Data lakehouse automation

A data lakehouse, as its definition implies, is a hybrid data architecture that combines the flexibility and scalability of a data lake with the structured querying capabilities of a data warehouse.

It can handle a variety of workloads (batch or streaming) and sources (structured, semi-, or unstructured) to bring all the data together under one roof.

This integrated approach provides organizations a unified platform for various data-driven activities such as machine learning, business intelligence, and predictive analytics.

The data warehouse serves as an integrated, time-variant repository for structured data, offering a predefined model tailored for business intelligence. Analysts and managers rely on the data warehouse to gain a comprehensive business view, aiding in informed decision-making processes. It acts as a central hub where data from transactional systems, relational databases, and other sources flow in, providing a wealth of information for analysis and decision support.

The data lake is a storage hub for all types of data—structured, semi-structured, and unstructured—kept in its original format. Initially hosted on-premises using Apache Hadoop, these lakes have shifted to cloud-based object stores. They hold vast amounts of data, perfect for big data tasks like machine learning and predictive analytics. However, they require specialized skills in data science for effective use and can suffer from data quality issues if not maintained properly. Real-time querying is also challenging as data needs to be processed before use.

The data lakehouse blends the flexibility and cost-efficiency of data lakes with the performance and ACID transactions of data warehouse (Atomic, Consistent, Isolated, and Durable). It is designed to support both real-time processing for immediate insights and batch processing for large-scale analysis.

The data lakehouse architecture is a unified, open architecture for integration, storage, processing, governance, sharing, analytics and AI.

A commonly used data management best practice is to organize data into three layers: Bronze (raw ingestion or landing), Silver (curated zone), and Gold (business reporting grade). This reference architecture is often referred to as the lakehouse medallion architecture.

A lakehouse can store huge volumes of data on the same low-cost cloud object storage as data lakes and eliminates the need to maintain both a data warehouse and a data lake. Storage is decoupled from compute.

A lakehouse is designed to integrate streaming and batch data within a unified platform and data model, even if these integrations occur at varying speeds. The same applies to centralizing structured, unstructured, and semi-structured data.

All data need to be processed and transformed into one data lakehouse model, preferably a Data Vault model. Automation accelerates the build and design of that comprehensive target model and the generation of all the different loading patterns to populate the same model.

Automation does not stop at supplying the code to instruct the loading patterns, though; CI/CD pipelines can be automated so that no time is lost deploying it.

• Eliminating data silos: One single repository for both structured and unstructured data.
• Lower costs: Storing data on low-cost storage and eliminating the need for separate data warehouses and data lakes reduces overall costs.
• Increased reliability: Data lakehouses minimize ETL data transfers, reducing the risk of quality or technical issues during data movement.
• Diverse workloads: Data lakehouses support various workloads including analytics, SQL, machine learning, and data science from a single repository.
• High scalability: Data lakehouses offer nearly limitless scalability by decoupling compute from storage.
• Better data governance: Centralizing resources and data sources in a data lakehouse enables better control over security, access, and management.
• Improved data quality: Enforcing schemas and reducing data availability time ensures fresher and more consistent data in data lakehouse architectures. And there are no inconsistencies caused by data duplication.