QN : Get started with lakehouses in Microsoft Fabric

# architecture# dataengineering# microsoft# tutorial
QN : Get started with lakehouses in Microsoft FabricPaulet Wairagu

A lakehouse is a unified platform that combines: The flexible and scalable storage of a data...

  • A lakehouse is a unified platform that combines:
    • The flexible and scalable storage of a data lake
    • The ability to query and analyze data of a data ware*house*
  • A lakehouse uses Apache Spark and SQL compute engines to process and analyze data at scale.
  • Traditional Warehouses handle structured data but struggle on semi-structured and unstructured data from app logs , IoT devices etc hence data silos and complex integration efforts
  • Data Lakes offer flexibility and scalability but lack structure and performance for b/s analytics
  • Data Warehouses have strong analytical capabilities but struggle with different data formats and costly to scale.
  • Lakehouse design:
    • tables : delta lake table that provide structured, queryable data
      • Support SQL queries through the SQL analytics endpoint
      • Enforce schemas and support ACID transactions
      • Can be accessed in Power BI for reporting
      • Benefit from automatic optimization and maintenance
    • files : stores raw or semi-structured data files in their native format
      • Support any file format (CSV, JSON, Parquet, images, documents)
      • Provide flexibility for data exploration and processing
      • Can be staged before transformation into tables
      • Don't enforce schema or support direct SQL queries
  • Delta Lake is a open source storage layer taht brings reliability to data lakes.
  • Data is stored in delta format in OneLake storage
  • Delta Lake advanatges
    • ACID Transactions : consistency with frequent reads
    • Schema enforcement : validates the data against the table schema
    • Time Travel : maintains transaction logs
    • Updates and Deletes :
  • Delta table has parquet data files + transaction logs
  • This design support batch + straeming workloads
  • Lakehouse access :
    • workspace roles for collaborators who need access to all items in the workspace
    • Item-level sharing to grant read-only access for specific needs, such as analytics or Power BI report development
    • SQL analytics endpoint supports row-level and column-level security, so you can restrict what specific users see when they query through SQL
    • schema-level permissions to control access by business domain
  • Well-organized lakehouse data becomes the foundation that intelligent experiences across Microsoft Fabric depend on.
  • investment you make in organizing, naming, and structuring lakehouse data pays dividends beyond your immediate analytics needs. Good data engineering practices in the lakehouse create a reusable foundation for intelligent experiences across the platform.