Data Systems
Data Lake Architecture
Data lakes store raw data at any scale for diverse analytics. Master the architecture of modern data platforms: storage layers, file formats, schema evolution, and the emerging lakehouse paradigm.
- Raw Storage β Store data as-is, schema-on-read
- Scalability β Petabytes of data on cheap object storage
- Flexibility β Support for structured, semi-structured, and unstructured data
Data lakes turn data hoarding into data-driven decisions.
Data Lake Fundamentals
Data Lake vs Data Warehouse vs Lakehouse
Storage Layers
| Layer | Purpose | Technologies |
|---|---|---|
| Raw/Bronze | Original data, immutable | S3, ADLS, GCS |
| Cleansed/Silver | Validated, deduplicated | Delta Lake, Iceberg |
| Curated/Gold | Aggregated, business-ready | Materialized views |
File Formats
| Format | Compression | Schema Evolution | Splittable | Use Case |
|---|---|---|---|---|
| CSV | Low | No | Yes | Simple data exchange |
| JSON | Moderate | Yes | No | Semi-structured data |
| Parquet | High | Limited | Yes | Analytics (columnar) |
| ORC | High | Yes | Yes | Hive ecosystem |
| Avro | High | Yes | Yes | Row-based, streaming |
The Modern Data Stack
Data Governance
| Aspect | Description |
|---|---|
| Cataloging | Track what data exists and where |
| Lineage | Track data transformations and provenance |
| Quality | Validate data meets quality standards |
| Access Control | Restrict access based on roles |
| Compliance | GDPR, CCPA, HIPAA requirements |
Practice Exercises
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Architecture Design: Design a data lake architecture for an e-commerce company that ingests data from PostgreSQL, Kafka, and third-party APIs. What storage formats would you use at each layer?
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Format Selection: Compare Parquet and ORC for a Spark-based analytics workload. What are the trade-offs in terms of compression, schema evolution, and ecosystem support?
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Governance Plan: Design a data governance plan for a data lake containing PII (personally identifiable information). What cataloging, access control, and compliance measures would you implement?
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Cost Estimation: Estimate the monthly storage cost for a data lake with 100TB of raw data, 20TB of processed data, and 5TB of materialized views on AWS S3.
What to Learn Next
-> Batch Processing MapReduce, Spark, and distributed batch processing.
-> Stream Processing Real-time data processing with Flink, Spark Streaming, and Kafka Streams.
-> Kafka Deep Dive Event streaming, partitioning, and exactly-once semantics.
-> Choosing the Right Database Systematic framework for database selection.
-> Event-Driven Architecture Event sourcing, CQRS, and message-driven systems.
-> Observability Logging, metrics, tracing, and monitoring distributed systems.