What Is a Data Lakehouse?

Data Lakehouse vs. Data Lake vs. Data Warehouse

When we talk about a data lakehouse, we’re referring to the combined usage of current data repository platforms.

• Data lake (the “lake” in lakehouse): A data lake is a low-cost storage repository primarily used by data scientists, but also by business analysts, product managers, and other types of end users. It is a big data concept. Unstructured raw data from various organizational sources goes into the lake, often for staging prior to loading into a data warehouse and building data sets.
  
  
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• Data warehouse (the “house” in lakehouse): A data warehouse is a different kind of storage repository from a data lake in that a data warehouse stores processed and structured data, curated for a specific purpose, and stored in a specified format. This data is typically queried by business users, who use the prepared data in analytics tools for reporting and projections. A data warehouse typically includes data management features such as data cleansing and extract/load/transform (ETL).
  
  
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So, how does a data lakehouse combine these two ideas? In general, a data lakehouse removes the silo walls between a data lake and a data warehouse. This means data can be easily moved between the low-cost and flexible storage of a data lake over to a data warehouse and vice versa, providing easy access to a data warehouse’s management tools for implementing schema and governance, often powered by machine learning and artificial intelligence for data cleansing. The result creates a data repository that integrates the affordable, unstructured collection of data lakes and the robust preparedness of a data warehouse. By providing the space to collect from curated data sources while using tools and features that prepare the data for business use, a data lakehouse accelerates processes. In a way, data lakehouses are data warehouses—which conceptually originated in the early 1980s—rebooted for our modern data-driven world.

Features of a Data Lakehouse

With an understanding of a data lakehouse’s general concept, let’s look a little deeper at the specific elements involved. A data lakehouse offers many pieces that are familiar from historical data lake and data warehouse concepts, but in a way that merges them into something new and more effective for today’s digital world.

Data Management Features

A data warehouse typically offers data management features such as data cleansing, ETL, and schema enforcement. These are brought into a data lakehouse as a means of rapidly preparing data, allowing data from curated sources to naturally work together and be prepared for further analytics and business intelligence (BI) tools.

Open Storage Formats

Using open and standardized storage formats means that data from curated data sources have a significant head start in being able to work together and be ready for analytics or reporting.

Flexible Storage

The ability to separate compute from storage resources makes it easy to scale storage as necessary.

Support for Streaming

Many data sources use real-time streaming directly from devices. A data lakehouse is built to better support this type of real-time ingestion compared to a standard data warehouse. As the world becomes more integrated with Internet of Things devices, real-time support is becoming increasingly important.

Diverse Workloads

Because a data lakehouse integrates the features of both a data warehouse and a data lake, it is an ideal solution for a number of different workloads. From business reporting to data science teams to analytics tools, the inherent qualities of a data lakehouse can support different workloads within an organization.

Advantages of a Data Lakehouse: A Modern Data Platform

By building a data lakehouse, organizations can streamline their overall data management process with a unified data platform. A data lakehouse can take the place of individual solutions by breaking down the silo walls between multiple repositories. This integration creates a much more efficient end-to-end process over curated data sources. This creates several benefits.

• Less administration: By using a data lakehouse, any sources connected to it can have their data accessible and consolidated for usage, as opposed to extracting it from raw data and preparing to work within a data warehouse.
• Better data governance: Data lakehouses simplify and improve governance by consolidating resources and data sources and are built with a standardized open schema, which allows for greater control over security, metrics, role-based access, and other crucial management elements.
• Simplified standards: Data warehouses originated in the 1980s, when connectivity was extremely limited, meaning localized schema standards were often created within organizations, even departments. Today, open schema standards exist for many types of data, and data lakehouses take advantage of that by ingesting multiple data sources with an overlapping standardized schema to simplify processes.
• Increased cost-effectiveness: Data lakehouses are built with infrastructure that separates compute and storage, which allows for easy addition of storage without the need to augment compute power. This creates cost-effective scaling with the simple use of low-cost data storage.

While some organizations will build a data lakehouse, others will purchase a data lakehouse cloud service.

Discover Why OCI Is the Best Place to Build a Lakehouse

With each passing day, more and more data sources are sending greater volumes of data across the globe. For any organization, this combination of structured and unstructured data continues to be a challenge. Data lakehouses link, correlate, and analyze these varied outputs into a single manageable system.