Your Data Lake
with Spark 3.3

Qubole’s multi-engine data lake fuses ease of use with cost-savings. 
Now powered by Spark 3.3, it’s faster and more scalable than ever.

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You’re handling massive datasets and can’t afford the time or cost of processing slowdowns. So, how can you ensure your business can deliver results without breaking the bank?

Try switching to Spark 3.3 on Qubole’s Open Source Data Lake. With new features like Bloom Filter Joins and AQE improvements, Spark 3.3 on Qubole is significantly faster and more scalable than its predecessors, with the stability needed for modern workloads.

Unlock insights & improved user experience with Spark 3.3 and Jupyter Notebook on Qubole. Harness the synergistic power of Spark 3.3 and Jupyter Notebooks. When integrated, they are an ideal match for AI/ML applications that work harder than the sum of their parts for enhanced data processing capabilities and interactive analytics your project needs.

Goodbye, Performance Issues.

Save 42% on data lake costs with Spark 3.3 on Qubole*

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Apache Spark 3.3 Benefits


Advanced cost controls result in up to a 42% reduction in costs with Qubole


Performance optimizations and smart management tools that increase Spark efficiency

New Features

Performance optimizations and smart management tools that increase Spark efficiency


Expand your data projects scope more easily, and with fewer errors

Expertly Tested,


Your data environment needs safeguarding: anyone who has been burned before when implementing ‘the latest and greatest’ too quickly can attest to this.

That’s why we exhaustively test in the Qubole environment before launching new releases. You can start using Spark 3.3 confidently because we’ve looked for the issues and found solutions, meeting all GDPR and CCPA compliance requirements.

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Next-Gen Operations Whitepaper

AI Enablement with
Spark 3.3 on Qubole

Harmonized Technology

Spark 3.3 and Jupyter Notebooks unite to deliver accelerated insights and a seamless user experience in AI/ML workflows.

Streamlined Innovation

Spark 3.3 on Qubole simplifies the ML lifecycle, from data preprocessing to model deployment, enhancing productivity and innovation.

Future-Ready Framework

Anticipating the evolution of big data, Qubole is committed to scaling, cloud adaptability, & supporting the continuous growth of ML models.

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Spark 3.3 Technical Features

Filter Joins

Bloom filters greatly improve join query performance, by reducing data shuffle and computation needs for up to 10x faster join query performance.

Query Execution

Adaptive query execution improvements, such as optimizing one-row query plans and eliminating limits for faster query executions and more efficient data processing.

New Built-in

A new range of functions featuring linear regression, statistical/string processing and encryption functions. Enhanced security, compatibility, and API features.


Increased ANSI SQL compliance, support for new interval data types and implicit casting in ANSI mode make migration easier from traditional data warehouses.

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An open and secure multi-cloud data lake platform for machine learning, streaming analytics, data exploration and ad-hoc analytics.


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* Source: Qubole's Business Value Engineering Team Research, comparisons against Databricks & AWS EMR.