Incorporating Big Data, Hadoop and NoSQL in Data Warehouse and Business Intelligence Systems

by Rick van der Lans download a PDF brochure

Description

Most current BI systems are developed with classic database servers, ETL tools, and reporting tools. But with the advent of Big Data, are the architectures of these BI systems still sufficient? Are they flexible enough? Can they handle such massive quantities of data? Or is it time to adopt all these new data storage technologies, such as Hadoop, MapReduce, NoSQL, SQL-on-Hadoop, and Data Virtualization.
This seminar focuses on all the new technologies that have become available to develop modern BI systems. BI systems that allow organizations to analyze Big Data with the simplest self-service tool up to the most advanced analytical tool, that make integration of Enterprise Data and Big Data transparent, and that allow an evolutionary adoption of all the new technologies. The seminar gives the audiences a full and critical update of all the new products and technologies.

What you will learn

• Learn about the trends and the technological developments related to Business Intelligence, Analytics, Data Warehousing, and Big Data
• Discover the value of Big Data and Analytics for organizations
• Learn which products and technologies are winners and which ones are losers
• Learn how new and existing technologies, such as Hadoop, NoSQL and NewSQL, will help you create new opportunities in your organization
• Learn how more Agile Data Business Intelligence systems can be designed
• Learn how to incorporate big data and analytics in existing business intelligence architectures.

Main Topics

• In-depth overview of all the modules making up Hadoop, including HDFS, MapReduce, HBase, Storm, and Yarn
• Critical assessment of the SQL-on-Hadoop engines, including
• Application areas of Hadoop in BI systems: sandbox, offloading cold data,
• Letting classic reporting and analytical tools access Big Data stored in Hadoop
• Transparently offloading Data Warehouse data to Hadoop using Data Virtualization servers
• Integrating an Hadoop-based sandbox for Data Scientists in BI systems
• Developing operational Business Intelligence systems using Storm and other event processing engines
• Using NoSQL (MongoDB, Cassandra, etc) or NewSQL (Clustrix, NuoDB, etc) as transaction systems
• Moving data quality aspects to the business users by using self-service data preparation
• Data Modeling for Big Data and Hadoop
• Implementing data lakes and data reservoirs with the right technology
• Comparison and overview of new data storage technology