Data Warehouse vs Data Lake vs Delta Lake: Whatโs the Difference?
Practical Guide to create DELTA Tables in Microsoft Fabric
๐โโ๏ธHi there. I am Atikant Jain (AJ). Welcome to my newsletter, where I talk about career in Analytics & Data Science. Currently spreading love about Microsoft Power BI & Microsoft Fabric.
As data grows in size and complexity, selecting the right data storage solution has become crucial. Hereโs a quick breakdown of Data Warehouses, Data Lakes, and Delta Lakesโand why Delta Lake offers a modern solution.
1๏ธโฃ ๐๐ฎ๐๐ฎ ๐ช๐ฎ๐ฟ๐ฒ๐ต๐ผ๐๐๐ฒ: ๐๐ต๐ณ๐ถ๐ค๐ต๐ถ๐ณ๐ฆ๐ฅ, ๐๐ฆ๐ญ๐ช๐ข๐ฃ๐ญ๐ฆ, ๐ข๐ฏ๐ฅ ๐๐ฏ๐ข๐ญ๐บ๐ต๐ช๐ค๐ด-๐๐ฐ๐ค๐ถ๐ด๐ฆ๐ฅ
๐ฉ๐๐๐ ๐๐๐:ย Historical and transactional data that needs to be highly structured.
๐ผ๐๐ ๐ช๐๐๐: Ideal for BI (Business Intelligence) and reporting; optimized for fast SQL queries.
๐ณ๐๐๐๐๐๐๐๐๐๐: Not built for large-scale unstructured or semi-structured data.
2๏ธโฃ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ธ๐ฒ: ๐๐ญ๐ฆ๐น๐ช๐ฃ๐ญ๐ฆ, ๐๐ข๐ณ๐จ๐ฆ-๐๐ค๐ข๐ญ๐ฆ ๐๐ต๐ฐ๐ณ๐ข๐จ๐ฆ ๐ง๐ฐ๐ณ ๐๐ญ๐ญ ๐๐ข๐ต๐ข ๐๐บ๐ฑ๐ฆ๐ด
๐ฉ๐๐๐ ๐๐๐: Storing large volumes of structured, semi-structured, and unstructured data.
๐ผ๐๐ ๐ช๐๐๐: Ideal for raw data storage and later transformation. Great for machine learning.
๐ณ๐๐๐๐๐๐๐๐๐๐: Data reliability can be an issue, as traditional data lakes lack structure, which can lead to โdata swampโ problems without proper management.
3๏ธโฃ ๐๐ฒ๐น๐๐ฎ ๐๐ฎ๐ธ๐ฒ: ๐๐ฉ๐ฆ ๐๐ฆ๐ด๐ต ๐ฐ๐ง ๐๐ฐ๐ต๐ฉ ๐๐ฐ๐ณ๐ญ๐ฅ๐ด
๐๐๐ฎ ๐๐โ๐ ๐๐๐๐ฉ๐๐ง: Delta Lake combines the storage capabilities of a Data Lake with the data integrity and reliability features of a Data Warehouse.
๐ญ๐๐๐ฉ๐๐ง๐๐จ: It provides ACID transactions, schema enforcement, and time travel for historical data.
๐ฉ๐๐๐ฉ ๐๐๐ง: Scalable, reliable, and high-performance analytics, especially when working with both batch and streaming data.
๐๐๐ฎ ๐ฟ๐๐ก๐ฉ๐ ๐๐๐ ๐ ๐๐ญ๐๐๐ก๐จ: ๐๐๐ ๐๐ค๐ฌ๐๐ง ๐ค๐ ๐๐๐ง๐ฆ๐ช๐๐ฉ ๐๐ค๐ง๐ข๐๐ฉ
Delta Lake uses the Parquet format for data storage, which brings multiple advantages:
โถ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐๐ซ ๐๐ญ๐จ๐ซ๐๐ ๐: Parquet organizes data by columns, which is ideal for analytics queries, allowing faster access to needed data.
โถ ๐๐จ๐ฆ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง: With high data compression, Parquet reduces storage costs and optimizes performance.
โถ ๐๐ง๐ญ๐๐ซ๐จ๐ฉ๐๐ซ๐๐๐ข๐ฅ๐ข๐ญ๐ฒ: Parquet is compatible with most big data tools, making it easier to integrate into diverse tech stacks.
Delta Lakeโs structure, combined with Parquetโs efficiency, enables both fast querying and cost savingsRecently I have created a Video on explaining this concept with case study in Microsoft Fabric. If you would like to learn more, you can watch it here:
Microsoft Fabric is getting a lot of traction lately. In my opinion, it is one of the best times to study about Fabric and take advantage. There will a lot of jobs for Data Analysts where employer will expect you to know Microsoft Fabric.
You can watch the YouTube Playlist as well to stay updated.
Talk to you soon, and donโt forget to learn something every day!
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