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.

Practice

1. What is the main difference between a data warehouse and a data lake?

easy

A. Data warehouse and data lake are exactly the same.

B. Data warehouse stores raw data; data lake stores only images.

C. Data warehouse is for storing videos; data lake is for storing text.

D. Data warehouse stores structured data; data lake stores raw data.

Solution

1. Step 1: Understand data warehouse data type

   A data warehouse stores cleaned and structured data optimized for fast queries and reporting.

2. Step 2: Understand data lake data type

   A data lake stores raw, unprocessed data in various formats, allowing flexible analysis later.

3. Final Answer:

   Data warehouse stores structured data; data lake stores raw data. -> Option D

4. Quick Check:

   Data warehouse = structured, Data lake = raw [OK]

Hint: Data warehouse = clean data; data lake = raw data [OK]

Common Mistakes:

• Confusing raw data with structured data
• Thinking both store only structured data
• Assuming data lakes store only images or videos

2. Which of the following is a correct statement about data lakes?

easy

A. Data lakes only store data in relational tables.

B. Data lakes can store structured, semi-structured, and unstructured data.

C. Data lakes require data to be cleaned before storing.

D. Data lakes are only used for real-time data processing.

Solution

1. Step 1: Review data lake storage capabilities

   Data lakes store all types of data: structured, semi-structured (like JSON), and unstructured (like images).

2. Step 2: Check other options

   Data lakes only store data in relational tables. is false because data lakes do not require relational tables. Data lakes require data to be cleaned before storing. is false because data lakes store raw data without cleaning. Data lakes are only used for real-time data processing. is false as data lakes are not limited to real-time processing.

3. Final Answer:

   Data lakes can store structured, semi-structured, and unstructured data. -> Option B

4. Quick Check:

   Data lakes = all data types [OK]

Hint: Data lakes store all data types, not just tables [OK]

Common Mistakes:

• Thinking data lakes only store tables
• Assuming data must be cleaned before storing
• Believing data lakes are only for real-time data

3. Consider a company that wants to analyze customer behavior using both structured sales data and raw clickstream logs. Which architecture best fits this need?

medium

A. Store all data in a data warehouse only.

B. Store clickstream logs in a data warehouse and sales data in a data lake.

C. Store sales data in a data warehouse and clickstream logs in a data lake.

D. Store all data in a data lake only.

Solution

1. Step 1: Match data types to storage

   Structured sales data fits well in a data warehouse for fast reporting. Raw clickstream logs fit better in a data lake for flexible analysis.

2. Step 2: Evaluate options

   Store sales data in a data warehouse and clickstream logs in a data lake. correctly assigns sales data to warehouse and logs to lake. Options A and C use only one storage type, which is less efficient. Store clickstream logs in a data warehouse and sales data in a data lake. reverses the ideal storage.

3. Final Answer:

   Store sales data in a data warehouse and clickstream logs in a data lake. -> Option C

4. Quick Check:

   Warehouse = structured, Lake = raw logs [OK]

Hint: Structured data in warehouse, raw logs in lake [OK]

Common Mistakes:

• Putting all data in one storage type
• Mixing raw logs into warehouse
• Reversing data types and storage

4. A team designed a system storing all raw data in a data warehouse without cleaning it first. What is the main problem with this design?

medium

A. Data warehouses cannot store raw data efficiently.

B. Data warehouses automatically clean raw data, so no problem.

C. Data lakes require data to be cleaned before storing.

D. Raw data is faster to query in data warehouses.

Solution

1. Step 1: Understand data warehouse requirements

   Data warehouses expect cleaned, structured data for fast querying and reporting.

2. Step 2: Identify issue with raw data in warehouse

   Storing raw data in a warehouse causes inefficiency and poor query performance because it is not structured or cleaned.

3. Final Answer:

   Data warehouses cannot store raw data efficiently. -> Option A

4. Quick Check:

   Warehouse needs cleaned data [OK]

Hint: Raw data in warehouse causes inefficiency [OK]

Common Mistakes:

• Assuming warehouses clean data automatically
• Confusing data lake requirements
• Thinking raw data queries are faster in warehouses

5. A company wants to build a system that supports fast business reporting and also flexible data science experiments on raw data. Which design is best?

hard

A. Use a data warehouse for reporting and a data lake for raw data experiments.

B. Use only a data lake for all data storage and analysis.

C. Use only a data warehouse for all data storage and analysis.

D. Use a data lake for reporting and a data warehouse for raw data.

Solution

1. Step 1: Identify reporting needs

   Fast business reporting requires cleaned, structured data in a data warehouse.

2. Step 2: Identify data science needs

   Flexible experiments need raw, diverse data stored in a data lake.

3. Step 3: Combine storage types

   Using both a data warehouse for reporting and a data lake for raw data experiments meets both needs efficiently.

4. Final Answer:

   Use a data warehouse for reporting and a data lake for raw data experiments. -> Option A

5. Quick Check:

   Warehouse = reporting, Lake = experiments [OK]

Hint: Combine warehouse for reports and lake for raw data [OK]

Common Mistakes:

• Using only one storage type for all needs
• Reversing roles of warehouse and lake
• Ignoring data science flexibility requirements