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.
When we talk about a data lakehouse, we’re referring to the combined usage of currently existing data repository platforms:
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.
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.
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.
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.
The ability to separate compute from storage resources makes it easy to scale storage as necessary.
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.
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.
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:
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.
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.
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.
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.
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.