QN : Get started with lakehouses in Microsoft Fabric

• 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.