Bringing Apache Pinot's Query Efficiency to the Data Lake (July 23rd)
Thursday, July 23rd, 2026: 8:00 AM to 9:00 AM
This session looks at a different model: bringing Apache Pinot's query efficiency to data lake workloads. Instead of reading broad chunks of data to answer selective questions, index-driven execution uses metadata and indexes to narrow data access before unnecessary Parquet data is read - including on open lakehouse tables such as Apache Iceberg and Delta Lake.
Virtual
The era of brute-force analytics is over. Bringing Apache Pinot’s query efficiency to the data lake means changing how analytical queries access data, not just where that data is stored.
Open table formats changed the lakehouse by making storage more flexible and open. But they did not fully fix query economics. For many teams, brute-force analytics is still the default: engines fetch far more data than the question actually requires.
Partition pruning, predicate pushdown, and metadata filtering help, but they often stop at the file or row-group level. What remains still includes unnecessary data access. At lakehouse scale, that extra I/O turns into higher CPU usage, more network movement, and inflated infrastructure spend.
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