Understanding the Evolution from Data Warehouses to Data Lakehouses

Traditional Data Warehouses

A traditional data warehouse is a centralized repository that stores data from various sources, making it easily accessible for business analytics and reporting. Typically, this is done using a Relational Database Management System (RDBMS) like Oracle, where both data storage and compute resources are housed together, usually within an on-premises infrastructure.

One key point is that the data and processing occur on the same machine.

What is a Data Lakehouse and Why is it Gaining Popularity?

Before diving deeper into the benefits of a data lakehouse, it’s important to understand a few foundational concepts:

Data Lake

A Data Lake is a cloud-based storage solution designed to hold vast amounts of raw data in various formats such as CSV, JSON, XML, or Parquet. These data files are typically stored as objects within the cloud environment.

Benefits of a Data Lake:

• High Availability: Cloud storage solutions offer up to 99.9% availability.
• Cost-Effective: Cloud storage is generally cheaper than traditional on-premises solutions.
• Scalability: Storage can be expanded or reduced as needed, providing flexibility to meet demand.

Challenges of a Data Lake:

While data lakes offer significant advantages, they come with a few challenges, particularly related to the lack of metadata:

1. Lack of Metadata: Files stored in a data lake are saved as objects without associated metadata. However, metadata can be incredibly useful in several scenarios:

• Scenario 1: Counting the number of records in a file without reading the entire file.
• Scenario 2: Reading only specific columns in a large file, thus avoiding unnecessary data processing.
• Scenario 3: Skipping the reading of a file entirely if a certain value is known not to exist.
• Scenario 4: Restoring a previous version of a file that is overwritten daily.
• Scenario 5: Updating a single record in a CSV file without having to overwrite the entire file.

Delta Lake

Delta Lake is a technology that addresses the shortcomings of traditional data lakes by adding metadata management and enabling ACID (Atomicity, Consistency, Isolation, Durability) transactions. This combination of features effectively brings the raw storage capabilities of a data lake closer to the reliability and performance of a data warehouse.

DELTA LAKEHOUSE

Data Lakehouses is best of Data lakes and data warehouses :- Benefits from Data Lake & Data Lake ( which we generally see in a Data warehouse )

Data Warehousing in Databricks

Databricks leverages the medallion architecture and Unity Catalog to effectively implement data warehousing within its platform.

Medallion architecture

It is a data design pattern that outlines a progression of refined data layers, offering a foundational structure within the lakehouse. These layers—bronze, silver, and gold—indicate ascending levels of data quality, with the gold layer representing the highest standard.

Unity Catalog

Single platform that helps us to perform data management , security ,lineage, monitoring and sharing.