What is a Data Lakehouse? Definition, Benefits & Features

A data lakehouse is a hybrid data management architecture that combines the best features of a data lake and a data warehouse into one data management solution.
A data lake is a centralized repository that allows storage of large amounts of data in its native, raw format. On the other hand, a data warehouse is a repository that stores structured and semi-structured data from multiple sources for analysis and reporting purposes.
A data lakehouse aims to bridge the gap between these two data management approaches by merging the flexibility, scale and low cost of data lake with the performance and ACID (Atomicity, Consistency, Isolation, Durability) transactions of data warehouses. This enables business intelligence and analytics on all data in a single platform.
Jump to:

What does a data lakehouse do?
Difference between data lakehouse, data warehouse, and data lake
5 layers of data lakehouse architecture
Key features of a data lakehouse
Advantages of a data lakehouse
Challenges of a data lakehouse
Bottom line: the data lakehouse





What Does a Data Lakehouse Do? 
A data lakehouse leverages a data repository’s scalability, flexibility and cost-effectiveness, allowing organizations to ingest vast amounts of data without imposing strict schema or format requirements.
In contrast with data lakehouses, data lakes alone lack the governance, organization, and performance capabilities needed for analytics and reporting.
Data lakehouses also are distinct from data warehouses. Data warehouses use extract, load and transform (ELT), or alternatively use extract, transform, and load (ETL) processes to load structured data into a relational database infrastructure – a data warehouse supports enterprise data analytics and business intelligence applications. However, a data warehouse is limited by its inefficiency in handling unstructured and semi-structured data. Additionally, they can get costly as data sources and quantity grow over time.
Data lakehouses address the limitations and challenges of both data warehouses and data lakes by integrating the flexibility and cost-effectiveness of data lakes with data warehouses’ governance, organization, and performance capabilities.
The following users can leverage a data lakehouse:

Data scientists can use a data lakehouse for machine learning, BI, SQL analytics and data science.
Business analysts can leverage it to explore and analyze diverse data sources and business uses.
Product managers, marketing professionals, and executives can use data lakehouses to monitor key performance indicators and trends.

Also see: What is Data Analytics
The data lakehouse combines the functionality of a data warehouse with that of a data lake. Source: Databricks.
Deeper Dive: Data Lakehouse vs. Data Warehouse and Data Lake
We have established that data lakehouse is a product of data warehouse and data lake capabilities. It enables efficient and highly flexible data ingestion. Let’s take a deeper look at how they compare.
Data warehouse
The data warehouse is the “house” in a data lakehouse. A data warehouse is a type of data management system specially designed for data analytics; it facilitates and supports business intelligence (BI) activities. A typical data warehouse includes several elements, such as:

A relational database.
An ELT solution for preparing the data for analysis, statistical analysis, reporting, and data mining capabilities.
A client analysis tools for data visualization.

Data lake
A data lake is the “lake” in a data lakehouse. A data lake is a flexible, centralized storage repository that allows you to store all your structured, semi-structured and unstructured data at any scale. A data lake uses a schema-on-read methodology, meaning there is no predefined schema into which data must be fitted before storage.
This chart compares data lakehouse vs. data warehouse vs. data lake concepts.

Parameters
Data lakehouse
Data warehouse
Data lake

Data structure
Structured, semi-structured, and raw
Structured data (tabular, relational)
Unstructured, semi-structured, and raw

Data storage
Combines structured and raw data, schema-on-read
Stores data in a highly structured format with a predefined schema
Stores data in its raw form (e.g., JSON, CSV) with no schema enforced

Schema
Combines elements of both schema-on-read and schema-on-write
Uses fixed schema known as Star, Galaxy, and Snowflake schema
Schema-on-read, meaning data can be stored without a predefined schema

Query performance
Combines the strengths of data warehouse and data lake for balanced query performance
Optimized for fast query performance and analytics using indexing and optimization techniques
Slower query performance

Data transformation
Often includes schema evolution and ETL capabilities
ETL and ELT
Limited built-in ETL capabilities; data often needs transformation before analysis

Data governance
Varies based on specific implementations but is generally better than a data lake
Strong data governance with control over data access and compliance
Limited data governance capabilities; data might lack governance features

Use cases
Analytical workloads, combining structured and raw data
Business intelligence, reporting, structured analytics
Data exploration, data ingestion, data science

Tools and ecosystem
Leverages cloud-based data platforms and data processing frameworks
Typically uses traditional relational database systems and ETL tools
Utilizes big data technologies like Hadoop, Spark, and NoSQL databases

Cost
Cost effective
Expensive
Cheaper than data warehouse

Adoption
Gaining popularity for modern analytics workloads that require both structured and semi-structured data
Common in enterprises for structured data analysis
Common in big data and data science scenarios

