Eberhard Hechler

This presentation describes guidelines to implement a hybrid cloud data fabric with IBM Z, using the Db2 Analytics Accelerator, Db2 Data Gate, and Cloud Pak for Data. We start with a description of data fabric as an emerging data management design concept in support of different data integration styles for operational and analytical use cases. Highlighting the various data fabric capabilities based on Cloud Pak for Data, we provide guidance on how the Db2 Analytics Accelerator and Db2 Data Gate can be used in data fabric scenarios providing access and consumption of Z-data, specifically for Db2 for z/OS data. Looking at data fabric through the lens of IBM Z, a number of application areas and entry points are described, where the Db2 Analytics Accelerator and Db2 Data Gate can be used. 

Especially with Data Gate v2.0 now available and with the EoM announcement of IDAA on IIAS, I'd like to provide a technical update and - most importantly - the roadmap of these 2 products.

Detecting fraud within payment systems is one of the top challenges for many IT organizations. Integrating fraud detection applications with payment systems can be complex. IBM and TAS/Mantica have just simplified this effort. IBM has partnered with TAS/Mantica to bring together the power of their Adaptive Intelligence Platform and Fraud Protection Solution with IBM Watson Machine Learning for z/OS. This helps organizations monitor and detect payment fraud in real-time, before a transaction completes, with rel-time scoring via Watson ML for z/OS. This presentation describes how to apply this ML use case for real-time scoring to your payment process. 

Detecting fraud within payment systems is one of the top challenges for many IT organizations. Integrating fraud detection applications with payment systems can be complex. IBM and TAS/Mantica have just simplified this effort. IBM has partnered with TAS/Mantica to bring together the power of their Adaptive Intelligence Platform and Fraud Protection Solution with IBM Watson Machine Learning for z/OS. This helps organizations monitor and detect payment fraud in real-time, before a transaction completes, with rel-time scoring via Watson ML for z/OS. This presentation describes how to apply this ML use case for real-time scoring to your payment process. 

This presentation describes the key technical aspects to develop new types of data scientist related applications on IBM Z, and provides sample analytical insight for IT Operational Analytics (ITOA) that is derived from various client engagements. It highlights the current and future integration of Machine Learning for z/OS with the Db2 Analytics Accelerator. The use case scenarios include anomaly detection from SMF data, correlation discovery among SMF measures, and predictive analytics for instance related to batch job elapsed times. The presentation is complemented with an introduction in Machine Learning for z/OS and a short demo with some use case scenarios.

This presentation describes various integration scenarios of the Db2 Analytics Accelerator. This includes integration best practices to use the Accelerator with Big SQL on the Hortonworks HDP platform, enabling SQL on Hadoop with Db2 for z/OS underpinned with the Accelerator as a federated data source. Furthermore, we describe the integration of the Accelerator with Machine Learning (ML) for z/OS and Data Science Experience (DSX) Local, and the benefit it represents for specific use cases. This session provides feedback and concrete scenarios from client engagements

The data lake is a solution for a system of insight that has gained strong momentum with clients. This presentation describes the deployment of a data lake in the context of z Systems analytics. Data lakes are usually associated with offloading large data volumes to an open source platform. We present DB2 for z/OS and the DB2 Analytics Accelerator deployment options with the data lake, and illustrate how the DB2 Analytics Accelerator enables more choices for making DB2 for z/OS and other z Systems data available for analytics while keeping z Systems within the analytics lifecycle. This limits data movement and performs analytics close to where the data originates and resides.  

Cognitive analytics has become an increasingly important business imperative across industries. This presentation describes the evolution of analytics and the key challenges in implementing cognitive analytics. It furthermore provides an overview of what IBM has to offer and details out a few use cases and industry opportunities. Last but not least, it describes a few integration scenarios and the role of the DB2 Analytics Accelerator from a z-centric cognitive perspective. 

The data lake is a solution for a system of insight that has gained strong momentum with clients. This presentation describes the deployment of a data lake in the context of z Systems analytics. Data lakes are usually associated with offloading large data volumes to an open source platform. We present DB2 for z/OS and the DB2 Analytics Accelerator deployment options with the data lake, and illustrate how the DB2 Analytics Accelerator enables more choices for making DB2 for z/OS and other z Systems data available for analytics while keeping z Systems within the analytics lifecycle. This limits data movement and performs analytics close to where the data originates and resides.  
 

Content: Starting with an explanation of Big Data and the Apache Hadoop ecosystem with key components and initiatives, complementing z Analytics by using Big Data analytics and Natural Language Processing (NLP), integration of BigInsights with z Systems, customer use case scenarios, keeping DB2 for z/OS as control point for real-time z Analytics with SPSS.

Abstract: During the past decade, the Apache Hadoop open source community has developed an impressive set of products and tools that represent an important aspect of Big Data. In the beginning, we will explain the term Big Data and its relationship to the Hadoop ecosystem. We will furthermore illustrate how to complement existing 'traditional' DB2 for z/OS based analytical capabilities with Big Data analytics, e.g. by using text analytics and Natural Language Processing (NLP) as part of IBM InfoSphere BigInsights. This leverages key Hadoop components (MapReduce programming model, HDFS, HBase, Zookeeper, etc.) to analyze data from Enterprise-owned systems of engagement (e.g. call center transcripts, e-mail traffic, Facebook), and data from external social media sites (e.g. Twitter tweeds, Facebook sites, Blogs) and putting this in context with transaction insight from data on IBM z Systems. We will provide examples on how Hadoop systems - by using HBase and Hive with corresponding connectors to existing z Systems – and Big SQL on HDFS and Hive will enrich analytical insight. We will describe the IBM SPSS Modeler Server Scoring Adapter for DB2 on z/OS that embeds scoring algorithms directly into IBM DB2 for z/OS transactional data, which enables the real-time scoring of new transactional data as it is created.