Speed Up Your Data Lake: How-to Supercharge Apache Spark

Join us: Thursday, 22nd February 2024 – 2:00 PM IST | 2:00 PM CET | 2:00 PM EST

Stop getting stuck in the data slow-lane: learn how to supercharge Apache Spark! Dive into our webinar ” Speed Up Your Data Lake: How-to Supercharge Apache Spark ” and discover how to supercharge your big data operations with Spark 3.3’s significant performance upgrades – enabling you to do more, in less time.

What You’ll Discover:

Revolutionary Performance Enhancements in Spark 3.3

  • Learn about the transformative Bloom Filter Joins that offer up to 10x speedup in join query performance, and how it’s changing the game in data processing efficiency.

Query Execution and Data Handling Breakthroughs

  • Get insights on the latest query execution enhancements, including significant speedups in full outer sort merge join & shuffled hash join and see how these improvements streamline your data workflows.

Pioneering Improvements in Data Types and Pandas API

  • Explore the remarkable advancements in handling complex data types and the Pandas API, leading to substantial performance improvements and faster data scanning.

Real-World Applications and Demos on Qubole

  • Witness live demonstrations showing how these enhancements translate to real-world scenarios, particularly focusing on Spark 3.3’s scalability and speed within the Qubole environment.

Don’t let this opportunity to turbocharge your data analytics capabilities pass you by. Register now and discover how to navigate the fast lane of big data with Spark 3.3.

Can’t attend the live session? No problem.

Register now and after the webinar, you will get the recording sent directly to your inbox.


Nagesh Reddy

Nagesh is Senior Solution Architect at Qubole and has helped build highly successful big data solutions in several industry verticals ranging from Telecom, Financials, and IoT. His experience spans early-stage startups, pre-IPO companies to big enterprises. Nagesh has a bachelor’s in Engg from the University of Pune.