A data lakehouse is a combination of a data lake and a data warehouse, with an added governance layer that effectively gives a traditional data lake a major security boost.
A data lake is a storage system that holds a large amount of raw data in its native format, and a data warehouse is a centralized repository for storing and accessing corporate data. In a data lakehouse, users can access raw data through a governance layer that enforces security and controls access to the data.
Organizations use data lakehouses when they deal with large volumes of all types of data—including structured, semi-structured, or unstructured data like video, images, audio, documents, and more. Companies benefit from implementing a lakehouse over a traditional warehouse because its ability to scale efficiently can accommodate ever-growing amounts of data.
However, the lack of structure that makes data lakehouses so flexible can also be a disadvantage because it is more complicated to understand and work with the data. For this reason, data lakehouses are often used in conjunction with other data repositories, such as data marts, which is a form of data warehouse focused on a single subject or line of business or data warehouses.
A data lakehouse is a powerful tool that gives users a unified view of their data regardless of where it is stored or how it’s structured. It allows them to store and analyze data of all types, from structured to unstructured.
The key features of a data lakehouse include:
With a data lakehouse, data engineers can run transformations on the data lake to convert raw data into structured data while business analysts and data scientists can use this new data to quickly identify trends, make predictions, and find new insights to derive business value directly from the data lake. Data lakehouses also allow organizations to govern data across the enterprise, ensuring that data is consistent and compliant with policies.
The data lakehouse architecture is designed to provide users with a single place to access all of their data, regardless of its structure or format. The architecture is designed to be highly scalable and allow for real-time processing of data.
The data lakehouse architecture includes the following components:
A typical problem for enterprises is ensuring that users can easily and securely access the data they need. This is because data is traditionally stored in silos, making it difficult to get a 360-degree view of all the available data. Not only do data lakes break past these barriers, they make the entire process more affordable too since data lakes are generally a less expensive data storage option.
Data lakehouses limit an organization’s exposure to costly data breaches by storing sensitive data in a centralized location that allows for security protocols to be applied across all assets. Furthermore, by ingesting data in its native format, companies don’t need to apply mass reconfiguration across their entire repository. This saves on costly engineering resources and allows companies to easily roll out new types of analytics applications, such as machine learning, without having to do substantial re-architecting.
The sweet spot for a lakehouse is an organization that has diverse data and wants to be able to experiment with new types of analytics on that data quickly and cost-effectively.
Data lakehouses provide a single platform for an organization to manage all of their data, from structured data in databases to unstructured data in files and logs, making it possible to run analytics across all of an organization’s data, regardless of where it is stored.
Data lakehouses are a newer type of data storage system that combine the best features of data lakes and data warehouses. They offer the same benefits as data lakes, including the ability to store large amounts of data, support for multiple data types, easy scalability, improved performance, and easier access to data, plus the added benefit of being able to process data in real-time, making it ideal for businesses that need to analyze large amounts of data quickly.
Users benefit from using a data lakehouse in their ability to prototype new applications rapidly. Data engineers can leverage a data lakehouse to run transformations directly on the data lake providing easier access to insights for data analysts. A cloud based architecture decoupling storage from computing makes it easy to spin up new analytic workloads without going through a lengthy provisioning process.
The right query engine helps businesses access their data through an interactive platform that delivers high concurrency, scalability, and performance, while increasing productivity and lowering infrastructure costs. This flexibility can speed up the time to value for new projects and help organizations leverage their data assets better.
Overall, using a data lakehouse includes storing more data, processing data quicker, achieving better performance, providing easier access to data, and supporting multiple users.
The challenges of using a data lakehouse include:
Data lakehouses can make it difficult to govern data due to the large volume and variety of data stored on the platform. Unlike a data warehouse, which exclusively stores processed data, a data lakehouse is designed to offer the capability of storing data in a variety of formats. This offers users more flexibility, but also adds a significant layer of complexity to implementing universal governance strategies across different data types.
Data lakehouses present a security challenge since data stored can contain sensitive or confidential information in its original format. Data subject to stringent security and compliance requirements are better suited for a traditional data warehouse.
Data lakehouses are prone to suffering from data quality issues because data will often be transformed on the fly for analytical purposes. They can also make it demanding to monitor and manage data quality issues due to the large volume and variety of data stored on the platform.
A data lake is a type of data storage and management where multiple applications can access stored data. A data lakehouse, on the other hand, is a method for managing and analyzing large amounts of diverse data. Data lakes are intended to provide a central repository for all different kinds of data, whereas data lakehouses are designed to support a variety of analytics workloads. Predictive modeling, real-time analysis, and ad-hoc queries are just a few examples of analytics workloads that may be run on a data lake.
The addition of the data governance layer distinguishes a data lake from a data lakehouse. This layer gives businesses greater control over their data while also posing new problems.
Get the best of both worlds from a data lake and a data warehouse with a data lakehouse. Learn more about adding a governance layer to your large data repository with Starburst.
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