Modern data teams need speed, scale, and flexibility. A data lakehouse blends the cost-efficiency of a data lake with the performance and structure of a data warehouse. At Essid Solutions, we design and implement lakehouse architectures that deliver real-time insights and future-proof your data stack.

🌎 What Is a Data Lakehouse?

A data lakehouse is a unified architecture that:

• Stores raw and structured data in cheap object storage (like a data lake)
• Allows for SQL querying and data management (like a warehouse)
• Supports both BI dashboards and ML workloads from a single platform

It solves the historic problem of maintaining separate pipelines and storage for analytics and machine learning.

⚖️ Key Components of a Lakehouse

1. Data Storage – S3, Azure Data Lake, GCS
2. Table Format & Metadata – Delta Lake, Apache Iceberg, Hudi
3. Query Engines – Databricks, Snowflake, Dremio, Trino, Athena
4. Data Transformation – dbt, Apache Spark
5. Orchestration – Apache Airflow, Dagster
6. Access Control & Governance – Unity Catalog, Lake Formation

🏗️ Sample Architecture

[ Ingestion: APIs, Kafka, Batch CSVs ]
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[ Data Lake: S3 / GCS / ADLS ]  <--- Delta Lake / Iceberg format
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[ Query Layer: Databricks / Snowflake / Athena ]
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[ BI Tools: Power BI, Metabase, Superset, Looker ]

Optional: ML tools like SageMaker or Vertex AI can plug in directly to the lakehouse.

💼 Use Case: Retail Company Scaling from 1M to 100M Records

A client storing analytics in PostgreSQL was hitting performance issues. We:

• Moved raw and historical data to S3 with Delta Lake
• Added a dbt layer to clean and model data
• Enabled dashboards in Superset and Metabase

Result: Query times dropped from 45s to under 2s. Monthly cloud cost reduced by 40%.

📅 Ready to Build Your Lakehouse?

We help teams design scalable, modular lakehouse architectures using open standards and proven tools.

👉 Book a lakehouse planning session
Or email: hi@essidsolutions.com