Data Industry and Trends

AWS re:Invent 2016 - The Top 5 Amazon Redshift Breakout Sessions

Written by Peter Gorne|November 16, 2016

We’re sure you’re already beginning to build your re:Invent schedule for 2016. As the largest cloud conference in the world approaches, it’s time to make sure that you’re maximizing your time and making the most of this stimulating Las Vegas week. With over 400 breakout sessions, workshops, trainings and hands-on labs to choose from, we’re all looking forward to this once-a-year event.

In this year’s AWS re:Invent itinerary, there are numerous breakout sessions that focus on data and analytics, particularly the Amazon Redshift data warehouse service.

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Redshift is a key AWS service. Due to its growth and constant advancements, major traditional IT vendors, such as Oracle and IBM, are losing business to Amazon. According to Barron’s blog post: “… it’s possible that RedShift could add $100-125m in incremental revenues every year to reach a $500m business by 2019.”

Redshift is definitely something to consider for your business’ data management, and luckily at this year’s re:Invent, there will be 20 sessions on Redshift alone. If you’re a data engineer, make sure to add these five Amazon Redshift-oriented breakout sessions on your list. Trust us, you won’t want to miss them!

1. Best Practices for Data Warehousing with Amazon Redshift

This is a great place to learn the basics of Amazon Redshift. In order to handle and scale large datasets. That’s exactly what Redshift does for you. It’s a fast, petabyte-scale data warehouse that makes analyzing your data affordable compared to traditional data warehouses. In this session, you’ll dive deep into data warehousing and discuss the best practices for Redshift’s multiple capabilities. Learn more

2. Spotlight Lab: Advanced Amazon Redshift On Data Loading

Once you learned the basics of Amazon Redshift in the above-mentioned session, this is a great next place to go. In this lab, you will compare and test various types of data loading scenarios with Amazon Redshift. You will be loading data from Amazon S3 to Redshift and use Data Pipeline. This includes creating tables, loading data from a remote host, and practicing troubleshooting errors. Learn more

3. Migrating Your Data Warehouse to Amazon Redshift

This session will examine the techniques and tools necessary for migrating your data to Redshift. To show how effective Redshift has been for today’s enterprises, you’ll also review a case study on Scholastic’s migration to Redshift. Scholastic is one of the United States’ oldest and largest publishing companies, previously, they were running their business with an outdated, on-promise, costly data warehouse solution that wouldn’t withstand their needs. When they made the decision to switch to Redshift, they quickly achieved quicker results at a dramatically lesser cost. This session will cover exactly how Scholastic achieved this and the lessons they learned along the way. Learn more

4. Workshop: Building Your First Big Data Application with AWS

Of course, re:Invent would be pointless if we weren’t able to learn directly from the Amazon experts. In this workshop, you learn how to build big data application in with Amazon Redshift, EMR, Kinesis, S3, and several other services. AWS experts will also be reviewing architecture design patterns, and you will receive exclusive access to a take-home lab so that you can customize the application for your organization yourself. Learn more

5: Leveraging Amazon Machine Learning (ML), Amazon Redshift, and an Amazon Simple Storage Service Data Lake for Strategic Advantage in Real Estate

Again, we have a deep appreciation for real-life stories that focus on the business value of data and analytics. This is why we recommend this session on The Howard Hughes Corporation that partnered with 47Lining to create an enterprise data lake on Amazon S3. The session will be reviewing their reasons for changing their business process and how they managed to identify and qualify leads based on the results from data-driven analytics. Howard Hughes managed to increase the number of identified qualified leads in their pipeline by over 400% and reduced the acquisition cost per lead by more than 10 times. In this session, you will review step-by-step how they used Amazon ML to get better business results, how to create a data lake with Amazon S3, and how to manage Amazon ML to ensure that it makes accurate predictions. Learn more

Visit Us

Want to learn more? Be sure to stop by our booth (#2821) and hear about our experience using AWS and Redshift to run our data management solution.

Panoply.io in AWS ReInvent Coonference 2016 at booth #2021

Want to setup a meeting? Just schedule the most convenient time for you:

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Furthermore, here at Panoply, we’re ready to join the excitement and plan to bring lots of gifts to our fellow AWS fans. We invite you to our booth to participate in a short AWS Redshift survey. After taking the quick survey on one of our iPads, you will automatically be entered into a raffle to win the latest virtual reality goggles! If you scratch that card and see that you’re the winner, you’ll be handed your VR goggles on the spot.

Can’t wait to see you there!

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