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Chapter 3: The Key Differences Between Data Lakehouses, Data Lakes and Data Warehouses

Chapter 3: The Key Differences Between Data Lakehouses, Data Lakes and Data Warehouses

By RightData
January 30, 2023
solution-sepreator
A data lakehouse emerged to serve as a single platform for the data lake and data warehousing.
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Before today's advances in data management technology, businesses mainly relied on data lakes and data warehouses for business insights and storage. Though some businesses combined data warehouses with a data lake, a data lakehouse emerged to serve as a single platform for the data lake and data warehousing.

The Elements of a Data Warehouse

A data warehouse can offer some advantages to your organization. The features of a data warehouse that distinguish it from lakes and lakehouses include the following:

The Elements of a Data Lake

Because of the types of data a data lake can store, it eliminates the need to store each data format in different environments.

Some elements of a data lake set it apart from the other data architecture systems. The following are the features of a data lake that distinguish it from warehouses and lakehouses:

The Elements of a Data Lakehouse

Finally, here are the features of a data lakehouse that distinguish it from data lakes and warehouses:

Data warehouse: Reporting, analytics and BI applications.  Data lake: Affordable storage for data science and ML workloads

Key Differences in Data Warehouse vs. Data Lake vs. Data Lakehouse

Understanding the key differences between a data lakehouse vs. a data warehouse vs. a data lake is essential for ensuring the proper data structure in your business. The differences between a data lakehouse vs. a data lake, for example, will determine which one is better for a business than the other. Data warehouses, lakes and lakehouses can vary in their purposes, costs, storage data types and ACID compliance.

A data warehouse is the oldest storage technology for big data and has a lengthy history in reporting, analytics and BI applications. However, data warehouses struggle with unstructured data and tend to be more expensive. The data lake emerged to handle raw data in various formats on affordable storage for data science and ML workloads. Even though a data lake works well with unstructured data, it lacks the ACID transactional features of a data warehouse, making which makes ensuring data reliability and consistency difficult.

Data lakehouses are the newest in data storage architecture. Lakehouses are a combination of data lakes and data warehouses, so data lakehouses have some similarities to both. For example, data lakehouses combine the consistency and reliability of data warehouses with the flexibility and cost-efficiency of data lakes. Deciding which is the right choice for your business will depend on what types of data you have, how stakeholders and users access and utilize the data and your data sources. To test the data solutions from RightData, ask for a demo today.