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.
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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:
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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.
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 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 |
The IT architecture of the data lakehouse consists of five layers, as follows:
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:
The ingestion layer can perform data extraction in a single, large batch or small bits, depending on the source and size of the data.
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.
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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:
The metadata layer empowers users to implement predefined schemas to enhance data governance and enable access control and auditing capabilities.
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.
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.
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.
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.
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.
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