data, while keeping it secure. Attendees will learn best practices for data movement, eliminating data silos, and analyzing diverse datasets easily and securely. Learn how AWS cloud databases can help you meet your distinct use cases all while delivering operational efficiency, performance, availability, scalability, security, and compliance.

Session 1: Best Practices for Migrating from Oracle to Amazon Aurora - Part 1

Level 300-400

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speakers: Mark Mulligan, Nelly Susanto

In this session, you will learn best practices and how to avoid common pitfalls when migrating from Oracle to Amazon Aurora. We’ll also discuss AWS Database Migration Service (DMS), AWS Schema Conversion Tool (SCT), and additional resources and programs to support your Database Freedom migrations. This Part 1 of the two-part series focuses on SCT best practices including a demo. This session is followed by Part 2 that will focus on using DMS to migrate the data along with a partner case study.

Session 2: Best Practices for Migrating from Oracle to Amazon Aurora - Part 2

Level 300-400

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speakers: Mark Mulligan, Nelly Susanto, Tom Camarro (Navisite)

Reduce your dependency on commercial databases by migrating to Amazon Aurora to increase agility and lower cost. In this continuation of the previous session, you’ll see some best practices and a demonstration of the Database Migration Service to complete a heterogeneous database migration. AWS partner Navisite will also share a real-world case study of a migration to Aurora PostgreSQL. Then join us on April 7 in Lab track 1 to apply what you learned in the Migrate from Oracle to Amazon Aurora hands-on lab.

Session 3: Migration Deep Dive with Amazon OpenSearch Service

Level 200

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speaker: Kevin Fallis

When businesses begin migrating their self-managed Elasticsearch, OpenSearch, and other search solutions to the Amazon OpenSearch Service, they need to understand migration patterns that will make them successful. In this session you will learn about focus areas that help you prepare for a migration. You will also learn about tooling, approaches, and mechanisms that can help you migrate your workloads to Amazon OpenSearch Service.

Track Description:

This track is intended to help attendees learn the approaches, tools, and frameworks to break down data silos and make data more accessible to everyone who needs it to enable secure and governed discovery, access, and analysis. Learn how AWS purpose-built databases and purpose-built analytics services empower organizations to use purpose-built data stores to get the best performance, scale, and cost advantages for their use cases.

Session 1: Why a managed search service based on Open Source principles is important

Level 200

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speaker: Arun Lakshmanan

Attend this session to learn: (1) What is Amazon OpenSearch Service (2) How it provides a choice of open source engines to deploy and run, including the latest versions of OpenSearch and the currently available 19 versions of ALv2 Elasticsearch (7.10 and earlier) (3) Why the freedom and innovation of open source-based software gives users greater freedom and fosters innovation (4) How to take advantage of Amazon OpenSearch Service's new features, dashboards, and operational simplicity to power your organization's log analytics and full-text search use-cases.

Session 2: Oracle Applications on Amazon RDS Best Practices

Level 300

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speakers: Sachin Vaidya, Jeremy Shearer

In this session, we will dive deep into why AWS is the best place to host Oracle applications. We will also discuss best practices for architecting your Oracle applications to be highly available, efficient, flexible, and fault tolerant using AWS Relational Database Services.

Session : Amazon RDS Custom for Oracle Technical Overview

Level 300

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speaker: Yamuna Palasamudram

In this session, we will deep dive into some of the exciting new features of Amazon RDS Custom for Oracle. We will provide deeper insight into use cases and service capabilities accompanied by a demo.

Track Description:

This track is intended to help attendees discover the various database, analytics, and machine learning integration services available on AWS that can help build, deploy, and innovate at scale. Whether you want to enhance your customer experience, improve productivity and optimize business processes, or speed up and scale up innovation, you can access the most complete set of integrated data and ML services to meet your business needs.

Session 1: AWS Partner Case Study: An ISV Migration to Amazon Aurora

Level 300

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speakers: Hetav Sanghavi, Venkat Mandala (Virtusa), Aliaksei Klimko (SugarCRM)

In this session, systems integrator and AWS partner Virtusa will discuss the architectural advantages of Amazon Aurora and why it has been proven to be a preferred choice for their customers for supporting enterprise-scale workloads. Customers often ask why they would choose Aurora over Amazon EC2 or Amazon RDS and what use cases are a fit for Aurora. Virtusa’s AWS-certified architect and ISV customer SugarCRM will share details of their successful migration and the benefits it brought to their organization.

Session 2: Building a Customer 360 Graph Application on Amazon Neptune

Level 300-400

9:00am - 10:00am PT  |  12:00pm - 3:00pm ET

Speaker: Justin Thomas

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. In this session, we show how Amazon Neptune can be used to build a Customer 360 graph for applications in marketing and targeted advertising use cases. In part 1, we’ll walk through how to design the graph data model for Customer 360 use cases. Then join us for Part 2 to learn how to gain insights through queries and visualizations.

Track Description:

In this track, attendees will gain hands-on experience with Amazon Aurora through interactive labs guided by AWS experts. Labs in this track are intended as companions to our Aurora-focused sessions, allowing you to apply what you learn, explore Aurora features, and migrate from legacy systems. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of each workshop.

Lab: Assess and Migrate from SQL Server to Amazon Aurora

Level 300-400

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speaker: Bachar Rifai

Reduce your dependency on commercial engine databases by migrating to cloud native managed databases to save time and cost. In this hands-on lab you wil learn how to leverage AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS) to complete a heterogeneous workload migration from SQL Server to Amazon Aurora PostgreSQL Compatible Edition. You'll start with an SCT assessment of the source database to determine the level of effort and compare migration targets. Then you'll run SCT to convert the database schema. Finally, run DMS to load the target database and validate the results to "Break Free" from commercial engine databases. Suggested Prerequisites: Best Practices for Migrating from SQL Server to Amazon Aurora Part 1 & 2

Track Description:

In this track, attendees will gain hands-on experience with AWS Analytics and Relational Database services through interactive labs guided by AWS experts. Labs in this track are intended as companions to related sessions throughout the agenda, allowing you to apply what you learn, explore service features, and migrate from legacy systems. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of each workshop.

Lab: Data Warehouse Modernization and Migration to Amazon Redshift (90 min)

Level 300

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speakers: Ranjan Burman, Indira Balakrishnan

On-premises data warehouses can be complex and expensive to manage. Migrating your on-premises data warehouse to Amazon Redshift can substantially improve query and data load performance, increase scalability, and save costs. This workshop leverages AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) to migrate an existing on-premises data warehouse to Amazon Redshift.

Track Description:

In this track, attendees will gain hands-on experience with AWS Analytics and Nonrelational Database services through interactive labs guided by AWS experts. Labs in this track are intended as companions to related sessions throughout the agenda, allowing you to apply what you learn and explore features of these services more deeply. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of the workshop.

Lab: Turbocharge Amazon RDS with Amazon ElastiCache for Redis

Level 400

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speaker: Siva Karuturi, Riyas Nalakkath Abdulrasak

During this lab you will leverage Amazon ElastiCache for Redis features to build and run a caching layer for your Amazon RDS workloads. This offloads pressure from your main RDS database to improve read performance and scalability, which can help reduce your overall cost. Join us to see how ElastiCache can fulfill your high-performance requirements.

Track Description:

This track is intended to help attendees learn how to move their data infrastructure to the cloud and leverage AWS database and analytics services that will help them deliver value from their data, while keeping it secure. Attendees will learn best practices for data movement, eliminating data silos, and analyzing diverse datasets easily and securely. Learn how AWS cloud databases can help you meet your distinct use cases all while delivering operational efficiency, performance, availability, scalability, security, and compliance.

Session 1: Building modern, high performance applications at any scale with Amazon DynamoDB - Part 1

Level 300

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speaker: Jason Hunter

Amazon DynamoDB offers an enterprise-ready database that helps you deliver apps with consistent, single-digit millisecond performance and nearly unlimited throughput and storage. In this session, we'll review some of the most powerful features that will help you save costs while driving the most business impact, such as multi-region replication with global tables, optimizing for cost with new DynamoDB table classes, on-demand capacity mode for spiky workloads, and exporting data from your continuous backups to Amazon S3.

Session 2: Building modern, high performance applications at any scale with Amazon DynamoDB - Part 2

Level 300

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speaker: Jason Hunter

Amazon DynamoDB offers an enterprise-ready database that helps you deliver apps with consistent, single-digit millisecond performance and nearly unlimited throughput and storage. In this session, we'll review some of the most powerful features that will help you save costs while driving the most business impact, such as multi-region replication with global tables, optimizing for cost with new DynamoDB table classes, on-demand capacity mode for spiky workloads, and exporting data from your continuous backups to Amazon S3.

Session 3: Building Analytics at Scale with Amazon Athena

Level 200

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speakers: Naresh Gautam, Suresh Akena

Amazon Athena is a highly scalable analytics service that makes it easy to analyze data in Amazon S3 and other data stores. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. This session offers a deep dive into the service, customer use cases, newly launched features, and what is next for Amazon Athena.

Track Description:

This track is intended to help attendees learn the approaches, tools, and frameworks to break down data silos and make data more accessible to everyone who needs it to enable secure and governed discovery, access, and analysis. Learn how AWS purpose-built databases and purpose-built analytics services empower organizations to use purpose-built data stores to get the best performance, scale, and cost advantages for their use cases.

Session 1: Building Data Pipelines using Amazon EMR on Amazon EC2 and EMR on Amazon EKS with Amazon Managed Workflows for Apache Airflow

Level 400

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speaker: Leonardo Gomez

Learn how to deploy and run data pipelines on Amazon Managed Workflows for Apache Airflow (MWAA) using EMR on EC2 and EMR on EKS. In this session, we'll show how to schedule jobs in Airflow using MWAA with EMR Operators and how to deploy and configure Airflow, EMR, and PySpark jobs.

