Book Image

IBM Cloud Pak for Data

By : Hemanth Manda, Sriram Srinivasan, Deepak Rangarao
3 (1)
Book Image

IBM Cloud Pak for Data

3 (1)
By: Hemanth Manda, Sriram Srinivasan, Deepak Rangarao

Overview of this book

Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise.
Table of Contents (17 chapters)
1
Section 1: The Basics
4
Section 2: Product Capabilities
11
Section 3: Technical Details

Enterprise data architecture

A typical enterprise today has several data stores, systems of record, data warehouses, data lakes, and end user applications, as depicted in the following diagram:

Figure 3.3 – Evolving enterprise data landscape

Also, these data stores are typically distributed across different infrastructures – a combination of on-premises and multiple public clouds. While most of the data is structured, increasingly, we are seeing unstructured and semi-structured datasets being persisted in NoSQL databases, Hadoop, or object stores. The evolving complexity and the various integration touchpoints are beginning to overwhelm enterprises, often making it a challenge for business users to find the right datasets for their business needs. This is represented in the following architecture diagram of a typical enterprise IT, wherein the data and its associated infrastructure is distributed, growing, and interconnected:

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