When a leading telecommunications operator hit the scaling wall with their legacy Hive infrastructure, managing petabyte-scale customer data across billions of records became untenable. Their IDPR workloads suffered from slow queries, rising storage costs,partition explosion, and schema changes that broke downstream systems.

This session explains why they chose Apache Iceberg and how it transformed their architecture, including the business and technical decision criteria used to select Iceberg over other open table formats

Key Takeaways: Why Iceberg: ACID guarantees, hidden partitioning, time travel, interoperability, and the executive case that secured buy-in Architecture & Best practices: A high level architecture plus Cloudera-recommended patterns for Iceberg + Impala, partitioning strategies, compaction policies, metadata optimization, and query engine tuning for workloads Measurable Impact: Faster queries, significant storage and infrastructure cost reductions, simpler operations, and improved regulatory compliance.

Speakers:

Akshat Mathur: Senior Product Manager @ Cloudera

With over 8 years of dedicated experience in the dynamic field of data engineering, Helped architecting and implementing robust data infrastructure for a variety of organizations, contributing to their success in harnessing the power of data, and now managing product strategy for Cloudera’s Data Lakehouse

Akshat has made contributions towards Apache Hive and Tez

Bill Zhang: VP Product, Cloudera, Lakehouse , Iceberg Integration

Bill is vice president of product management at Cloudera, responsible for Open Data Lakehouse product strategy and Apache Iceberg integration with all Cloudera Data Platform (CDP) form factors. Bill also leads Apache Hive product roadmap and adoption. Most recently, Bill was responsible for solution management for SAP HANA Data Platform and SAP HANA Cloud. Prior to that he led product management for Sybase Replication Server.