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Data Engineering Architecture Big Data
· 3 min read

Building a Modern Data Lakehouse Architecture

Combine the best of data lakes and warehouses with lakehouse architecture for flexible, scalable analytics.

The lakehouse paradigm is transforming how organizations handle data. Here’s my take on building one:

What is a Lakehouse?

A lakehouse combines:

  • Data Lake flexibility: Store any data format
  • Data Warehouse governance: ACID transactions, schema enforcement
  • BI and ML support: Single platform for all analytics

Key Components

  1. Storage Layer: S3, ADLS, GCS
  2. Table Format: Delta Lake, Iceberg, Hudi
  3. Compute Engine: Spark, Trino, StarRocks
  4. Governance: Unity Catalog, DataHub

Implementation Tips

  • Start with open table formats
  • Implement data quality checks early
  • Plan for incremental processing
  • Design for multi-workload support

The lakehouse approach has simplified our data architecture significantly while reducing costs.

Data Engineering Architecture Big Data
MD Furkanul Islam

MD Furkanul Islam

Data Engineer & AI/ML Specialist

9+ years building intelligent data systems at scale. Passionate about bridging the gap between data engineering, AI, and robotics.

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