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Delta lake and Lakehouse Architecture

Delta Lake and Lakehouse Architecture: Unified Governance The Data Lakehouse is a novel data management architecture that has surfaced to facilitate artificial intelligence and business intelligence operations on massive quantities […]

Delta lake and Lakehouse Architecture

Delta Lake and Lakehouse Architecture: Unified Governance

The Data Lakehouse is a novel data management architecture that has surfaced to facilitate artificial intelligence and business intelligence operations on massive quantities of data stored in data lakes on low-cost cloud storage. Without requiring a distinct data warehouse, this architecture integrates the most advantageous features of data lakes and data warehouses, including direct file access, native support for Python, data science, and AI frameworks, and SQL and performance capabilities.

Key Features of a Lakehouse:

Recent innovations with the Data Lakehouse architecture can help simplify data and AI workloads, ease collaboration for data teams, and maintain the kind of flexibility and openness that allows your organization to stay agile as you scale. Here are key features to consider when evaluating Data Lakehouse architectures:

Transaction Support: In an enterprise Lakehouse, many data pipelines will often be reading and writing data concurrently. Support for ACID (Atomicity, Consistency, Isolation, and Durability) transactions ensures consistency as multiple parties concurrently read or write data.

Schema Enforcement and Governance: The Lakehouse should have a way to support schema enforcement and evolution, supporting data warehouse schema paradigms such as star/snowflake. The system should be able to reason about data integrity, and it should have robust governance and auditing mechanisms.

Data Governance: Capabilities including auditing, retention, and lineage have become essential, particularly considering recent privacy regulations. Tools that allow data discovery have become popular, such as data catalogs and data usage metrics.

BI Support: Lakehouse allows the use of BI tools directly on the source data. This reduces staleness and latency, improves recency, and lowers costs by not having to operationalize two copies of the data in both a data lake and a warehouse.

Storage Decoupled from Compute: In practice, this means storage and compute use separate clusters, so these systems can scale to many more concurrent users and larger data sizes. Some modern data warehouses also have this property.

Openness: The storage formats, such as Apache Parquet, are open and standardized, so a variety of tools and engines, including machine learning and Python/R libraries, can efficiently access the data directly.

Support for Diverse Data Types (Unstructured and Structured): The Lakehouse can be used to store, refine, analyze, and access data types needed for many new data applications, including images, video, audio, semi-structured data, and text.

Support for Diverse Workloads: Use the same data repository for a range of workloads, including data science, machine learning, and SQL analytics. Multiple tools might be needed to support all these workloads.

End-to-End Streaming: Real-time reports are the norm in many enterprises. Support for streaming eliminates the need for separate systems dedicated to serving real-time data applications.

About presenter

Venkata Jyothi Kotra serves as the Director of Data & AI at Focal CXM and is responsible for the Data & AI Solutions for the Federal Government Agency (PRAC). Additionally, Ms. Kotra serves as an adjunct faculty for Data Analytics at South New Hampshire University.

Ms. Kotra oversees data modernization, data architecture, data governance, and enterprise data modeling activities. She is a project leader with expertise integrating data, analytics, and machine learning to create effective data-driven decisions and gain a competitive edge in sales, marketing, finance, and operations. Ms. Kotra is a trailblazer in next-generation technological solutions such as big data, cloud computing, distributed data processing, system upgrades, and business process transformation.

In her presentation, “Data Lakehouse: Simplifying Data Engineering Analysis and Artificial Intelligence,” Ms. Kotra would want to impart the knowledge of successful application of Lakehouse architecture.

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