Data Warehouse etc. Data Lake: Differences and Modern Data Lakehouse

Understanding the Nature of Data

One of the first questions organizations ask is: "Why should I set up a data warehouse instead of my operational database?". The answer is simple: Operational systems are optimized for writing the transaction (OLTP), while Data Warehouse (DWH) is optimized for reading and analyzing millions of rows (OLAP).

DWH and Data Lake Contrast

Traditional Data Warehouses (SQL Server, Oracle) hold structured, cleaned and schema-on-write data. It is the ultimate source of truth for financial reporting and business intelligence (BI). On the other hand, Data Lake weighs huge unstructured data such as IoT logs, images and text files in raw format (Schema-on-read). It is a playground for data scientists.

Data Lakehouse Evolution

Today, technologies such as Microsoft Fabric and Databricks have created the Data Lakehouse concept, which combines the flexibility of Data Lake with the ACID transaction assurance of Data Warehouse. As DVision Technology, we design the most cost-effective Azure DWH or Lakehouse architecture depending on the size of your organization and enable you to generate instant value from data.