
Data Lake: A data lake is a storage system that allows organizations to store vast amounts of raw, unstructured, semi-structured, or structured data in its native format. It provides flexibility by not requiring predefined schemas and can handle large volumes of data, making it ideal for big data and machine learning use cases.
Data Warehouse: A data warehouse is a centralized repository designed to store structured data from multiple sources. It uses predefined schemas and is optimized for analytical queries, reporting, and business intelligence. Data in a warehouse is typically transformed, cleaned, and loaded through ETL (Extract, Transform, Load) processes.
Lakehouse: A lakehouse combines the best features of data lakes and data warehouses. It stores raw data like a data lake but adds management features such as ACID transactions, schema enforcement, and data versioning, making it suitable for both analytics and machine learning workloads. The lakehouse aims to offer the scalability of a lake and the structure of a warehouse.
Imagine starting a job at a company where you’re responsible for creating reports and visualizations for the business team based on the data from the company’s product. Initially, it’s simple—there’s just one database that records transactional data, and you pull data directly from it to generate reports.
Soon, the company starts using a third-party service, and the business users request data from this service to be included in their reports. You can still manage this by pulling data from both the database and the third-party API, joining and aggregating it to create the reports.
But as the company grows and adds more services and integrates with additional external tools, each generating its own data, your end users want all of this data included in their reports. At this point, pulling data from each source separately and manually combining it becomes unmanageable.
This is where the data warehouse comes in. It acts as a centralized repository where large amounts of data from various sources can be stored and managed, serving the company’s analytics needs.
Initially, data is extracted from multiple sources, transformed into a predefined structure (schema-on-read), and loaded into the data warehouse. This setup helps businesses centralize their data, improving data management and enabling more efficient analysis.
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However, challenges soon arise. Data doesn’t always come in a structured tabular format—there’s also unstructured data such as videos, audio, and text documents. This unstructured data creates significant challenges for relational data warehouses, which are designed to handle only structured data.
You’ve likely heard the term Big Data during your career.
Big tech companies that survived the DotCom bubble in the early 2000s, such as Yahoo, Google, and Amazon, were pioneers in working with Big Data. Initially, they continued to rely on traditional data warehouses for centralizing their data. However, these systems struggled to scale with the rapidly increasing volume and variety of data.
To address this, Yahoo developed Apache Hadoop, which included processing (MapReduce) and storage (HDFS) based on Google’s papers on MapReduce and the Google File System.
Data at this point was no longer strictly structured; unstructured data—such as videos, text, and documents—was recognized as valuable for generating business insights. However, relational data warehouses were only capable of managing structured data, creating a gap.
This led to the introduction of the data lake.
A data lake allows vast amounts of raw data to be stored in its native format (such as in HDFS or later, in cloud object storage). Unlike traditional data warehouses, the data lake doesn’t require a predefined schema, allowing all types of data, including unstructured, to be stored without format constraints.
Initially, there was an attempt to replace the traditional data warehouse with the data lake by bringing processing directly on top of it. However, this approach had several issues, and the data lake eventually became a “data swamp” due to a lack of key data management features, such as data discovery, quality assurance, ACID transactions, and support for data manipulation.
Therefore, combining the strengths of both the data lake and the data warehouse became the optimal solution.
Maintaining the two-tier architecture of a data lake and data warehouse comes with several challenges:
To address these challenges, the Lakehouse architecture was introduced, primarily by Databricks.
A Lakehouse combines the benefits of both a data lake and a data warehouse. It uses low-cost storage (e.g., object storage) while providing traditional data warehouse features like ACID transactions, versioning, caching, and query optimization.
What differentiates Lakehouse from previous attempts to bring processing to data lakes is the introduction of more efficient metadata layers. For example:
In Lakehouse architecture, all data operations go through open table formats to provide essential data warehouse features such as table snapshotting, time travel, schema evolution, and partitioning. These formats also record statistics to help query engines optimize performance by pruning unnecessary data, such as by checking the min/max values of columns.
Here are some interview questions for Data Lake, Data Warehouse, and Lakehouse: