Datalake data

Hi azure_learner,

Azure Data Lake Storage (ADLS) does not perform data compression automatically. Instead, it allows you to incorporate compression strategies as part of your data ingestion and processing workflows.

For managing large volumes of data in ADLS, you can apply popular compression algorithms like GZIP or BZIP2 during the ingestion process. For instance, when uploading data, you can specify the compression type in your configuration. This ensures the data is compressed before being stored in ADLS and can be decompressed when reading it back.

Additionally, when working with structured data, you can utilize file formats like Parquet, which compresses and stores data in a columnar format. While ADLS itself does not handle compression directly, using file formats like Parquet in data pipelines allows you to optimize storage and improve analytical performance.

In specialized scenarios like healthcare data solutions within Microsoft Fabric, data stored in Delta tables is automatically compressed in a columnar format through Parquet files. This approach helps with space optimization and performance improvements during analysis.

To summarize, while ADLS does not have built-in data compression, you can implement various compression techniques during data ingestion and processing to manage large volumes of data effectively.

https://learn.microsoft.com/en-us/sql/relational-databases/data-compression/data-compression?view=sql-server-ver16
https://learn.microsoft.com/en-us/azure/architecture/data-guide/scenarios/data-lake#technology-choices

Hope the above suggestion helps! Please let us know do you have any further queries.

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Hi,

Thank you for reaching out with your question on the Q&A portal! I appreciate your curiosity about how data compression works in Data Lake.

In Azure Data Lake Storage (ADLS), data compression is handled automatically in certain scenarios, depending on the underlying storage layer and the services interacting with it. For example, when using services like Azure Synapse Analytics or Azure Databricks, you can leverage built-in compression options (e.g., Parquet/ORC formats) which apply efficient columnar compression. However, you can also manually configure compression settings based on your specific needs, such as choosing algorithms like GZIP, Snappy, or Zstandard for optimal performance.

For more details, I recommend checking out these Microsoft articles:

Best practices for using Azure Data Lake Storage

Let me know if you'd like further clarification I'm happy to help!

Best regards,

Alex

P.S. If my answer helped you, please Accept my answer.