How do you design a data lakehouse architecture that supports data integration across multiple domains?

To design a data lake-house architecture for seamless multi-domain integration, start by defining data sources and automating ingestion with tools like Airbyte to expedite integration. Standardise data formats (structured/unstructured) and document transparent data models to ensure consistency. Implement the data catalog tools like OpenMetadata or Project Nessie for governance. Optimise storage costs using open table formats like Iceberg. Finally, build scalable data pipelines using orchestration tools like Airflow or Prefect to maintain efficiency and resilience.

Understand how your domain interact with other domains based in dependencies types between them, etc; and based in these points you can create layers to manage the interaction. For example, i will create following layers: > Landing layer: to store extracted data. > Bronze layer: to generate a copy of table from all extracted data, named raw table. > Silver layer: to generate a cleansing table from raw table. > Integrated layer: you can generate new entities or concept by combine current entities from silver layer. > Gold layer: contain all datasets that clients will consume from data lakehouse architecture. This way you separate the entities, how generate new ones and business logics to consume it that all domaon can share and know.

Steps to follow Layered Architecture: Implement a multi-layered approach with raw, curated, and consumption layers to manage data ingestion, transformation, and access. Data Integration Techniques: Use ETL/ELT processes, data streaming, and data virtualization to integrate data from various sources efficiently. Governance and Security: Establish a robust data governance framework and implement strong security measures to protect data integrity and privacy. Interoperability: Promote the use of open standards, APIs, and connectors to ensure seamless data exchange and compatibility. Performance and Scalability: Design for scalability and optimize performance through scalable infrastructure and continuous performance tuning.

To design a data lakehouse that supports integration across multiple domains, I'll start with a unified platform that combines the flexibility of a data lake with the structured management of a data warehouse. I'll use a modular approach, separating data ingestion, storage, processing, and analytics layers. A strong metadata management system is key for tracking data lineage and ensuring consistency. I'll enforce data governance policies for quality and security. The architecture will include scalable storage, support both real-time and batch processing, and use open data formats for easy integration. Finally, I'll ensure it enables self-service analytics for different domain teams.

Designing a data lakehouse architecture for multi-domain integration involves merging data lake scalability with data warehouse performance. Create a unified platform supporting both structured and unstructured data, with robust data governance and cataloging for security and discoverability. Use a layered approach with raw, cleaned, and curated data layers to manage flow and transformation. Integrate tools that support both real-time and batch processing, and leverage APIs for seamless cross-domain access. Ensure strong access controls and monitoring to maintain data quality and security, enabling effective data integration across domains.

Selecting the appropriate data formats and schemas is crucial for optimizing performance and usability in a data lakehouse. The choice of formats (e.g., Parquet, Avro, JSON) can impact data processing speed and accessibility. For Instance: While working with a financial services firm, we opted for Parquet format for storing structured data due to its efficient columnar storage capabilities. This choice significantly improved query performance for analytics tasks. Additionally, we used JSON for semi-structured data from external APIs, allowing us to maintain flexibility in how we processed and analyzed incoming data.

Implementing robust data pipelines is essential for ensuring that data flows seamlessly into the data lakehouse. These pipelines must handle various tasks, including ingestion, cleansing, validation, and transformation. For Instance: In a project for a logistics company, we built data pipelines using Apache Airflow to orchestrate the ingestion of data from multiple sources, including GPS tracking and warehouse management systems. By automating these workflows, we ensured timely updates to the data lakehouse, enabling real-time insights into shipment statuses and inventory levels. This automation reduced manual errors and improved operational efficiency.

Optimizing data storage and organization is vital for cost efficiency and accessibility. The structure of your data storage can significantly affect how quickly and easily data can be retrieved and analyzed. For Instance: For a healthcare organization, we utilized cloud storage solutions to optimize our data lakehouse architecture. We organized data into logical partitions based on data domains, such as patient records and treatment histories. This partitioning strategy improved query performance and reduced storage costs, as we could easily manage and archive older data without impacting access to current records.

Establishing strong data governance practices is essential for maintaining data quality and compliance. A data lakehouse must incorporate policies and processes that ensure data integrity, security, and accountability. For Instance: In my experience with a government agency, we implemented data governance rules that defined data ownership, stewardship, and compliance requirements. Using tools like Apache Atlas, we tracked data lineage and established clear accountability for data quality. This governance framework not only ensured compliance with regulations but also fostered a culture of data responsibility among users.