Data warehousing lakes

Data warehouse products
A data lake can store any kind of data, whether it's structured, semi-structured or unstructured.
This means that it does not enforce any model on the way to store the data.
This makes it cost-effective.
Data warehouses enforce a structured format, which makes them more costly to manipulate..
Data warehouse products
A data lake is not a direct replacement for a data warehouse; they are supplemental technologies that serve different use cases with some overlap.
Most organizations that have a data lake will also have a data warehouse..
Data warehouse products
Organize the data lake (create zones for use by various user communities and ingest the data).
Set the data lake up for self-service (create a catalog of data assets, set up permissions, and provide tools for the analysts to use).
Open the data lake up to the users..
How does data lake store data?
A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data.
It can store data in its native format and process any variety of it, ignoring size limits.
Learn more about modernizing your data lake on Google Cloud..
Is data lake replacing data warehouse?
A data lake is not a direct replacement for a data warehouse; they are supplemental technologies that serve different use cases with some overlap.
Most organizations that have a data lake will also have a data warehouse._{Dec 4, 2022}.
What are examples of data lakes?
A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video)..
What is a data warehouse lake?
While data warehouses store structured data, a lake is a centralized repository that allows you to store any data at any scale.
A data lake offers more storage options, has more complexity, and has different use cases compared to a data warehouse..

Data lakes store all types of raw data, which data scientists may then use for a variety of projects. Data warehouses store cleaned and processed data, which can then be used to source analytic or operational reporting, as well as specific BI use cases.

While data warehouses store structured data, a lake is a centralized repository that allows you to store any data at any scale. A data lake offers more storage options, has more complexity, and has different use cases compared to a data warehouse.

What are data lakes & data warehouses?

Data lakes, much like real lakes, have multiple sources (rivers) of structured and unstructured data that flow into one combined site

Data warehouses are designed to be repositories for already structured data to be queried and analyzed for very specific purposes

Why should you use a data warehouse?

By restricting data to a schema, data warehouses are very efficient for analyzing historical data for specific data decisions

You may notice that data lakes and data warehouses complement each other in a data workflow

Ingested company data will be stored immediately into a data lake

Data lakes are large-scale storage repositories that hold raw data in a native format until it's needed. This approach took off with Hadoop back in the early 2010s, when HDFS introduced a scalable way to store arbitrary data and MapReduce was the framework for processing that massive amounts of data.

Data warehousing lakes

Data warehouse products

Data warehouse products

Data warehouse products

How does data lake store data?

Is data lake replacing data warehouse?

What are examples of data lakes?

What is a data warehouse lake?

What are data lakes & data warehouses?

Why should you use a data warehouse?