Data lake solutions

Power your applications, analytics and AI with any data in an open cloud data lake

Isometric illustration

Overview

Data lake solutions and IBM

A data lake is a centralized repository for managing extremely large data volumes. It serves as a foundation for collecting and analyzing structured, semistructured, and unstructured data in its native format to drive new insights, better predictions, and improved optimization. Unlike traditional data warehouses, data lakes can process video, audio, logs, texts, social media, sensor data and documents to power apps, analytics, and AI. Data lakes can be built as part of a data fabric architecture to provide the right data, at the right time, regardless of where it is resides.

A data lakehouse is an evolution in analytic data repositories that supports data acquisition to refinement, delivery and storage with open data and open table formats. IBM enables you to get more from your existing investments in data warehouses and data lakes by building data lakehouse access to a larger variety of data for increased flexibility.


Benefits

Improve customer experience

Understand and anticipate customer behaviors with complete, governed insights.

Streamline operations

Spot patterns and trends to reduce waste and overhead through diverse analytic and AI techniques.

Manage governance, risk and compliance

Promote auditability and transparency with metadata-powered, native data access in a governed data lake.

Increase agility and productivity

Speed time to value with self-service data exploration and discovery for any user.

Unify users, tools and repositories

Increase collaboration, and reduce the time and cost of managing disparate systems and tools in an integrated environment.

Harness open source and existing skills

Turn your open-source and ecosystem investments into innovation opportunities with enterprise-ready secure data lakes.

Capabilities

Core technology for high-value use cases

Reuse the data lake for 360° customer and operational intelligence, governance, and risk and compliance reporting.

End-to-end data management

Ingest and integrate with transactional, operational, and analytical data to promote a complete insight.

Flexibility to handle analytic and AI needs

Extend a data fabric to provide right data at the right time on a common foundation for staging, storage and access.

Metadata, queries and data curation

Build and maintain a data foundation that powers data cataloging, curation, exploration, and discovery needs.

Scale with access to virtually any data

Take a hybrid, multicloud approach to access any data from any locations from years of records to real-time data.

Ease of extending your data warehouse

Integrate and expand analytics across multiple data repositories to drive innovation and optimization at scale.

Why IBM?

Enterprise-ready from day one

Rely on scale, security, resiliency and flexibility of IBM data lakes that helps run the world’s most mission-critical environments.

Simplify procurement deployment

Enjoy a one-stop shop at IBM including support, IBM ecosystem, and open-source tooling.

World-class data and AI innovation and experience

Partner with IBM industry experts who have in-depth experience and know-hows in successful deployments.

Foundation

The IBM data lake approach IBM takes a cloud-based, open approach to our data lake solution, building on the following principles. Embedded governance

Rely on a data lake governance that houses raw structured and unstructured data — trusted, secured, and governed — with automated privacy and security anywhere.

Automated integration

Use data integration tools such as ETL, data replication, and data virtualization combine data from disparate sources into valuable data sets.

Virtualization

Query data directly in the data lake without duplication or movement with the data virtualization of IBM Watson® Query.

Data lake solutions

Case study

ING Bank carries out its data fabric vision

ING’s centralized governed data lake seemed to serve its organizational and regulatory needs — but their chief AI architect wanted more out of this business-critical environment. The amount of manual work, the number of subject matter experts, and the associated maintenance costs became inhibitors to getting more data into the data lake.


Partner spotlight

Better together: Cloudera and IBM

Cloudera and IBM work together to help you build a data lake for analytics and AI. You can collect, store, govern and secure raw data from across your business anywhere on premises or on any cloud. Cloudera Data Platform is available through a one-stop shop at IBM to help you simplify licensing, procurement, support and deployment.


Data lake use cases

Financial services

Improve customer targeting, make better informed underwriting decisions and provide better claims management while mitigating risk and fraud.

Read the brief (492 KB)
Healthcare

Improve direct patient care, the customer experience, and administrative, insurance and payment processing while responding quicker to emerging diseases.

Read the brief (1.3 MB)
Communications service providers

Optimize network monitoring, management and performance to help mitigate risk and reduce costs and improve customer targeting and service.

Read the brief (819 KB)

Get started

Set up a no-cost, one-on-one call with IBM to explore data lake solutions.

Learn more What is a data lake? What is big data analytics? What is Hadoop? What is Apache Spark? What is a data mart? What is a relational database? What is ETL? What is data management? What is a data fabric?