Traditional data management approaches store data in disparate databases, often with data duplication across systems and time consuming, risky, and expensive data integration and processing. Getting reliable data without friction is key in achieving successful Generative AI. is a data lakehouse architecture built with open standards that support both traditional SQL-derived analytics and AI driven insights with automation in a single platform, supporting the needs of different data users and a broad variety of enterprise workloads. 

Think 2024 announcements, now GA! 

IBM announced several upcoming updates for new and exciting capabilities in IBM®™at Think 2024, our annual event that brings together over 5,000 technology pioneers and leaders. These new features are now generally available in   

IBM introduced a new query engine within IBM, Presto C++, along with an integrated query optimizer featuring enterprise-proven query compilation technology and advanced query rewrite and cost-based optimization techniques. In other words, has been enhanced for fast query performance at optimized costs. 

IBM with Presto C++ v0.286 and query optimizer on IBM Storage Fusion HCI, tested internally by IBM, was able to deliver better price performance compared to Databrick’s Photon engine, with equal query runtime at less than 60% of the cost, derived from public 100 TB TPC-DS Query benchmarks.* 

IBM clients can now unlock transactional mainframe data for artificial intelligence (AI) and analytics with IBM Data Gate for watsonx™ integrated with IBM This revolutionizes the way organizations synchronize, analyze and build AI models from data originating on IBM Z®.  

By bringing transactional data from the mainframe into an open, governed data lakehouse such as, enterprises can readily build AI models to grow revenue, enhance productivity and manage costs. 

IBM Knowledge Catalog announced a Gen-AI infused semantic layer, embeddable into IBM When embedded, the semantic layer generates data enrichments that enable clients to find and understand previously cryptic, structured data across their data estate in natural language through semantic search. This accelerates data discovery to unlock insights faster, without requiring SQL. 

IBM also announced enhanced integrations with IBM® Db2® database, Db2® Warehouse, IBM® Netezza® and Informix® with, and support for open formats such as Apache Iceberg to unify and share a single copy of data and metadata across the hybrid cloud without needing to migrate or re-catalog. With these integrations, clients can query data from their IBM databases across multiple engines to prepare data for AI. 

IBM also announced IBM Data Product Hub, a new solution for repeatable data sharing between internal data producers and data consumers, now generally available. IBM Data Product Hub users can connect to IBM and package relevant metadata to create a repeatable, governed data product. That data product can then deliver the right data for various AI use cases across the organization at scale, without the need for repeated, manual workflows.  

What our clients say about 

IBM was thrilled to highlight some of the amazing work our clients are doing with on stage at Think this year. Themes included scalability, data governance and management, and speed to value.  

“Our architecture was a monolithic data lake, and then we started a migration to IBM Many of the things we were struggling with, we got from the platform: workload isolation, data governance, and the most important part, having the confidence that IBM scales really well with our growth.”   —Toomas Römer, VP of Engineering at Bolt 
“One of the things IBM does really well is scale. They have great data governance and data management strategies that are prebuilt into the platform so that we can enable every data steward, every data engineer, and every data scientist to very quickly get access to all the data that’s required.”  —Dr. Stephan Gerali, Senior Fellow at Lockheed Martin 
“IBM’s hybrid cloud and Bhuma’s real-time analytics and app platform are paving the way for us to leverage and IBM®™ without the need to transition away from our existing AWS environment. This seamless integration will be a game-changer for us, enabling us to maintain operational efficiency, cost-effectiveness and at the same time leverage IBM and Bhuma’s tech stacks.”   —Srikanth Radhakrishnan, Senior Vice President of Engineering at Tyfone 
“Bhuma is thrilled to be an IBM ecosystem partner, helping our customers build real-time data apps, analytics, and AI agents delivering modern experiences with insights-actions-outcomes on a modern lakehouse. Many of our customers, such as Tyfone, are deploying IBM for the best price-performance for real-time queries, flexibility to deploy on-prem and in the cloud of your choice to meet security and regulation, and the ability to integrate into the watsonx ecosystem with assistants and governance.”  —Srini Gurrapu, Founder and CEO at Bhuma Inc. 
“The combination of AI and mainframe data has many clients intrigued, as many of them now have the integrated AI acceleration unit of the IBM® z16®, which they would like to exploit. I am seeing a lot of discussion and activity in individual workshops. Clients want to understand how technologies like Data Gate for watsonx can help them. It’s becoming top of mind in many mainframe shops today.” —Leonard J. Santalucia, CTO and Business Development Manager at Vicom Infinity 

Get started with IBM  

Try yourself with a free trial or book a meeting with an IBM product specialist. Interested in diving deeper into Think announcements? Watch the watsonx keynote

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*Based on IBM internal testing of Presto C++ 0.286 on a hyperconverged infrastructure setup with 1 master + 75 worker nodes, 1009 vCPUs, 18 TB memory, 344.8 TB of file system storage, distributed RAID and 50 GB network compared to public Databricks 100TB TPC-DS. Query benchmarks published in 2021 with 1 master + 256 worker nodes, 2112 vCPUs, 16.1 TB Memory, 528.2 TB of total storage and 10 GB Network. Pricing calculations are based on IBM pricing as of 7 May 2024 and Databricks published pricing for Photon as of 7 May 2024. Results are based on testing conditions and pricing as of the dates shown. Actual costs and performance can vary depending on individual client configurations and conditions. Results are derived from the Databricks SQL 8.3 benchmark and as such is not comparable to published Databricks SQL 8.3 benchmark results, as results do not comply with the Databricks SQL 8.3 benchmark specification. 

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