A data lakehouse is a modern data architecture that creates a single platform by combining the key benefits of data lakes (large repositories of raw data in its original form) and data warehouses (organized sets of structured data). Specifically, data lakehouses enable organizations to use low-cost storage to store large amounts of raw data while providing structure and data management functions.

Historically, data warehouses and data lakes had to be implemented as separate, siloed architectures to avoid overloading the underlying systems and creating contention for the same resources. Companies used data warehouses to store structured data for business intelligence (BI) and reporting and data lakes to store unstructured and semi-structured data for machine learning (ML) workloads. But this approach required data to be regularly shifted between the two separate systems when data from either architecture needed to be processed together, creating complexity, higher costs, and issues around data freshness, duplication, and consistency. 

Data lakehouses aim to break down these silos and deliver the flexibility, scalability, and agility needed to ensure your data generates value for your business, rather than inefficiencies.

The term “data lakehouse” merges two types of existing data repositories: the data warehouse and the data lake. So, what exactly are the differences when it comes to a data lakehouse vs. data lake vs. data warehouse? 

Data warehouses provide fast access to data and SQL compatibility for business users that need to generate reports and insights for decision-making. All data must go through ETL (extract, transform, load) phase. This means it is optimized in a specific format, or schema, based on the use case before it is loaded to support high-performance queries and data integrity. However, this approach limits the flexibility of access to the data and creates additional costs if data needs to be moved around for future use. 

Data lakes store large amounts of unstructured and structured data in its native format. Unlike data warehouses, data is processed, cleaned up, and transformed during analysis to enable faster loading speeds, making them ideal for big data processing, machine learning, or predictive analytics. However, they require expertise in data science, which limits the set of people who can use the data, and if they’re not properly maintained, data quality can deteriorate over time. Data lakes also make it more challenging to get real-time queries as the data is unprocessed, so it still potentially needs to be cleaned, processed, ingested, and integrated before it can be used. 

A data lakehouse merges these two approaches to create a single structure that allows you to access and leverage data for many different purposes, from BI to data science to machine learning. In other words, a data lakehouse captures all of your organization’s unstructured, structured, and semi-structured data and stores it on low-cost storage while providing the capabilities for all users to organize and explore data according to their needs. 

The key data lakehouse features include: 

• Single data low-cost data store for all data types (structured, unstructured, and semi-structured) 

• Data management features to apply schema, enforce data governance, and provide ETL processes and data cleansing
• Transaction support for ACID (atomicity, consistency, isolation, and durability) properties to ensure data consistency when multiple users concurrently read and write data 
• Standardized storage formats that can be used in multiple software programs
• End-to-end streaming to support real-time ingestion of data and insight generation 
• Separate compute and storage resources to ensure scalability for a diverse set of workloads

Direct access for BI apps to the source data in the lakehouse to reduce data duplication. 

When it comes to making a data lakehouse work, it’s important to consider what it’s trying to achieve. Data lakehouses aim to centralize disparate data sources and simplify engineering efforts so that everyone in your organization can be data users. 

A data lakehouse uses the same low-cost cloud object storage of data lakes to provide on-demand storage for easy provisioning and scaling. Like a data lake, it can capture and store large volumes of all data types in raw form. The lakehouse integrates metadata layers over this store to provide warehouse-like capabilities, such as structured schemas, support for ACID transactions, data governance, and other data management and optimization features.