https://community.databricks.com/t5/data-engineering/how-to-efficiently-read-the-data-lake-files-metadata/m-p/73733#M34657 <P>Efficiently reading data lake files involves:</P><OL><LI><P><STRONG>Choosing the Right Tools</STRONG>: Select tools optimized for data lake file formats (e.g., Parquet, ORC) and distributed computing frameworks (e.g., Apache Spark, Apache Flink).</P></LI><LI><P><STRONG>Partitioning and Indexing</STRONG>: Partition data logically and create indexes to minimize data scanning.</P></LI><LI><P><STRONG>Optimizing Queries</STRONG>: Write queries that leverage predicate pushdown, column pruning, and other optimizations provided by the data lake engine.</P></LI><LI><P><STRONG>Parallel Processing</STRONG>: Utilize parallel processing to distribute workload across multiple nodes or cores, improving read performance.</P></LI><LI><P><STRONG>Caching and Materialization</STRONG>: Cache frequently accessed data or precompute aggregates to reduce read times for subsequent queries.</P></LI></OL><P>Krunal Medapara,</P><P>CTO</P><P>NewEvol</P><P><A href="https://www.newevol.io/product/data-lake-solutions.php" target="_self">https://www.newevol.io/product/data-lake-solutions.php</A></P> Thu, 13 Jun 2024 07:17:33 GMT KrunalMedapara 2024-06-13T07:17:33Z https://community.databricks.com/t5/data-engineering/how-to-efficiently-read-the-data-lake-files-metadata/m-p/20651#M13963 <P><B>I want to read the last modified datetime of the files in data lake in a databricks script. If I could read it efficiently as a column when reading data from data lake, it would be perfect.</B></P><P><B>Thank you:)</B></P> Thu, 24 Jun 2021 15:17:28 GMT https://community.databricks.com/t5/data-engineering/how-to-efficiently-read-the-data-lake-files-metadata/m-p/20651#M13963 User16790091296 2021-06-24T15:17:28Z https://community.databricks.com/t5/data-engineering/how-to-efficiently-read-the-data-lake-files-metadata/m-p/73733#M34657 <P>Efficiently reading data lake files involves:</P><OL><LI><P><STRONG>Choosing the Right Tools</STRONG>: Select tools optimized for data lake file formats (e.g., Parquet, ORC) and distributed computing frameworks (e.g., Apache Spark, Apache Flink).</P></LI><LI><P><STRONG>Partitioning and Indexing</STRONG>: Partition data logically and create indexes to minimize data scanning.</P></LI><LI><P><STRONG>Optimizing Queries</STRONG>: Write queries that leverage predicate pushdown, column pruning, and other optimizations provided by the data lake engine.</P></LI><LI><P><STRONG>Parallel Processing</STRONG>: Utilize parallel processing to distribute workload across multiple nodes or cores, improving read performance.</P></LI><LI><P><STRONG>Caching and Materialization</STRONG>: Cache frequently accessed data or precompute aggregates to reduce read times for subsequent queries.</P></LI></OL><P>Krunal Medapara,</P><P>CTO</P><P>NewEvol</P><P><A href="https://www.newevol.io/product/data-lake-solutions.php" target="_self">https://www.newevol.io/product/data-lake-solutions.php</A></P> Thu, 13 Jun 2024 07:17:33 GMT https://community.databricks.com/t5/data-engineering/how-to-efficiently-read-the-data-lake-files-metadata/m-p/73733#M34657 KrunalMedapara 2024-06-13T07:17:33Z