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If you're interested in advanced visualizations that are simple to create, interactive dashboards and you want to move away from the classic standard data spreadsheets, then this is the lecture for you! Tableau software is oriented towards the visual display of data and towards the end user, and it allows quick and easy data analysis. Product includes best practices of data visualization which reduces the probability of a wrong choice of visualization form. By creating new and innovative ways of displaying data Tableau singled itself out from the market of analytical products.

Data Scientist is a person with exceptional analytical skills, person with statistical background and computer skills, and understanding of the business processes. These skills, which had previously appeared only individually, now in combination are imposed in response to the many future challenges for the companies, primarily the processing of large amounts of data in real-time and Big Data analytics.

In recent years, in the era of Big Data, Data Warehouse architectures (EDW - Enterprise Data Warehouse) evolve towards Logical Data Warehouses (LDW), which are becoming the new standard architecture for data management and data analytics. In these architectures Hadoop is used for evaluation and processing of new data sources, which can then be loaded into the Data Warehouse or combined with the data that is already there. IBM Fluid Query technology allows access to data from various sources such as Hadoop or Spark and combines them with data located in the Data Warehouse on IBM PDA device.

Big Data brings out great opportunities as well as new challenges that fundamentally change the way business is done in different companies. Only companies that will utilise their data properly will gain a significant competitive advantage in the market, which will result in faster and smarter decision making based on facts. In order to achieve full potential of Big Data, system architecture in enterprise environments should be able to support different data analysis in lab environment and production of data in the factory environment for specific projects or products.In order to make the transition from the lab to the factory environment as easy as possible, it is essential to implement the appropriate technology and  data management standards.

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. 

Use Cases at the Intersection of Apache Spark/Hadoop and IBM z Analytics
Abstract: This presentation provides insight into sample integration points and use case scenarios that are derived from client engagements. We highlight integration options between DB2 for z/OS and the DB2 Analytics Accelerator with IBM BigInsights for Apache Hadoop and Spark. We demonstrate how to complement traditional z Analytics with Big Data analytics, e.g. by using SQL on open source (Big SQL and Spark SQL) and text analytics, and describe the integration of key Spark components into a z Analytics environment with DB2 for z/OS. We introduce opportunities for data scientists, application developers, and business analytics leaders as we reveal how the z Systems analytics portfolio is leveraging and integrating open source technology now.

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.