Cloudera customers run some of the biggest data lakes on earth. These lakes power mission-critical, large-scale data analytics and AI use cases—including enterprise data warehouses. Nearly two years ago, Cloudera announced the general availability of Apache Iceberg in the Cloudera platform, which helps users avoid vendor lock-in and implement an open lakehouse. With an open data lakehouse powered by Apache Iceberg, businesses can better tap into the power of analytics and AI.

Data lakes can save your enterprise money but come with unique challenges. These tips will help you overcome three of the biggest issues.

Why data lakes are the essential centerpiece of modern data architectures and an indispensable prerequisite for AI.

Artificial Intelligence (AI) is primed to reshape the way just about every business operates. Cloudera research projected that more than one third (36%) of organizations in the U.S. are in the early stages of exploring the potential for AI implementation. But even with its rise, AI is still a struggle for some enterprises. AI, and any analytics for that matter, are only as good as the data upon which they are based. And that’s where the rub is.

A data lake is a main storage repository that can hold vast amounts of raw, unstructured data. A data lake is not the same as a data warehouse, which maintains data in structured files. Five key takeaways about data lake tools: A data warehouse uses a hierarchical structure, whereas the architecture of a data lake is flat.

Apache Iceberg’s ecosystem of diverse adopters, contributors and commercial support continues to grow, establishing itself as the industry standard table format for an open data lakehouse architecture. Snowflake’s support for Iceberg Tables is now in public preview, helping customers build and integrate Snowflake into their lake architecture. In this blog post, we’ll dive deeper into the considerations for selecting an Iceberg Table catalog and how catalog conversion works.

For customers using Microsoft’s Data Lake offerings, they can securely land data in Delta Lake format from any one of Fivetran’s 400+ source connectors.

The importance of a well-structured data lake architecture cannot be overstated. As businesses work with an ever-increasing influx of data, the need for a robust, scalable, and efficient data storage solution becomes crucial. Let’s explore Data Lake Architecture Design—a concept revolutionizing how enterprises store, access, analyze, and compute their data.