Data Engineering Weekly

Lakehouse architecture represents a major evolution in data engineering. It combines data lakes' flexibility with data warehouses' structured reliability, providing a unified platform for diverse data workloads ranging from traditional business intelligence to advanced analytics and machine learning. Roy Hassan, a product leader at Upsolver, now Qlik, offers a comprehensive reality check on Lakehouse implementations, shedding light on their maturity, challenges, and future directions.

Defining Lakehouse Architecture

A Lakehouse is not a specific product, tool, or service but an architectural framework. This distinction is critical because it allows organizations to tailor implementations to their needs and technological environments. For instance, Databricks users inherently adopt a Lakehouse approach by storing data in object storage, managing it with the Delta Lake format, and analyzing it directly on the data lake.

Assessing the Maturity of Lakehouse Implementations

The adoption and maturity of Lakehouse implementations vary across cloud platforms and ecosystems:

Databricks: Many organizations have built mature Lakehouse implementations using Databricks, leveraging its robust capabilities to handle diverse workloads.

Amazon Web Services (AWS): While AWS provides services like Athena, Glue, Redshift, and EMR to access and process data in object storage, many users still rely on traditional data lakes built on Parquet files. However, a growing number are adopting Lakehouse architectures with open table formats such as Iceberg, which has gained traction within the AWS ecosystem.

Azure Fabric: Built on the Delta Lake format, Azure Fabric offers a vertically integrated Lakehouse experience, seamlessly combining storage, cataloging, and computing resources.

Snowflake: Organizations increasingly use Snowflake in a Lakehouse-oriented manner, storing data in S3 and managing it with Iceberg. While new workloads favor Iceberg, most existing data remains within Snowflake’s internal storage.

Google BigQuery: The Lakehouse ecosystem in Google Cloud is still evolving. Many users prefer to keep their workloads within BigQuery due to its simplicity and integrated storage.

Despite these differences in maturity, the industry-wide adoption of Lakehouse architectures continues to expand, and their implementation is becoming increasingly sophisticated.

Navigating Open Table Formats: Iceberg, Delta Lake, and Hudi

Discussions about open table formats often spark debate, but each format offers unique strengths and is backed by a dedicated engineering community:

Iceberg and Delta Lake share many similarities, with ongoing discussions about potential standardization.

Hudi specializes in streaming use cases and optimizing real-time data ingestion and processing. [Listen to The Future of Data Lakehouses: A Fireside Chat with Vinoth Chandar - Founder CEO Onehouse & PMC Chair of Apache Hudi]

Most modern query engines support Delta Lake and Iceberg, reinforcing their prominence in the Lakehouse ecosystem. While Hudi and Paimon have smaller adoption, broader query engine support for all major formats is expected over time.

Examining Apache XTable’s Role

Apache XTable aims to improve interoperability between different table formats. While the concept is practical, its long-term relevance remains uncertain. As the industry consolidates around fewer preferred formats, converting between them may introduce unnecessary complexity, latency, and potential points of failure—especially at scale.

Challenges and Criticisms of Lakehouse Architecture

One common criticism of Lakehouse architecture is its lower abstraction level than traditional databases. Developers often need to understand the underlying file system, whereas databases provide a more seamless experience by abstracting storage management. The challenge is to balance Lakehouse's flexibility and traditional databases' ease of use.

Best Practices for Lakehouse Adoption

A successful Lakehouse implementation starts with a well-defined strategy that aligns with business objectives. Organizations should:

• Establish a clear vision and end goals.

• Design a scalable and efficient architecture from the outset.

• Select the right open table format based on workload requirements.

The Significance of Shared Storage

Shared storage is a foundational principle of Lakehouse architecture. Organizations can analyze data using multiple tools and platforms by storing it in a single location and transforming it once. This approach reduces costs, simplifies data management, and enhances agility by allowing teams to choose the most suitable tool for each task.

Catalogs: Essential Components of a Lakehouse

Catalogs are crucial in Lakehouse implementations as metadata repositories describing data assets. These catalogs fall into two categories:

Technical catalogs, which focus on data management and organization.

Business catalogs, which provide a business-friendly view of the data landscape.

A growing trend in the industry is the convergence of technical and business catalogs to offer a unified view of data across the organization. Innovations like the Iceberg REST catalog specification have advanced catalog management by enabling a decoupled and standardized approach.

The Future of Catalogs: AI and Machine Learning Integration

In the coming years, AI and machine learning will drive the evolution of data catalogs. Automated data discovery, governance, and optimization will become more prevalent, allowing organizations to unlock new AI-powered insights and streamline data management processes.

The Changing Role of Data Engineers in the AI Era

The rise of AI is transforming the role of data engineers. Traditional responsibilities like building data pipelines are shifting towards platform engineering and enabling AI-driven data capabilities. Moving forward, data engineers will focus on:

• Designing and maintaining AI-ready data infrastructure.

• Developing tools that empower software engineers to leverage data more effectively.

Final Thoughts

Lakehouse architecture is rapidly evolving, with growing adoption across cloud ecosystems and advancements in open table formats, cataloging, and AI integration. While challenges remain—particularly around abstraction and complexity—the benefits of flexibility, cost efficiency, and scalability make it a compelling approach for modern data workloads.

Organizations investing in a Lakehouse strategy should prioritize best practices, stay informed about emerging trends, and build architectures that support current and future data needs.

All rights reserved ProtoGrowth Inc, India. I have provided links for informational purposes and do not suggest endorsement. All views expressed in this newsletter are my own and do not represent current, former, or future employers’ opinions.