What is the data lake and data warehouse | Key difference between data lake and data warehouse

November 3, 2024November 3, 2024 by Insightoriel

There are few key terms and concepts in data analytics which are really crucial to understand. The concept of data analysis and data lakes are among those important concept.

Here’s a detailed explanation of data lakes and data warehouses, including examples:

Table of Contents

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Data Lake

A data lake is a centralized repository that stores raw, unprocessed data in its native format. It’s designed to handle large volumes of data from various sources, such as:

• Social media
• IoT devices
• Sensors
• Logs
• Transactional data

Data lakes are often characterized by:

• Schema-on-read (no predefined schema)
• Flat architecture (no hierarchical structure)

Scalability (handle large data volumes)

Flexibility (store diverse data formats)

Data Warehouse

A data warehouse is a structured repository that stores processed data in a transformed format. It’s designed for querying and analyzing data to support business decision-making.

Data warehouses are often characterized by:

• Schema-on-write (predefined schema)
• Hierarchical architecture (star or snowflake schema)
• Optimized for querying and analysis
• Data governance and quality control

Key Difference

Will discuss 6 key difference between data lake and data warehouse.

Data Lake || Data Warehouse

1. Schema: Schema-on-read || Schema-on-write

2. Data Format: Raw and unprocessed || Processed transformed

3. Architecture: Flat || Hierarchical

4. Purpose: Storage and exploration || Querying and analysis

5. Data Volume: Large, scalable || Smaller, curated

6. Data Variety: High (diverse formats) || Lower (standardized formats)

Example

Will use the example of a Retail Company.

Suppose a retail company wants to analyze customer purchasing behavior.

Data Lake:

• Collects raw data from:
  • Sales transactions
  • Customer feedback forms
  • Social media
  • Website logs
• Stores data in its native format (e.g., JSON, CSV)
• Data lake contains:

File Format Size sales_data.csv | CSV 10 GB customer_feedback.json | JSON 5 GB social_media_logs.txt | Text 2 GB

Data Warehouse:

• Processes and transforms data into:
  • Customer demographics
  • Sales trends
  • Product categories
• Stores data in a structured format (e.g., relational database)
• Data warehouse contains:

Table Columns Rows customers id, name, email, age 100,000 sales id, customer_id, product_id, date 500,000 products id, name, category 10,000

Use cases

• Data Lake:
  • Data exploration and discovery
  • Machine learning model training
  • Data archiving and compliance
• Data Warehouse:
  • Business intelligence and reporting
  • Data analysis and visualization
  • Strategic decision-making

Tools and Technologies

• Data Lake:
  • Hadoop
  • Amazon S3
  • Azure Data Lake Storage
  • Google Cloud Storage
• Data Warehouse:
  • Amazon Redshift
  • Google BigQuery
  • Microsoft Azure Synapse Analytics
  • Oracle Exadata

In summary, data lakes and data warehouses serve different purposes and offer unique benefits. Data lakes provide a flexible, scalable storage solution for raw data, while data warehouses offer a structured, optimized environment for querying and analyzing processed data.

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