Lake House - 1

 

Concept	Description
Lake House	✅ Architecture pattern that combines the benefits of a data lake (like Amazon S3) and a data warehouse (like Redshift). It provides a unified platform for analytics, machine learning, and reporting.
Lake Formation	❌ Not an architecture — it's an AWS service used to build and govern secure data lakes, often used as part of a Lake House implementation.

 

 

🔍 As an architecture pattern, Lake House aims to: 

• Store all data (structured, semi-structured, unstructured) in one place (usually object storage like S3) 

• Query it using different engines (Athena, Redshift Spectrum, EMR, Flink, SageMaker) 

• Enforce schema, governance, access controls 

• Enable advanced analytics and ML workflows 

 

✅ Key Characteristics of the Lake House Architecture Pattern: 

Feature	Description
Storage-first	Object storage (like S3) is the central source of truth
Open formats	Uses formats like Parquet, ORC, Iceberg for compatibility and performance
Multiple engines	Redshift, Athena, Spark, Flink, SageMaker access the same data
Schema evolution	Supports schema-on-read and schema enforcement as needed
Unified governance	Manages access control, lineage, audit (e.g., via AWS Lake Formation)

 

 

🔷 Why Lake House? 

Traditional data architectures have limitations: 

• Data warehouses are expensive and optimized for structured data. 

• Data lakes are cheaper and store all types of data, but lack strong governance, consistency, and performance for analytics. 

Lake House combines both worlds: 

• Central data store (data lake) in open formats like Apache Iceberg, Delta Lake, or Hudi 

• Federated queries and warehouse capabilities using tools like Amazon Redshift Spectrum, Athena, Presto, BigQuery, or Databricks 

 

🔷 Key Components 

Layer	Description	AWS Services
Data Storage	Store raw and curated data in open formats like Parquet, ORC, Iceberg, etc.	Amazon S3
Metadata Catalog	Central schema repository, schema evolution, partitioning	AWS Glue Data Catalog
Data Lake Governance	Secure, fine-grained access controls, row-level permissions	AWS Lake Formation
Data Processing	ETL/ELT, streaming, batch	AWS Glue, EMR (Spark, Flink), Lambda
Data Warehouse / Query Engines	Fast SQL access to data	Amazon Redshift, Athena, Redshift Spectrum
Machine Learning	Train and deploy ML models directly on lake data	Amazon SageMaker
BI & Visualization	Reports, dashboards, ad-hoc queries	Amazon QuickSight, Tableau, Power BI

🔷 Data Flow Example (Lake House Pipeline on AWS) 

       ┌──────────────────────────┐ 
        │  Raw Data (Logs, JSON)   │ 
        └──────────┬───────────────┘ 
                   ▼ 
           +------------------+ 
           |    Amazon S3     | ← Data Lake (Raw Zone) 
           +------------------+ 
                   ▼ 
          ┌─────────────────────┐ 
          │ Glue Crawler + ETL │ ← Structuring and Cleaning 
          └─────────────────────┘ 
                   ▼ 
           +------------------+ 
           |  S3 (Curated Zone)| 
           +------------------+ 
                   ▼ 
    ┌──────────────┬───────────────┐ 
    ▼              ▼               ▼ 
Athena      Redshift Spectrum     SageMaker 
(SQL Query)    (Warehouse Query)     (ML on S3) 
 
               ▼ 
         QuickSight (BI) 
 

Benefits of Lake House 

Benefit	Description
✅ Unified Data Platform	Use one storage layer (S3) for raw, curated, and analytics-ready data
✅ Open Table Formats	Work with Apache Iceberg, Delta Lake, etc., supporting ACID, schema evolution, time travel
✅ Cost-Efficient	Store once (on S3), query with different engines
✅ Scalable	Massive scalability with S3 and serverless tools like Athena
✅ Multi-purpose	Supports analytics, machine learning, real-time and batch pipelines
✅ Governance	Lake Formation provides

 

 

 

🔷 Open Table Formats in Lake House 

These are essential to make S3 act like a database: 

Format	Features
Apache Iceberg	Hidden partitioning, time travel, snapshot isolation, schema evolution
Delta Lake	ACID transactions, schema enforcement, time travel
Apache Hudi	Fast upserts, incremental queries, great for streaming data

AWS Athena, EMR, Redshift, and Flink now support Iceberg and Hudi. 

 

 

🔷 Example AWS Lake House Architecture 

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[IoT, App Logs, Files] 
       │ 
       ▼ 
+----------------------+ 
|     Amazon S3        | ← Raw Zone 
+----------------------+ 
       │ 
+----------------------+ 
| AWS Glue (ETL Jobs)  | 
| Glue Crawlers        | 
+----------------------+ 
       ▼ 
+----------------------+ 
| Amazon S3 (Curated)  | 
| Glue Catalog Tables  | 
+----------------------+ 
  │        │         │ 
  ▼        ▼         ▼ 
Athena   Redshift  SageMaker 
(SQL)    (DWH)     (ML) 
 
      ▼ 
 QuickSight BI 

 

✅ Summary 

Feature	Lake House Architecture
Combines	Data Lake + Data Warehouse
Storage	Amazon S3 (open formats: Iceberg, Delta)
Compute	Serverless (Athena), DWH (Redshift), ML (SageMaker)
Governance	Lake Formation
Query Engines	Athena, EMR, Redshift Spectrum
Benefits	Unified analytics, cost-efficient, scalable, secure