Cloud data warehouses are great - but ONLY for some analytics workloads. What if you want to include streaming or unstructured data? Or perhaps you want to use the same data for non-Business Intelligence and reporting use cases, such as training and fine-tuning AI/ML models, graph processing, and more. Trying to make your data warehouse fit these use cases will lead to cost overruns, such as integrating with other analytics engines, managing complex ETL pipelines, and storing and maintaining multiple data copies – all of which increase exponentially as data scales in the cloud.
Not anymore: Apache Iceberg finally provides a common standard for data across different use cases and domains. It is open, flexible, and supports data in multiple open file formats. What’s more, is that it is accessible by multiple engines for different use cases. Cloudera’s open data lakehouse powered by Apache Iceberg reduces cloud data warehouse spend, de-duplicates data, lowers risk, supports more use cases, and eases data access and visibility. Best of all, it works well with proprietary cloud data warehouses, so that you can avoid rebuilding applications and the risk of business disruption.
Join this session to see how Cloudera’s data lakehouse:
- Delivers better analytic performance while reducing cloud spend by up to 75%
- Unlocks data for modern AI and ML use cases
- Minimizes risk while simplifying data access
- Reduces ingestion, processing, and storage costs
You will also learn the benefits and limitations of Iceberg’s REST catalog and how you can overcome them. You’ll also learn how you can evaluate your potential savings through a customer case study and TCO analysis for your implementation.