Combining the efficiency and structure of a data warehouse with the adaptability of a data lake, Data Lakehouse design maximizes the advantages of both data lakes and data warehouses.
The five layers of a Data Lakehouse architecture—data ingestion, data storage, metadata, API, and data consumption—will be dissected in this article. We'll also learn about the increased possibilities that a data lakehouse presents for generative AI and how data observability can be used to preserve data quality throughout the pipeline.

What is Data Lakehouse Architecture?

The semantic layer of the data lakehouse contributes to open and simplified data access inside an enterprise. Data engineers have less work to do since downstream data consumers, such as data scientists and analysts, can experiment with different analytical techniques and generate their own reports without transferring or copying data. Data Lakehouse design facilitates interoperability between data lake types, it is becoming a more and more attractive option for several enterprises. It can do quick queries on structured and unstructured data directly on object storage in the cloud or on-premises using SQL commands, and it supports ACID transactions.

The 5 Key Layers of Data Lakehouse Architecture

One of the numerous advantages of storing both structured and unstructured data in a data lakehouse for a data organization is that it facilitates and streamlines the support of workloads related to both business intelligence and data science. The data source is where this begins.

1. Ingestion layer

Data is extracted and brought into the data lake by the ingestion layer of a data lakehouse design. These sources include relational and transactional databases, APIs, real-time data streams, CRM apps, NoSQL databases, and more. At this tier, a company may employ tools such as Apache Kafka for data streaming, Amazon Data Migration Service (Amazon DMS) for importing data from RDBMSs and NoSQL databases, and many more.

2. Storage layer

The layer in the data lakehouse architecture that stores the ingested data in inexpensive locations like Amazon S3 is called the storage layer. Organizations can read objects directly from the storage layer utilizing open file formats, such as Parquet, and metadata, which contains the schemas of both structured and unstructured datasets, by using their preferred tool or APIs now that object storage is independent of computing.

3. Metadata layer

The metadata layer is responsible for organizing and managing the metadata related to ingested and stored data. Data insights and information such as orchestration jobs, transformation models, field profiles, current updates and users, historical data quality problems, and more are all included in the metadata. Moreover, features like ACID transactions, caching, indexing, zero-copy cloning, and data versioning can be implemented thanks to the metadata layer. Moreover, schema management and enforcement enable data teams to maintain data quality and integrity by rejecting updates that don't match the schema of the table and altering an existing table's schema to make sure it's compatible with dynamic data. A data team can also follow the provenance and modification of data to comprehend its evolution by using data lineage.

4. API layer

Applications from outside the company and analytics tools can query the data stored in the data lakehouse architecture thanks to APIs (application programming interfaces). An analytical tool can determine which datasets are necessary for a given application and how to retrieve, transform, or write sophisticated queries on the data through an API call or interface. Moreover, real-time data, such as streaming data, can be instantly consumed and processed by apps thanks to APIs. This implies that teams may evaluate data in real-time to extract insights, particularly for data streams that are dynamic and continually updated.

5. Data Consumption Layer

The consumption layer gives downstream users—such as data scientists, analysts, and other business users—the ability to utilize their client apps to access the data stored in the data lake along with all of its metadata by leveraging a variety of tools, including Power BI, Tableau, and others. Every user has access to the lakehouse's data to perform various analytics operations, such as creating dashboards, displaying data, executing SQL queries, executing machine learning tasks, and more.

Maximizing GenAI Potential with Data Lakehouse Architecture

There are significant chances to improve generative AI using data lakes. Data teams may more efficiently produce content, insights, and dynamic rapid replies by utilizing the complete range of data resources for their generative AI applications, thanks to the capabilities and structure of a data lakehouse. Data teams use a variety of tools on top of their data lakehouse to maximize the performance of their generative AI applications. These tools include vector databases, which help reduce hallucinations; AutoML, which expedites the deployment of machine learning; LLM gateways for integration; prompt engineering tools, which enhance stakeholder engagement and communication; and robust data monitoring capabilities, such as data observability tools, which guarantee high-quality data is fed into the GenAI model and, consequently, high-quality responses are generated.

Conclusion

One significant advancement in data management that is still developing is data lakehouse architecture. Teams must make sure they have the resources in place to keep an eye on the data being stored, processed, and queried in the lakehouse as data lakehouse architecture gains traction. Brigita Data transformation Solutions can provide data observability, which is valuable for data and will be crucial in monitoring and preserving the datasets' quality within the lakehouse for seamless business operations.