June 23, 2021 By Hemanth Manda 4 min read

When’s the last time you considered if you’re operating in a truly predictive enterprise, furthermore, if it’s easy for your data consumers, models and apps to access the right data? More often than not the answer is a resounding “not very”. Between the proliferation of data types and sources and tightening regulations, data is often held captive, sitting in silos. Traditionally, strategies for overcoming this challenge relied on consolidating the physical data into a single location, structure and vendor. While this strategy seemed great in theory, anyone that has undertaken a migration of this magnitude can tell you it’s easier said than done.

Earlier this year at THINK we unveiled our plans for the next generation of IBM Cloud Pak for Data, our alternative to help customers connect the right people to the right data at the right time. Today, I’m excited to share more details on how the latest version of the platform, version 4.0, will bring that vision to life through an intelligent data fabric.

The journey so far

Since the launch of IBM Cloud Pak for Data in 2018, our goal has always been to help customers unlock the value of their data and infuse AI throughout their business. Understanding the needs of our clients, we doubled down on delivering a first-of-its-kind containerized platform that provided flexibility to deploy the unique mix of data and AI services a client needs, in the cloud environment of their choice.

IBM Cloud Pak for Data supports a vibrant ecosystem of proprietary, third party and open source services that we continue to expand on with each release. With version 4.0 we take our efforts to the next level. New capabilities and intelligent automation help business leaders and users tackle the overwhelming data complexity they face to more easily scale the value of their data.

Weaving the threads of an intelligent data fabric

A data fabric is an architectural pattern that dynamically orchestrates disparate data sources across a hybrid and multicloud landscape to provide business-ready data in support of analytics, AI and applications. The modular and customizable nature of IBM Cloud Pak for Data offers the ideal environment to build a data fabric from best-in-class solutions that is tailored to your unique needs. The tight integration of the microservices within the platform allow for further streamlining of the management and usage of distributed data by infusing intelligent automation. With version 4.0 we’re applying this automation in three key areas:

  1. Data access and usability – AutoSQL is a universal query engine that automates how you access, update and unify data across any source or type (clouds, warehouses, lakes, etc.) without the need for data movement or replication. With AutoSQL you can query distributed data across disparate landscapes up to 8x faster than the standard data warehouse.
  2. Data ingestion and cataloging – AutoCatalog automates the discovery and classification of data to streamline the creation of a real-time catalog of data assets and their relationships across disparate data landscapes.
  3. Data privacy and security – AutoPrivacy uses AI to intelligently automate the identification, monitoring and enforcement of sensitive data across the organization to help minimize risk and ensure compliance.

Register for the webinar to learn more about our intelligent data fabric and how you can take advantage of these new technologies.

Additional enhancements woven into 4.0

Further augmenting the intelligent automation of our data fabric capabilities is another new service coming to IBM Cloud Pak for Data, IBM Match 360 with Watson. Match 360 provides a machine learning-based, easy to use experience for self-service entity resolution. Non-developers can now match and link data from across their organization, helping to improve overall data quality.

IBM SPSS Modeler, IBM Decision Optimization and Hadoop Execution Engine services are also included as part of IBM Cloud Pak for Data 4.0. These capabilities complement the IBM Watson Studio services already within the base and enables users such as business analysts and citizen data scientists, to participate in building AI solutions.

AutoAI is enhanced to support relational data sources and generate exportable python code, enabling data scientists to review and update models generated through AutoAI. This is a significant differentiator compared to the AutoML capabilities of competitors, where the generated model is more of a black box.

Complementary capabilities are also released on IBM Cloud Pak for Data as a Service, including IBM DataStage and IBM Data Virtualization. Now available fully managed, DataStage helps enable the building of modern data integration pipelines, and the Data Virtualization capability helps to share data across the organization in near real-time, connecting governed data to your AI and ML tools.

Finally, IBM Cloud Pak for Data 4.0 includes several platform enhancements, most notable of which. is the addition of Red Hat OpenShift Operators. These help to automate the provisioning, scaling, patching and upgrades of IBM Cloud Pak for Data. First time installs are significantly simplified, decreasing the cost of implementation, while seamless upgrades reduce the upgrade process from weeks to hours. Also beginning in 4.0, IBM Cloud Pak for Data is built on a common IBM Cloud Pak platform, enabling standardized Identify and Access Management and seamless navigation across all of the IBM Cloud Paks.

Data is a huge competitive advantage for companies and when combined with AI, has the power to drive business transformation. IBM Cloud Pak for Data enables just that, but with the potential to be 10x faster due to new built-in automation.

Learn more about the latest version of IBM Cloud Pak for Data by signing up for the Data Fabric Deep Dive webinar or by registering for a free trial.

Was this article helpful?

More from Cloud

Announcing Dizzion Desktop as a Service for IBM Virtual Private Cloud (VPC)

2 min read - For more than four years, Dizzion and IBM Cloud® have strategically partnered to deliver incredible digital workspace experiences to our clients. We are excited to announce that Dizzion has expanded their Desktop as a Service (DaaS) offering to now support IBM Cloud Virtual Private Cloud (VPC). Powered by Frame, Dizzion’s cloud-native DaaS platform, clients can now deploy their Windows and Linux® virtual desktops and applications on IBM Cloud VPC and enjoy fast, dynamic, infrastructure provisioning and a true consumption-based model.…

Microcontrollers vs. microprocessors: What’s the difference?

6 min read - Microcontroller units (MCUs) and microprocessor units (MPUs) are two kinds of integrated circuits that, while similar in certain ways, are very different in many others. Replacing antiquated multi-component central processing units (CPUs) with separate logic units, these single-chip processors are both extremely valuable in the continued development of computing technology. However, microcontrollers and microprocessors differ significantly in component structure, chip architecture, performance capabilities and application. The key difference between these two units is that microcontrollers combine all the necessary elements…

Seven top central processing unit (CPU) use cases

7 min read - The central processing unit (CPU) is the computer’s brain, assigning and processing tasks and managing essential operational functions. Computers have been so seamlessly integrated with modern life that sometimes we’re not even aware of how many CPUs are in use around the world. It’s a staggering amount—so many CPUs that a conclusive figure can only be approximated. How many CPUs are now in use? It’s been estimated that there may be as many as 200 billion CPU cores (or more)…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters