Data warehouse vs data lake in HLD - Architecture Patterns Compared

System Overview - Data warehouse vs data lake

This system compares two popular data storage solutions: data warehouses and data lakes. The goal is to understand their architecture, how data flows through them, and their key differences in handling data for analytics and business intelligence.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +---------------------+---------------------+
  |                     |                     |
Data Warehouse       Data Lake             Metadata Store
  |                     |                     |
  v                     v                     v
Relational DB        Object Storage       Catalog DB
  |                     |                     |
  +---------------------+---------------------+
                        |
                        v
                    Analytics Engine
                        |
                        v
                    Visualization

Components

User

user

End user or analyst requesting data or reports

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Routes requests to either Data Warehouse or Data Lake services

Data Warehouse

service

Structured storage optimized for fast SQL queries and business reporting

Data Lake

service

Stores raw, unstructured or semi-structured data in large volumes

Metadata Store

service

Manages metadata and schema information for data lake contents

Relational DB

database

Stores cleaned, structured data in tables for the data warehouse

Object Storage

storage

Stores raw files like logs, images, and JSON in the data lake

Catalog DB

database

Stores metadata and schema details for data lake files

Analytics Engine

service

Processes data from warehouse or lake for analysis and machine learning

Visualization

service

Generates reports and dashboards for users

Request Flow - 14 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Data Warehouse

Data Warehouse → Relational DB

Relational DB → Data Warehouse

Data Warehouse → API Gateway

API Gateway → Visualization

Visualization → User

API Gateway → Data Lake

Data Lake → Metadata Store

Data Lake → Object Storage

Data Lake → API Gateway

API Gateway → Analytics Engine

Analytics Engine → Visualization

Failure Scenario

Component Fails:Relational DB

Impact:Data Warehouse queries fail, reports cannot be generated from structured data

Mitigation:Use database replication and failover to standby DB; cache recent query results for read availability

Architecture Quiz - 3 Questions

Which component stores raw, unstructured data in this architecture?

A Relational DB

B Object Storage

C Metadata Store

D Data Warehouse

Design Principle

This architecture highlights the difference between structured data storage optimized for fast queries (Data Warehouse) and flexible storage for raw data (Data Lake). It uses an API Gateway to route requests appropriately and metadata management to handle schema in the data lake, enabling scalable and efficient analytics.

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