The article discusses the integration of ClickHouse with the Parquet file format, emphasizing how this combination enhances the efficiency of lakehouse analytics. It highlights the performance benefits and the ability to handle large-scale data analytics seamlessly, making it a strong foundation for modern data architectures.

User-defined indexes can be embedded within Apache Parquet files, enhancing query performance without compatibility issues. By utilizing existing footer metadata and offset addressing, developers can create custom indexes, such as distinct value indexes, to improve data pruning efficiency, particularly for columns with limited distinct values. The article provides a practical example of implementing such an index using Apache DataFusion.

Apache Parquet has long been the standard for analytical data storage, but modern workloads, particularly in AI and machine learning, highlight its limitations in random access and performance. As a result, new file formats like BtrBlocks, FastLanes, Lance, and Nimble are emerging, each designed to optimize for specific use cases and hardware architectures, offering faster decompression and improved efficiency. These innovations reflect a shift towards more dynamic data access needs that Parquet was not originally built to address.