Efficiently reading data lake files involves:

1. Choosing the Right Tools: Select tools optimized for data lake file formats (e.g., Parquet, ORC) and distributed computing frameworks (e.g., Apache Spark, Apache Flink).

2. Partitioning and Indexing: Partition data logically and create indexes to minimize data scanning.

3. Optimizing Queries: Write queries that leverage predicate pushdown, column pruning, and other optimizations provided by the data lake engine.

4. Parallel Processing: Utilize parallel processing to distribute workload across multiple nodes or cores, improving read performance.

5. Caching and Materialization: Cache frequently accessed data or precompute aggregates to reduce read times for subsequent queries.

Krunal Medapara,

CTO

NewEvol

https://www.newevol.io/product/data-lake-solutions.php