This post outlines the usages of datalakes and data warehouses.
Datalakes are generally built on databases which can
support storage of unstructured or semi-structured data. There isn’t a strict
schema such as like datawarehouse built on top of RDBMS’s.
This allows adding additional fields to tables(documents) when
new fields become available. For example:
We may have 1 million customers already stored. Now
we need to import another million with a new field for each customer such as
credit rating. This can be done in a datalake without having to change the
previous customers and adding null credit ratings to historical rows.
The Datalake acts as a buffer between live application
databases and analytics. Data from live databases is periodically exported and
placed into the datalake. Analytics teams would then only work with the data
held in the datalake.
Datalake is an intermediate storage for analytics, original
data imported to a data lake is generally not readily analysable. The data
would go through series of transformations to transform it to a form that can
be analysed. This may be in the forms of running ETL jobs or Stream
transformations to convert from the semi structured data into warehouse schema.
The warehouse schema may be held in a RDBMS allowing it to be queried using SQL
by business professionals. Transformations from datalake into structured data
would require scripting and not just querying. This is achieved languages such
as python, scala hosted through platforms such as spark or hadoop. They take
the semi structured data and transform them into a schema. Once the data is
transformed it can be queried for reporting purposes.
Below is a typical data pipeline to describe where a datalake would sit:
Below is an article
outlining the differences:
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