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.
How to resolve today’s data challenges with a lakehouse architecture
Scale AI workloads, for all your data, anywhere
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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.
Watsonx.data makes it possible for enterprises to scale analytics and AI with a fit-for-purpose data store, built on an open lakehouse architecture, supported by querying, governance and open data formats to access and share data. With watsonx.data, you can connect to data in minutes, quickly get trusted insights and reduce your data warehouse costs. Now available as a service on IBM Cloud and AWS and as containerized software.
Reduce cost and time to insight and enhance trust and confidence in data and decisions with an open data lakehouse.
IBM and Cloudera have partnered to create industry-leading, enterprise-grade data and AI services using open source ecosystems—all designed to achieve faster data and analytics at scale
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.
Learn about the fast and flexible open-source query engine available with watsonx.data.
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.
Tackling AI’s data challenges with IBM databases on AWS.