Data Lakehouse

A data lakehouse is a data warehouse that combines the scalability and cost-effectiveness of a data lake with the performance and governance of a data warehouse.

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A data lakehouse provides organizations with the ability to store and manage large volumes of structured and unstructured data, giving them the flexibility to quickly access and analyze data in the way that best suits their business objectives.

Data lakehouse architecture

A data lakehouse architecture is a modern alternative to the traditional data warehouse architecture. It provides organizations with the ability to scale their data warehouse and data lake capabilities rapidly. This architecture allows for the easy and rapid deployment of data warehouses and data lakes, enabling organizations to quickly and easily access the data they need.

A data lakhouse architecture is comprised of the following components:

Storage: This is where the data ends up after it is ingested from its sources. Object storage is available from cloud service providers that support storing various kinds of data and able to facilitate the required performance and security. These systems are also highly scalable and inexpensive, which can help lowering costs.

Files: These are the actual data stored, which are typically columnar formats that offer great advantages for the reading or sharing of data between multiple systems.

Tables: These allow you to organize and manage the raw data in the heart of the data lakehouse architecture - the data lake storage. The data lake table formats help abstract the complexity of physical data structures and allow different query engines to work on the same data simultaneously. They also allow transactions to be performed at the data warehouse level with ACID guarantees.

Query engines: These are responsible for the processing of data and they can provide efficient read performance, some can also natively connect to other business intelligence tools, making it easy to report directly on the data in object storage.

Other complimentary tools: These might be necessary in order to interact with the data, such as business intelligent tools or machine learning frameworks that can allow data scientists and analysts to directly access the data in a more efficient way.

Data lakehouse vs data warehouse

A data lakehouse offers advantages over traditional data warehouses, as it allows data to be stored in a more cost-effective and accessible way. It is also much more scalable and flexible, allowing organizations to quickly and easily access and analyze data in a variety of formats, such as structured, semi-structured and unstructured data. Additionally, data lakehouses are able to handle massive amounts of data, making them well-suited for organizations that have large volumes of data.

The main differences between a data warehouse and a data lakehouse are the types of data that can be stored and the ease of access to the data. Data warehouses typically store curated, structured data, while data lakehouses can store both structured and unstructured data that might not be curated. Data warehouses are designed to be more easily queried than data lakehouses, making them more useful for traditional business intelligence and analytics applications, therefore typically used by business analysts. Data lakehouses, on the other hand, are designed to be more flexible and open to exploration of new data sources, thus more commonly used by data scientists and machine learning engineers.

Data lakehouse vs data lake

A data lakehouse has the advantage of providing better data management, security and governance than data lakes, which is also their main difference. By using data governance tools, data lakehouses are able to organize and manage data in a secure and efficient manner. This ensures that data is kept safe from unauthorized access and helps organizations to comply with data privacy regulations.

Suggested reading:

Data warehouse, data lake, or data lakehouse

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