Abstract AlgorithmsAbstract Algorithms

All Posts

Data Warehouse vs Data Lake vs Data Lakehouse: Which One to Choose?

Snowflake, Databricks, or S3? We explain the evolution of data storage. Learn when to use a struc...

Abstract AlgorithmsAbstract Algorithms
Β·Β·5 min read
Share
Share on X / Twitter
Share on LinkedIn
Copy link

TLDR: Warehouse = structured, clean data for BI and SQL dashboards (Snowflake, BigQuery). Lake = raw, messy data for ML and data science (S3, HDFS). Lakehouse = open table formats (Delta Lake, Iceberg) that bring SQL performance to raw storage β€” the best of both.

πŸ“– Three Ways to Store Data at Scale: What Problem Each Solves

As organizations grow, they end up with data coming from many sources: transactional databases, clickstreams, IoT sensors, third-party APIs. Storing and querying all of it efficiently requires choosing the right abstraction.

QuestionWarehouseLakeLakehouse
What data format?Structured tables, SQLRaw files (JSON, Parquet, CSV)Open formats (Parquet + metadata layer)
Who uses it?Analysts, BI toolsData scientists, ML engineersBoth
Primary operationSQL aggregationsBatch ML training, explorationSQL + ML on same data
When to update data?ETL: clean before storingELT: store raw, transform laterACID on open files
Cost modelCompute + managed storageCheap object storageObject storage + optional compute

πŸ”’ The Data Warehouse: SQL on Clean, Structured Data

A warehouse ingests pre-processed, schema-validated data. The ETL pipeline (Extract, Transform, Load) cleans and normalizes data before it enters.

flowchart LR
    Source[Operational DBs\nAPIs / Events] --> ETL[ETL Pipeline\nTransform + Validate]
    ETL --> DW[Data Warehouse\nSnowflake / BigQuery\nRedshift]
    DW --> BI[BI Tools\nTableau / Looker]
    DW --> Analyst[SQL Analysts]

Strengths:

  • Sub-second query performance on aggregations over billions of rows.
  • Strong governance: schema enforcement, row-level security, audit trails.
  • Familiar SQL interface for business users.

Limitations:

  • Only stores structured, pre-defined schemas.
  • Poor fit for unstructured data (images, logs, text, audio).
  • Re-processing raw data means going back to source β€” the warehouse discards it.

βš™οΈ The Data Lake: Raw Files for Every Use Case

A lake stores everything in its raw form β€” no transformation required at write time. Data scientists can explore it, ML engineers can train on it, and analysts can query subsets of it.

flowchart LR
    Source2[Any source] --> Lake[Data Lake\nS3 / GCS / HDFS\nParquet, JSON, CSV, Images]
    Lake --> Spark[Spark Jobs\nML Training]
    Lake --> Notebook[Notebooks\nExploration]
    Lake --> DW2[Copy subsets\nto Warehouse]

Strengths:

  • Stores anything: structured, semi-structured, unstructured.
  • Cheap object storage β€” pennies per GB.
  • Supports large-scale ML training with frameworks like Spark, Ray, PyTorch.

Limitations:

  • "Data swamp" risk: without governance, data becomes untrustworthy and unmaintainable.
  • No ACID transactions β€” multiple writers can corrupt files.
  • Poor for interactive SQL queries (full file scans, no indexes).

🧠 The Data Lakehouse: Open Table Formats Bridge the Gap

The lakehouse uses open table formats (Delta Lake, Apache Iceberg, Apache Hudi) on top of cheap object storage to add the features a warehouse provides:

  • ACID transactions on Parquet files using a transaction log.
  • Schema enforcement and evolution.
  • Time travel β€” query data as it existed at any past timestamp.
  • Query optimization β€” data skipping, Z-ordering, compaction.
flowchart TB
    Raw[S3 / GCS\nParquet files] --> TableFmt[Delta Lake / Iceberg\nTransaction Log + Stats]
    TableFmt --> SparkSQL[Spark SQL\nML Pipelines]
    TableFmt --> Presto[Presto / Trino\nInteractive SQL]
    TableFmt --> BITool[BI Tools\nnative connector]

Used by Databricks (Delta Lake), Netflix (Iceberg), Uber (Hudi). You keep cheap storage but gain warehouse-quality query performance and data consistency.

βš–οΈ Decision Guide: Which One to Pick

If your team primarily needs…Choose
Fast SQL dashboards for business usersWarehouse (Snowflake, BigQuery)
ML model training on raw, diverse dataLake (S3 + Parquet)
Both SQL and ML on the same dataLakehouse (Delta Lake, Iceberg)
Quick start with minimal ops overheadWarehouse (fully managed)
Maximum flexibility and open standardsLakehouse
Smallest storage cost, OK with management complexityLake

In practice, many large organizations run all three: a warehouse for reporting, a lake for raw archival and ML, and a lakehouse for the intersection.

πŸ“Œ Key Takeaways

  • Warehouse: clean, structured, SQL, fast dashboards. Bad for unstructured or raw data.
  • Lake: raw, cheap, flexible. Good for ML. Bad for interactive SQL and governance.
  • Lakehouse: open table formats add ACID + SQL performance on top of cheap object storage.
  • ETL (warehouse) transforms before storing; ELT (lake/lakehouse) stores raw and transforms later.
  • Most serious data organizations run a hybrid: warehouse for BI, lake/lakehouse for ML.

🧩 Test Your Understanding

  1. Why does a data lake become a "data swamp" without governance?
  2. What specific property does Delta Lake add to raw Parquet files on S3?
  3. A data scientist needs to train a PyTorch model on the last 5 years of raw clickstream data. Which storage paradigm fits best?
  4. What is time travel in the context of a data lakehouse?

Tags

Abstract Algorithms

Written by

Abstract Algorithms

@abstractalgorithms

More Posts

System Design Service Discovery and Health Checks: Routing Traffic to Healthy Instances

TLDR: Service discovery is how clients find the right service instance at runtime, and health checks are how systems decide whether an instance should receive traffic. Together, they turn dynamic infrastructure from guesswork into deterministic routi...

β€’8 min read

System Design Roadmap: A Complete Learning Path from Basics to Advanced Architecture

TLDR: This roadmap organizes every system-design-tagged post in this repository into learning groups and a recommended order. It is designed for interview prep and practical architecture thinking, from fundamentals to scaling, reliability, and implem...

β€’10 min read

System Design Observability, SLOs, and Incident Response: Operating Systems You Can Trust

TLDR: Observability is how you understand system behavior from telemetry, SLOs are explicit reliability targets, and incident response is the execution model when those targets are at risk. Together, they convert operational chaos into measurable, re...

β€’8 min read

System Design Message Queues and Event-Driven Architecture: Building Reliable Asynchronous Systems

TLDR: Message queues and event-driven architecture let services communicate asynchronously, absorb bursty traffic, and isolate failures. The core design challenge is not adding a queue. It is defining delivery semantics, retry behavior, and idempoten...

β€’8 min read