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What is a Data Lakehouse?

Data Warehouse + Data Lake = Data Lakehouse

A data lakehouse can be defined as a modern data platform built from a combination of a data lake and a data warehouse. More specifically, a data lakehouse takes the flexible storage of unstructured data from a data lake and the management features and tools from data warehouses, then strategically implements them together as a larger system. This integration of two unique tools brings the best of both worlds to users. To break down a data lakehouse even further, it’s important to first fully understand definition of the two original terms.

Data lakehouse vs. data lake vs. data warehouse

When we talk about a data lakehouse, we’re referring to the combined usage of currently existing 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.


  • 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).

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 modern 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 a number of 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 standardized open scheme, which allows for greater control over security, metrics, role-based access, and other critical 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 standards for schema exist for many types of data, and data lakehouses take advantage of that by ingesting multiple data sources with overlapping standardized schema to simplify processes.
  • Increased cost-effectiveness: Data lakehouses are built with modern 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.

Customer successes: data lakehouse

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Experian

Experian improved performance by 40% and reduced costs by 60% when it moved critical data workloads from other clouds to a data lakehouse on OCI, speeding data processing and product innovation while expanding credit opportunities worldwide.

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Generali

Generali Group is an Italian insurance company with one of the largest customer bases in the world. Generali had numerous data sources, both from Oracle Cloud HCM and other local and regional sources. Their HR decision process and employee engagement were hitting roadblocks, and the company sought a solution to improve efficiency. Integrating Oracle Autonomous Data Warehouse with Generali's data sources, removed silos and created a single resource for all HR analysis. This improved efficiency and increased productivity among HR staff, allowing them to focus on value-added activities rather than the churn of report generation.

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Lyft

One of the world's leading rideshare providers, Lyft was dealing with 30 different siloed finance systems. This separation hindered the growth of the company and slowed processes down. By integrating Oracle Cloud ERP and Oracle Cloud EPM with Oracle Autonomous Data Warehouse, Lyft was able to consolidate finance, operations, and analytics onto one system. This cut the time to close its books by 50%, with the potential for even further process streamlining. This also saved on costs by reducing idle hours.

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Agroscout

Agroscout is a software developer that works with helps farmers maximize healthy and safe crops. To increase food production, Agroscout used a network of drones to survey crops for bugs or diseases. The organization needed an efficient way to both consolidate the data and process it for identifying signs of crop danger. Using Oracle Object Storage Data Lake, the drones uploaded crops directly. Machine learning models were built with OCI Data Science to process the images. The result was a vastly improved process that enabled rapid response to increase food production.

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.