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Azure datalake and consistent data

azure_learner 570 Reputation points
2025-03-23T11:14:59.57+00:00

Hi Friends, I am a bit perplexed by some of the datalake aspects which are :

  1. We have identified the large number of data sets that are of low to medium data range, the reason being optimizing transaction access patterns (fewer datasets the less I/O (read writes), and we intend to do these merge in the Silver layer( as we do not have a dynamic data such sales as data with daily exchange rates.).Please confirm whether this approach is on the right path. 2.  Since datalake is a file-based mechanism and traditional data warehousing kind of integrity 
       relationships wouldn't be possible, do we still consider the common key concept?
    1. If the answer to point 2 is negative, how can this merge data activity be implemented? What are the other ways we can implement merging data?
    2. The dataset we have identified has a common key column, but when we merge the data by using databricks on these columns one of them dynamically assigned a random name during the execution as both key columns have the same name, hence we intend to define a new column as per business input. Is this correct?
  2. What other key considerations should be taken into account so that performance and scalability remains consistently high? Our goal is Datalakehouse at gold layer and datawarehousing solution with Synapse.
  3. Please also shed light on foreseeable challenges that I should be aware of , and how to thread along those  challenges?

Your help will be highly appreciated. Thank you in advance.

Azure Data Lake Analytics
Accepted answer
  1. Vinodh247 33,076 Reputation points MVP Moderator
    2025-03-23T16:23:02.66+00:00

    Hi ,

    Thanks for reaching out to Microsoft Q&A.

    1. Merging data in Silver Layer based on access patterns

    Yes, your approach is on the right path. Silver Layer in the medallion architecture is typically where data is cleansed, conformed, and joined. If you have low-to-medium volume datasets, merging them to optimize access patterns is beneficial.

    • Fewer files means fewer metadata operations and I/O reads, which can improve performance.
    • However, ensure that you do not merge indiscriminately do it based on query patterns, update frequency, and use cases.
    • Since your data is not highly dynamic (no transactional sales data), doing merges in batch makes sense.
    1. Relationships in Data Lake and use of common keys

    You are right. File-based systems do not enforce referential integrity like traditional databases. However, common keys are still extremely important.

    • They enable joins across datasets during processing (with Spark or SQL engines like Synapse or Databricks SQL).

    Even though there is no RDBMS-style enforcement, logical integrity must be maintained through your pipeline design and data validation.

    So, the answer is yes. You must consider and maintain common keys in a lakehouse model.

    1. Key collision and merge logic

    When joining two datasets in Spark with the same column names, Spark will auto-rename one of the columns (key becomes key_1). This is expected.

    What you are planning to rename one of the keys explicitly or define a new business-defined key is correct and recommended.

    This avoids confusion and ensures clarity in downstream processing.

    Also consider using aliasing or prefixing during joins to clearly distinguish source columns.

    1. Key considerations for performance and scalability

    To ensure consistent high performance:

    • Partitioning: Use appropriate partition columns (date, region) to prune data during queries.

    File size tuning: Avoid too many small files. Aim for 100 MB to 1 GB Parquet files.

    Compaction strategy: Periodically compact files especially in Silver and Gold layers.

    Z-Ordering (Databricks): Helps with data skipping for faster reads on frequently filtered columns.

    Schema evolution: Manage schema versions cleanly; avoid hard-breaking changes.

    1. Lakehouse in Gold + Synapse for Data Warehousing

    This is a valid hybrid architecture.

    Use the Gold layer to store aggregated, business-ready data.

    • Then use Synapse Serverless or Dedicated SQL Pools to query the gold data.
    • Consider Synapse views on top of Delta/Parquet files if you want near real-time querying without movement.

    To sum up:

    • Yes, your plan is directionally correct.
    • Maintain logical key relationships; enforce them through design.
    • Resolve column name collisions via renaming or aliasing.
    • Use partitioning, compaction, and Z-ordering for performance.
    • Plan metadata and schema evolution early.
    • Keep an eye on governance and cost control.

    Please feel free to click the 'Upvote' (Thumbs-up) button and 'Accept as Answer'. This helps the community by allowing others with similar queries to easily find the solution.

    1 person found this answer helpful.

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