Data lakes and data lakehouses provide a centralized repository for managing large data volumes. They serve as a foundation for collecting and analyzing structured, semi-structured, and unstructured data in its native format for long-term storage and to drive insights and predictions. Unlike traditional data warehouses, they can process video, audio, logs, texts, social media, sensor data, and documents to power apps, analytics, and AI. They can also be built as part of a data fabric architecture to provide the right data, at the right time, regardless of where it is resides.
Hadoop-based data lakes were an attempt to address these new workloads, but required hard-to-find skills for developing applications and managing the platforms. Data lakes are largely being supplanted by a new architectural approach called a data lakehouse.
Scale AI workloads, for all your data, anywhere
How to resolve today’s data challenges with a lakehouse architecture
Reduce cost and time to insight, and enhance confidence and trust in data used for applications, analytics, and AI with a modern data architecture. Identify new patterns and trends to improve operations and deliver new offerings.
Access existing data lakes and data warehouses on-premises or in the cloud, and integrate them with new data to unlock insights and opportunity with a modern data lakehouse and data fabric approach.
Deliver business value and reduce data management complexity. Start small and scale across use cases and deployments (cloud, hybrid, and on-premises).
Control data privacy and security with built-in governance and metadata management. Manage centrally and deploy globally with enterprise-wide governance solutions.
Partner with IBM to accelerate deployments across hybrid and multi-cloud environments. Support all types of data and use cases with open source, open standards, and interoperability with IBM and 3rd party services.
Take advantage of lower cost compute and storage, and fit-for-purpose analytics engines that dynamically scale up and down—pairing the right workload with the right analytic engine.
Rely on scale, security, resiliency and flexibility of IBM data lakes that helps run the world’s most mission-critical environments.
IBM is trusted to manage the world’s most mission-critical data and applications. Our experience of innovation in enterprise data solutions includes market-making database technology and enterprise ready-AI.
We enable our clients to run our solutions on cloud or in on-premises environments and believe that our client’s data solely belongs to them.
Optimize warehouse workloads using fit-for-purpose query engines including Presto and Spark that support all data types and workload needs. Modernize data lakes with warehouse-like capabilities.
Access and share a single copy of data supported by multiple engines and integrated metadata, eliminating duplication and data silos.
Deploy anywhere with full support for hybrid-cloud and multi cloud environments.
Reduce cost and time to insight and enhance trust and confidence in data and decisions with an open data lakehouse.
Activate business-ready data for AI and analytics with intelligent cataloging, backed by active metadata and policy management.
Connect the right data to the right people at the right time with IBM and third-party services spanning the data lifecycle.
Query across Hadoop, object storage, and data warehouses with a hybrid SQL-on-Hadoop engine.
Harness the power of transactional, operational, and analytic data for mission-critical environments.
Achieve simplicity, scalability, speed and sophistication — all deployable as a service, on the cloud and on premises.
Learn about a modern solution to distributed data landscapes: the data lakehouse.
The real-world challenges organizations are facing with big data today are multi-faceted.
Today's data challenges require a new strategic approach to data management.