As the use of ChatGPT becomes more prevalent, I frequently encounter customers and data users citing ChatGPT’s responses in their discussions. I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics. ChatGPT is an excellent resource for gaining high-level insights and building awareness of any technology. However, caution is necessary when delving deeper into a particular technology.
A data lakehouse is a data storage repository designed to store both structured data and data from unstructured sources. It allows users to access data stored in different forms, such as text files, CSV or JSON files. Data stored in a data lakehouse can be used for analysis and reporting purposes.
This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes.
For the past 30 years, the primary data source for business intelligence (BI) and data visualization tools has generally been either a data warehouse or a data mart. But as enterprises today struggle to cope with the growing complexity, scale, and speed of data, it’s becoming clear that the data tools of 30 years ago weren’t designed to handle the enterprise data management challenges of today - especially with the growing variety and amounts of data that enterprises are generating.
Current data architecture is going through a revolution. Enterprises are starting to shift away from the monolithic data lake towards something less centralized: data mesh. It’s a relatively new concept, first coined in 2019, that addresses potential issues with data warehouses and data lakes that can cause businesses to be slow, unresponsive, or even suffer from data silos. What is a data mesh, and how could it benefit your business?
From databases to data warehouses and, finally, to data lakes, the data landscape is changing rapidly as volumes and sources of data increase. With a growth projection of almost 30%, the data lake market will grow from USD 3.74 billion in 2020 to USD 17.6 billion by 2026. Also, from the 2022 Data and AI Summit, it is clear that data lake architecture is the future of data management and governance.
The data lakehouse is a promising new technology that combines aspects of data warehouses and data lakes.