What are the Differences Between Data Lake and Data Warehouse?
Two Different Ways to Manage Big Data
As organizations have digitized, the size of the data they produce has reached Petabyte (PB) levels. Years ago, we could store all our data in classic SQL-based Data Warehouses, but today, massive unstructured data such as email logs, social media comments, sensor data (IoT), videos, and PDF documents have emerged. This is exactly where the concept of the Data Lake was born.
So, what are the main differences between a Data Lake and a Data Warehouse? Which one is more suitable for your organization?
1. Structure and Format of the Data
Data Warehouse (DWH): Accepts only structured data. In other words, they are database tables consisting of rows and columns with predefined strict rules. Before taking the data into the warehouse, you need to clean it, transform it, and fit it into a specific schema (Schema-on-Write).
Data Lake: Accepts both structured data and unstructured/semi-structured data such as images, videos, PDFs, JSON, XML in their raw form, as they are. No schema or formatting rules are required when throwing data into the lake. You fit the data into a schema only when you want to read and analyze it (Schema-on-Read).
2. Processing Logic: ETL vs ELT
ETL in Data Warehouse (Extract, Transform, Load): Extract the data from the source, make it processable and rule-based (Transform), and then load it into the warehouse. The operations take place outside (e.g., via Microto or SSIS) before the data enters the warehouse.
ELT in Data Lake (Extract, Load, Transform): Extract the raw data from the source as it is and throw it directly into the lake (Load). Then, use the processing power of the massive Cloud architecture to transform it when you are going to analyze it (Transform). This way, no data loss occurs, and the raw data can always be reverted to.
3. User Base
- Data Warehouse: Focused on Reporting and Business Intelligence (BI). End-users, business analysts, and managers consume DWH data via Power BI to make instant strategic decisions. The data is clean, filtered, and reliable.
- Data Lake: A playground for data scientists and Artificial Intelligence (Machine Learning) algorithms. Data scientists dive into the raw, massive piles within the lake to try and extract statistical insights.
The Modern Solution: Data Lakehouse
Today, technology aims to combine these two structures rather than separate them. The Data Lakehouse architecture (e.g., Databricks, Microsoft Fabric) offers the cheap and massive storage capacity of the Data Lake and the high-performance ACID (Reliability) rules of the Data Warehouse on a single platform. As DVision Technology, we set up both On-Premise DWH and Cloud-based Lakehouse architectures end-to-end as turnkey solutions with the best practices according to your business needs.
