Getting Started with Big Data on AWS

10AM – 11AM Architectural Patterns and best Practices for Big Data on AWS

In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost. Level: 200

11AM – Noon Spark and the Hadoop Ecosystem: Getting started with Amazon EMR

Amazon EMR is a managed Hadoop 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 show 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. Then we will walk through a demo to show how you can deploy a Spark environment running on Amazon EMR within minutes. Level: 200

Noon-1PM Lunch Break

1PM – 2PM Data Warehousing: Intro to Amazon Redshift

Amazon Redshift is a fast, simple, cost-effective data warehousing service. In this session, you'll get an overview of how Amazon Redshift uses columnar technology, massively parallel processing, and other techniques to deliver fast query performance on petabyte-size datasets. Learn how you can set up a fully functional data warehouse with just a few clicks in the AWS Management Console. Level: 100

2PM – 3PM Predictive analytics: Getting Started with Amazon Machine Learning

Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will show you how to use machine learning with the data you already have to arrive at accurate and actionable predictions, to create smart applications. Level: 100

3PM – 4PM Real-time Streaming: Intro to Amazon Kinesis

Amazon Kinesis is a platform for streaming data on AWS, offering powerful services to make it easy to load and analyze streaming data; and also providing the ability for you to build custom streaming data applications for specialized needs. This session, will provide you with an overview of Amazon Kinesis Firehose, Amazon Kinesis Analytics, and Amazon Kinesis Streams.  We will also discuss sample use cases, and reference architecture so you can quickly get started with real-time, streaming data. Level: 100

4PM – 6PM Workshop 1: Building your First Big Data App with AWS

Want to get ramped up on how to use Amazon’s big data web services and launch your first big data application on AWS? Join us on our journey as we build a big data application using Amazon Kinesis, Amazon EMR, and other Amazon big data web services. Level: 200

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Building Big Data Solutions with AWS

10AM – 11AM Building a Data Lake on AWS

A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema. Data Lakes enable a far more agile and flexible architecture, which make it easier to gain new types of analytical insights from your data. In this session, we introduce key architectural concepts to build a data lake using Amazon S3 as the storage layer and making this data available for processing with a broad set of analytic options including Amazon EMR and open source frameworks such as Apache Hadoop, Spark, Presto, and more. Level: 300

11AM – Noon Working with Apache Kafka and Amazon Kinesis on AWS

Streaming data processing has become increasingly prevalent and different platforms and frameworks have emerged to ingest, process, and analyzing streaming data in a durable and scalable way. In this session you will learn about two popular choices for streaming data applications on AWS, namely Apache Kafka and Amazon Kinesis. We will help you understand how these services work together so you can quickly build real-time applications and get actionable insights from streaming data. Level: 200

Noon-1PM Lunch Break

1PM – 3PM Workshop 2: Building a Streaming Data Platform on AWS  

Evolving your analytics from batch processing to real-time processing can have a major impact on your business. However, designing and maintaining applications to continuously collect and analyze large amounts of data per hour can be extremely difficult without the right tools. Using Amazon Kinesis you can easily ingest and analyze streaming data without the need to build any complex pipelines. In this workshop, we will show you how you can build your first real-time processing application on AWS. Level: 200

3PM – 5:30PM Workshop 3: Abstract and Session Coming Soon! Level: 200

5:30PM-7:30PM Big Data & Analytics Networking Reception and Happy Hour!

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Big Data Technical Deep Dives

10AM– 11AM Amazon Kinesis Deep Dive – In this session, we discuss best practices for building an end-to-end streaming data application using Amazon Kinesis. First, we focus on building a scalable, durable streaming data ingestion workflow from data producers like mobile devices, servers, and web browsers. Next, we show some of the proven architectures for processing streaming data using a combination of tools including Amazon Kinesis Streams, Kinesis Firehose, Kinesis Analytics, and Spark Streaming running on Amazon EMR. Finally, we address technical questions frequently asked by those building streaming data applications. Level: 300

11AM – Noon Amazon EMR Deep Dive & best practices

In this session, we introduce you 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. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can optimize your cluster.

Noon-1PM Lunch Break

1PM-2PM Amazon Redshift Deep Dive & Best Practices

Learn best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your data warehouse performance. This session explains how to create an optimized schema, efficiently load data, use work load management, and use Amazon Redshift's interleaved sorting features. Level: 300

2PM-3PM  Visualizing Big Data Insights with Amazon QuickSight

In this session we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE – a Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight – which performs advanced calculations and renders visualizations rapidly without requiring any additional infrastructure or programming. Level: 100

3PM-4PM Deep Dive on Predictive Analytics with Amazon Machine Learning and SparkML

In this session, we will start with a light demo of Amazon QuickSight to visualize a dataset, and then build a model with Amazon ML, demonstrating how you can perform predictions in real-time. We will also demonstrate how you can use Spark ML on Amazon EMR, connecting to Zeppelin, walking you through how to load data, build a collaborative filtering model and predict in batch and real time mode. Level: 200

Please stay tuned for session updates closer to the event!

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