5 Layers of Data Lakehouse Architecture
The IT architecture of the data lakehouse consists of five layers, as follows:
Ingestion layer
Data ingestion is the first layer in the data lakehouse architecture. This layer collects data from various sources and delivers it to the storage layer or data processing system. The ingestion layer can use different protocols to connect internal and external sources, such as:

Database management systems
Software as a Service (SaaS) applications
NoSQL databases
Social media
CRM applications
IoT sensors
File systems

The ingestion layer can perform data extraction in a single, large batch or small bits, depending on the source and size of the data.
​​Storage layer
The data lakehouse storage layer accepts all data types as objects in affordable object stores like AWS S3.
This layer stores structured, unstructured, and semi-structured data in open source file formats like Parquet or Optimized Row Columnar (ORC). A data lakehouse can be implemented on-premise using a distributed file system like Hadoop Distributed File System (HDFS) or cloud-based storage services like Amazon S3.
Also see: Top Data Analytics Software and Tools 
Metadata layer
This layer is very important because it serves as the origin of the data lakehouse. Metadata is data that provides information about other data pieces – in this layer, it’s a unified catalog that includes metadata for data lake objects. The metadata layer also equips users with a range of management functionalities, such as:

ACID (Atomicity, Consistency, Isolation, Durability) transactions ensure atomicity, consistency, isolation, and durability for data modifications.
File caching capabilities optimize data access by keeping frequently accessed files readily available in memory.
Indexing accelerates queries by enabling swift data retrieval.
Data versioning enables users to save specific versions of the data.

The metadata layer empowers users to implement predefined schemas to enhance data governance and enable access control and auditing capabilities.
API layer
The API layer is a particularly important component of a data lakehouse. It allows data engineers, data scientists, and analysts to access and manipulate the data stored in the data lakehouse for analytics, reporting, and other use cases.
Consumption layer
The consumption layer is the final layer of data lakehouse architecture – it is used to host tools and applications such as Power BI and Tableau, enabling users to query, analyze, and process the data. The consumption layer allows users to access and consume the data stored in the data lakehouse for various business use cases.
Key Features of a Data Lakehouse

ACID transaction support: Many data lakehouses use a technology like Delta Lake (developed by Databricks) or implement ACID transactions to provide data consistency and reliability in a distributed environment.
Single data low-cost data storage: Data lakehouse is a cost-effective option for storing all data types, including structured, semi-structured and unstructured data.
Unstructured and streaming data support: While a data warehouse is limited to structured data, a data lakehouse supports many data formats, including video, audio, text documents, PDF files, system logs and more. A data lakehouse also supports real-time ingestion of data – and streaming from devices.
Open formats support: Data lakehouses can store data in standardized file formats like Apache Avro, Parquet and ORC (Optimized Row Columnar).

Advantages of a Data Lakehouse
A data lakehouse offers many benefits, making it a worthy alternative solution to a standalone data warehouse or data lake. Data lakehouses combine the quality service and performance of a data warehouse with the affordability and flexible storage infrastructure of a data lake. Data lakehouse helps data users solve the following issues.

Unified data platform: It serves as a structured and unstructured data repository, eliminating data silos.
Real-time and batch processing: Data lakehouses support real-time processing for fast and immediate insight and batch processing for large-scale analysis and reporting.
Reduced cost: Maintaining a separate data warehouse and data lake can be too pricey. With a data lakehouse, data management teams only have to deploy and manage one data platform.
Better data governance: Data lakehouses consolidate resources and data sources, allowing greater control over security, metrics, role-based access, and other crucial management elements.
Reduced data duplication: When copies of the same data exist in disparate systems, it is more likely to be inconsistent and less trustworthy. Data lakehouses provide organizations with a single data source that can be shared across the business, preventing any inconsistencies and extra storage costs caused by data duplication.

Challenges of a Data Lakehouse
A data lakehouse isn’t a silver bullet to address all your data-related challenges. The data lakehouse concept is relatively new and its full potential and capabilities are still being explored and understood.
A data lakehouse is a complex system to build from the ground up. You’ll need to either opt for an out-of-box data lakehouse solution whose performance is highly variable, depending on the query type and the engine processing it, or invest time and resources to develop and maintain your custom solution.
Bottom Line: the Data Lakehouse
The data lakehouse is a new concept that represents a modern approach to data management. It’s not an outright replacement for the traditional data warehouse or data lake but a combination of both.
Although data lakehouses offer many advantages that make it desirable, it is not foolproof. You must take proactive steps to avoid and manage the security risks, complexity, as well as data quality and governance issues that may arise while using a data lakehouse system. 
Also see: Generative AI and Data Analytics Best Practices 
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