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.
One of the primary benefits of deploying AI and analytics within an open data lakehouse is the ability to centralize data from disparate sources into a single, cohesive repository. By leveraging the flexibility of a data lake and the structured querying capabilities of a data warehouse, an open data lakehouse accommodates raw and processed data of various types, formats, and velocities. This unified data environment eliminates the need for maintaining separate data silos and facilitates seamless access to data for AI and analytics applications.
Here’s what implementing an open data lakehouse with Cloudera delivers:
- Integration of Data Lake and Data Warehouse: An open data lakehouse brings together the best of both worlds by integrating the storage flexibility of a data lake with the query performance and structured querying capabilities of a data warehouse.
- Openness: The term “open” in open data lakehouse signifies interoperability and compatibility with various data processing frameworks, analytics tools, and programming languages. This openness promotes collaboration and innovation by empowering data scientists, analysts, and developers to leverage their preferred tools and methodologies for exploring, analyzing, and deriving insights from data. Whether it’s traditional SQL-based querying, advanced machine learning algorithms, or complex data processing workflows, an open data lakehouse provides a flexible and extensible platform for accommodating diverse analytics workloads.
- Scalability and Flexibility: Like traditional data lakes, an open data lakehouse is designed to scale horizontally, accommodating large volumes of data from diverse sources. It provides flexibility in storing both raw and processed data, allowing organizations to adapt to changing data requirements and analytical needs. As data volumes grow and analytical needs evolve, organizations can seamlessly scale their infrastructure horizontally to accommodate increased data ingestion, processing, and storage demands. This scalability ensures the data lakehouse remains responsive and performant, even as data complexity and usage patterns change over time.
- Unified Data Platform: An open data lakehouse serves as a unified platform for data storage, processing, and analytics, eliminating the need for maintaining separate data silos and ETL (Extract, Transform, Load) processes. Deploying AI and analytics within an open data lakehouse promotes data democratization and self-service analytics, empowering users across the organization to access, analyze, and derive insights from data autonomously. By providing a unified and accessible data platform, organizations can break down data silos, democratize access to data and analytics tools, and foster a culture of data-driven decision-making at all levels. This democratization of data and analytics enhances organizational agility and competitiveness and promotes a more collaborative and data-literate workforce.
- Support for Modern Analytics Workloads: With support for both SQL-based querying and advanced analytics frameworks (e.g., machine learning, graph processing), an open data lakehouse caters to a wide range of analytics workloads, from ad-hoc querying to complex data processing and predictive modeling.
Open data lakehouse architecture represents a modern approach to data management and analytics, enabling organizations to harness the full potential of their data assets while embracing openness, scalability, and interoperability.
Learn more about the Cloudera Open Data Lakehouse here.
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