Data warehouse vs data lake in HLD - Scaling Approaches Compared

Scalability Analysis - Data warehouse vs data lake

Growth Table: Data Warehouse vs Data Lake

Scale	Data Warehouse	Data Lake
100 users	Small structured data, simple queries, single server	Small raw data, flexible formats, single storage system
10K users	Moderate data volume, need for query optimization, start using read replicas	Growing raw data, need for metadata catalog, start partitioning data
1M users	Large structured data, heavy query load, scale out with clusters, use caching	Massive raw and semi-structured data, complex metadata, distributed storage and compute
100M users	Very large data, multi-region clusters, advanced indexing, data sharding	Exabyte scale data, multi-cloud or hybrid storage, automated data lifecycle management

First Bottleneck

For a data warehouse, the first bottleneck is the database query engine under heavy concurrent analytical queries, as it struggles with CPU and memory limits on complex joins and aggregations.

For a data lake, the first bottleneck is the metadata management and data indexing, since raw data is large and unstructured, making data discovery and efficient querying difficult.

Scaling Solutions

• Data Warehouse: Use horizontal scaling with distributed query engines (e.g., MPP clusters), add read replicas, implement caching layers, and shard data by key.
• Data Lake: Implement metadata catalogs (e.g., AWS Glue), partition data by time or category, use distributed storage (e.g., HDFS, S3), and separate storage from compute with scalable engines (e.g., Presto, Spark).

Back-of-Envelope Cost Analysis

Assuming 1M users generating 10 queries/day:

• Queries per second (QPS): ~115 (1,000,000 * 10 / 86400 seconds)
• Data Warehouse storage: 100 TB structured data, requires fast SSD storage and high CPU for query processing.
• Data Lake storage: 1 PB raw data, cheaper object storage but higher cost in compute for data processing.
• Network bandwidth: Data lake needs high bandwidth for data ingestion and processing; data warehouse needs bandwidth for query results and replication.

Interview Tip

Start by defining the use case: structured vs raw data, query patterns, and latency needs. Then discuss scaling challenges for storage, compute, and metadata. Finally, propose solutions like distributed systems, caching, and data partitioning, explaining trade-offs clearly.

Self Check

Your data warehouse database handles 1000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Add read replicas and implement query caching to distribute load and reduce CPU bottleneck before scaling hardware or sharding data.

Key Result

Data warehouses first hit CPU and memory limits on complex queries as users grow, solved by distributed query engines and caching; data lakes first struggle with metadata management and data indexing due to raw unstructured data, solved by metadata catalogs and separating storage from compute.