Best practices for optimizing your big data costs with Amazon EMR

As data volumes increase, so do the costs of processing it. We’ll review several best practices and new features that enable you to cut operating costs and create efficiencies when processing vast amounts of data using Amazon EMR. Session attendees will be able to walk away with a solid understanding of Managed Scaling, improving Apache Spark performance to help lower their Amazon EMR costs, and monitoring, tuning, and troubleshooting solutions for big data workloads on Amazon EMR.


Angelo Carvalho, Principal Solutions Architect @ AWS

I'm passionate about working with the latest technology and designing cost-effective, highly available, and highly scalable solutions. I admire the pace of innovation of cloud computing and how it is transforming the information technology market. My goal is to promote the use of such technologies and help other companies to put aside the past and embrace the future.

What is the cost to attend and watch the virtual sessions?

Data Team Summit is always free and open for all to attend.

What is Data Teams Summit?

This year, we've taken the peer-to-peer empowerment of data teams one step further and formally transformed DataOps Unleashed into Data Teams Summit to better reflect our focus on the teams and individuals running, managing, and monitoring data pipelines.

Data Teams Summit is an annual, full-day virtual conference, led by data rockstars at future-forward organizations about how they're establishing predictability, increasing reliability, and creating economic efficiencies with their data pipelines.

We'll be back again in 2024 and look forward to another round of data team focused conversations.

Who comes to Data Teams Summit?

Data professionals and experts including data engineers, administrators, architects, analysts, AI/ML professionals, and relevant data technology leadership.

Join us for sessions on:

  • Data teams & best practices
  • Data pipelines & applications
  • DataOps observability
  • Data quality & data governance
  • Operations observability
  • MLOps
  • Data modernization & architecture
  • Biz/FinOps observability

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