Session 2: ETL Modernization with AWS Glue

Level 300

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speaker: Shiv Narayanan

The ETL/Data Integration landscape is significantly changing, with organizations looking for cost effective, scalable solutions that enable self-service data integration. In this session, we'll cover emerging trends in data integration, discuss how AWS Glue enables self-service and infuses ML to transform data, and share best practices for ETL modernization.

Session 3: Amazon Aurora Performance Optimization Techniques

Level 300

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speakers: Rajesh Matkar, Arabinda Pani

Amazon Aurora is a high-performance, highly scalable database service with MySQL and PostgreSQL compatibility. While Aurora offers high performance with multi-region, automatic storage scaling, and high throughput, learn about different techniques to optimize your database performance.

Track Description:

This track is intended to help attendees discover the various database, analytics, and machine learning integration services available on AWS that can help build, deploy, and innovate at scale. Whether you want to enhance your customer experience, improve productivity and optimize business processes, or speed up and scale up innovation, you can access the most complete set of integrated data and ML services to meet your business needs.

Session 1: Protect against Ransomware and Insider Threats with Best Practices on Data Resiliency

Level 300

8:00am - 9:00am PT  |  11:00am - 12:00pm ET

Speakers: Sundar Raghavan, Sukhpreet Kaur Bedi

In this session, we will dive deep into best practices for configuring Amazon Aurora databases to safeguard against ransomware and related insider threats to your database.

Session 2: Achieve Compliance and Enhanced Security with Relational Databases for Financial Services

Level 300

9:00am - 10:00am PT  |  12:00pm - 1:00pm ET

Speakers: Shayon Sanyal, Rajib Sadhu

In this session, we will walk through the best practices and reference architectures for achieving compliance and enhanced security for Amazon Aurora for Financial Services use cases.

Session 3: Simplify Data Integration and Preparation using AWS Glue

Level 300

10:00am - 11:00am PT  |  1:00pm - 2:00pm ET

Speakers: Shiv Narayanan, Deen Prasad

There are many ways to ingest and prepare data for analytics and ML. In this session, you will learn how AWS Glue Studio makes it simple and easy to visually build data integration pipelines using hundreds of connectors and transformations. We will also cover how using AWS Glue DataBrew can simplify data preparation tasks, allowing your users to get insights from data quicker. Lastly, we will discuss the enhancements made to the underlying AWS Glue execution engine to improve performance and reliability and reduce cost.

Track Description:

In this track, attendees will gain hands-on experience with Amazon Aurora through interactive labs guided by AWS experts. Labs in this track are intended as companions to our Aurora-focused sessions, allowing you to apply what you learn, explore Aurora features, and migrate from legacy systems. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of each workshop.

Lab: Migrate from Oracle to Amazon Aurora

Level 300-400

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speakers: Akm Raziul Islam

Save time and cost and reduce your dependency on Oracle databases by migrating to cloud native managed databases. In this session you will learn how to leverage AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS) to complete a heterogeneous workload migration from Oracle to Aurora. Suggested Prerequisites: Best Practices for Migrating from Oracle to Amazon Aurora Part 1 & 2.

Track Description:

In this track, attendees will gain hands-on experience with AWS Analytics and Relational Database services through interactive labs guided by AWS experts. Labs in this track are intended as companions to related sessions throughout the agenda, allowing you to apply what you learn, explore service features, and migrate from legacy systems. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of each workshop.

Lab: Working with Amazon RDS Oracle Advanced Features

Level 400

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speakers: Nathan Fuzi, Sachin Vaidya

In this hands-on lab, you'll learn how to manage high availability, disaster recovery, backup, upgrades, and monitoring in RDS Oracle. By participating in the "Working with Amazon RDS Oracle Advanced Features" Lab you agree that you have valid Oracle Database Enterprise Edition license(s) with active “Software Update License & Support”. You further agree to be bound by the terms contained in Section 10.3.2. of the AWS Service Terms.

Track Description:

In this track, attendees will gain hands-on experience with AWS Analytics and Nonrelational Database services through interactive labs guided by AWS experts. Labs in this track are intended as companions to related sessions throughout the agenda, allowing you to apply what you learn and explore features of these services more deeply. Labs feature primarily 300-400 level content and are intended for those experienced with the AWS console. Access to lab environments will be provided for the duration of the workshop.

Lab: Migrate your Kafka Workloads to Amazon MSK using MirrorMaker 2

Level 300

8:00am - 11:00am PT  |  11:00am - 2:00pm ET

Speaker: Ali Alemi

MirrorMaker is a tool that supports replicating data between Apache Kafka clusters. Join us in this hands-on lab to learn how you can use MirrorMaker 2.0 to migrate your Apache Kafka workloads to Amazon MSK.