Blogs Data Lake House | The new paradigm in the data platform architecture
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Hassan Rahmathulla
Cloud, Data,

Data Lake House | The new paradigm in the data platform architecture

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Data LakeHouse is the new term in the Data platform architecture paradigm. LakeHouse is like the combination of both Data Lake and Data Warehouse (obviously from the term you might have understood). In Data LakeHouse, you will have processing power on top of Data Lakes such as S3, HDFS, Azure Blob, etc.

In other words, you don’t have to load the data onto any of the data warehouses to process and get the analysis or Business intelligence requirement done. You can directly query the data underlying in your data lakes made of object storage or Hadoop. This method decreases the operational overhead on Data Pipelining and maintenance.

Advantage of Data LakeHouse

In the Data warehousing technique, the data has to be loaded into Data Warehouse to Query or to perform analysis. For example.

Elimination of simple ETL jobs

In the Data warehousing technique, the data has to be loaded into Data Warehouse to Query or to perform analysis. For example, loading the data from your existing Data Lake to the Data Warehouse by cleaning and transforming it into the destination schema using some of the ETL/ELT tools. But using the Data LakeHouse tool, the ETL process will be eliminated by connecting the query engine directly to your Data Lake.

Reduced Data Redundancy

Data LakeHouse removes Data Redundancy. For Example, You have Data on multiple tools and platforms such as cleaned data on Data warehouse for processing, some meta-data on Business intelligence tool, temporary data on ETL tools, etc. These Data has to be maintained and monitored continuously to avoid any data sanity issues. So if you are using a single tool to process out of your raw data, you can overcome this data redundancy problem.

Ease of Data Governance

Data LakeHouse can eliminate the operational overhead of managing Data governance on multiple tools. If you are handling sensitive data, you have to be careful when you are transferring data from one tool to another, so that each tool can maintain the proper access controls and encryption. But if you use a single Data LakeHouse tool, Data Governance management can be done from one-point.

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