Panel: A discussion on batch, streaming, and real-time data processing for ML

Eric Gonzalez, VP, Business Intelligence Architecture @ Eastern Bank

Vaishnavi Muraldihar, Data Engineer @ Intuit

Michael Del Balso, CEO & Co-Founder @ Tecton

This panel will explore critically different perspectives on batch, streaming, and real-time data processing, shedding light on common misconceptions, cost considerations, decision-making criteria, and the future of these architectures.

The panelists will discuss the most prevalent misconception in this domain, address the cost challenges of streaming architectures, and share insights on potential cost reduction strategies. They will also explore the decision-making criteria that guide organizations in choosing between these processing methods and discuss ways to make streaming data processing more accessible.

Furthermore, the advantages of streaming data for machine learning will be examined, emphasizing its potential to revolutionize real-time insights and predictive capabilities.

Where & when?

Open Source Data Summit 2024 will be held on October 2nd, 2024.

What is the cost of access to the live virtual sessions?

OSDS is always free and open for all to attend.

What is Open Source Data Summit?

OSDS is a peer-to-peer gathering of data industry professionals, experts, and enthusiasts to explore the dynamic landscape of open source data tools and storage.

The central theme of OSDS revolves around the advantages of open source data products and their pivotal role in modern data ecosystems.

OSDS is the annual peer hub for knowledge exchange that fosters a deeper understanding of open source options and their role in shaping the data-driven future.

Who attends OSDS?

OSDS is attended by data engineers, data architects, developers, DevOps practitioners and managers, and data leadership.

Anyone who is looking for enriched perspectives on open source data tools and practical insights to navigate the evolving data landscape should attend this event.

Join again on October 2nd, 2024 for discussions around:
  • Benefits of open source data tools
  • Cost/performance trade-offs
  • Building data storage solutions
  • Challenges surrounding open source data tool integration
  • Solutions for the cost of storing, accessing, and managing data
  • Data streams and ingestion
  • Hub-and-spoke data integration models
  • Choosing the right engine for your workload
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