Data warehouse vs data lake in HLD - Design Approaches Compared

Design: Data Storage and Analytics Platform

Design the architecture comparing data warehouse and data lake approaches for storing and analyzing enterprise data. Exclude detailed ETL pipeline design and specific vendor implementations.

Functional Requirements

FR1: Store large volumes of structured and unstructured data

FR2: Support fast querying and reporting for business intelligence

FR3: Allow flexible data ingestion from multiple sources

FR4: Enable data scientists to explore raw data for advanced analytics

FR5: Ensure data quality, security, and governance

FR6: Support historical data retention and versioning

Non-Functional Requirements

NFR1: Handle petabytes of data with scalable storage

NFR2: Query latency under 5 seconds for typical reports

NFR3: Availability of 99.9% uptime

NFR4: Cost-effective storage and compute resource usage

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Data ingestion pipelines

Storage layers (structured vs raw data)

Metadata and catalog services

Query engines and analytics tools

Data governance and security modules

Design Patterns

Lambda architecture for combining batch and real-time data

Schema-on-write vs schema-on-read

Data partitioning and indexing

Data lifecycle management

Reference Architecture

                +---------------------+                 
                |   Data Sources      |                 
                +----------+----------+                 
                           |                            
          +----------------+----------------+           
          |                                 |           
+---------v---------+             +---------v---------+ 
|   Data Lake       |             | Data Warehouse    | 
| (Raw, unstructured|             | (Structured,      | 
|  data storage)    |             |  cleaned data)    | 
+---------+---------+             +---------+---------+ 
          |                                 |           
          |                                 |           
+---------v---------+             +---------v---------+ 
| Metadata Catalog  |             | Metadata Catalog  | 
+---------+---------+             +---------+---------+ 
          |                                 |           
          +----------------+----------------+           
                           |                            
                +----------v----------+                 
                | Analytics & BI Tools |                 
                +---------------------+                 

Components

Data Sources

Various (databases, logs, IoT devices, APIs)

Provide raw data in different formats and structures

Data Lake Storage

Object storage (e.g., Amazon S3, Azure Blob Storage)

Store raw, unstructured, semi-structured, and structured data without predefined schema

Data Warehouse Storage

Columnar relational database (e.g., Snowflake, Redshift, BigQuery)

Store cleaned, structured data optimized for fast querying and reporting

Metadata Catalog

Data catalog tools (e.g., AWS Glue Data Catalog, Apache Hive Metastore)

Manage metadata, schemas, and data discovery for both lake and warehouse

Analytics & BI Tools

BI platforms (e.g., Tableau, Power BI), SQL query engines

Enable users to run queries, generate reports, and perform data analysis

Request Flow

1. 1. Data is ingested from various sources into the data lake in raw format.

2. 2. Data scientists explore raw data in the data lake for advanced analytics and machine learning.

3. 3. ETL/ELT processes clean and transform relevant data from the data lake into the data warehouse.

4. 4. Business users query the data warehouse for fast, reliable reports and dashboards.

5. 5. Metadata catalogs maintain schema and data lineage information for both storage layers.

6. 6. Governance and security policies are enforced across both data lake and warehouse.

Database Schema

Entities: DataSource (id, type, format), RawDataFile (id, source_id, path, ingestion_time), CleanedDataTable (id, schema, last_updated), MetadataCatalog (id, entity_type, schema, lineage), User (id, role, permissions). Relationships: DataSource 1:N RawDataFile, RawDataFile N:1 MetadataCatalog, CleanedDataTable N:1 MetadataCatalog, User N:N Permissions.

Scaling Discussion

Bottlenecks

Storage limits and costs as data volume grows

Query performance degradation with large datasets

Metadata management complexity with many data assets

Data ingestion throughput bottlenecks

Ensuring data consistency between lake and warehouse

Solutions

Use scalable object storage with lifecycle policies to archive old data

Implement partitioning, indexing, and caching in warehouse queries

Adopt automated metadata management and cataloging tools

Parallelize and batch data ingestion pipelines

Use incremental data processing and strong data validation in ETL

Interview Tips

Time: Spend 10 minutes understanding requirements and clarifying use cases, 20 minutes designing the architecture and explaining components, 10 minutes discussing scaling and trade-offs, 5 minutes summarizing.

Difference between schema-on-read (data lake) and schema-on-write (data warehouse)

Trade-offs between flexibility and query performance

Importance of metadata and governance in hybrid architectures

How data flows from raw ingestion to cleaned analytics-ready data

Scaling strategies for storage, compute, and metadata

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