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Slalom, AWS, and Women in Big Data Present

Building Intelligent Data Lakes

Thursday, May 30, 2019
12:00 PM - 5:00 PM

Amazon Web Services: Ballston Office
4250 N Fairfax Drive, Floor 13
Arlington, VA 22203

Please join us for lunch and an afternoon of learning delivered by women in technology. You will hear from women leaders in technology, and connect with AWS subject matter experts through a hands-on lab featuring how to build a data lake using AWS Services.

Overview - Building Intelligent Data Lakes

Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. AWS provides secure, scalable, comprehensive, and cost-effective portfolio of services that enable customers to build their data lakes in the cloud, analyze all their data, including data from IoT devices with a variety of analytical approaches including machine learning. In this workshop, we discuss key concepts and architectural components of a data lake, explore the AWS broad set of analytic and data management tools in a data lake architecture, and implement a data lake.

Agenda:

12:00pm - 1:00pm —Lunch/Networking

1:00pm - 1:15pm — Welcome & Women in Big Data overview

1:15pm - 1:40pm — Building Intelligent Data Lakes

1:40pm - 2:00pm — Customer Story

2:00pm - 4:00pm — Hands-on Lab: Build a Data Lake using AWS Services

4:00pm - 5:00pm — Panel Discussion featuring Women in Tech

Hands on Labs

Hands on Labs

Learn how to build an end to end Serverless Data Lake pipeline to gain business insights using native AWS services which includes Amazon SageMaker.

Lab 1: Hydrate the data lake

In this lab, you will generate streaming sensor data using AWS Lambda. The sensor data will be collected using Amazon Kinesis Data Streams and Amazon Kinesis Firehose.  The data will be stored durably and securely in Amazon Simple Storage Service. We will also perform data analytics on streaming data using Amazon Kinesis Data Analytics.

Lab 2: Catalog, transform and visualize data

In this lab, we will use AWS Glue to catalog the data, run jobs to transform the data format, and share the data with other AWS services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. Finally, we will visualize this data using Amazon QuickSight to derive business insights.

Lab 3: Advanced analytics featuring Amazon SageMaker

In this lab, we will use Amazon SageMaker to train, create, and host a machine learning model to perform anomaly detection similar to Amazon Kinesis Analytics in Lab 1.

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