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Title: Data Warehousing, Big Data, and Business Intelligence: New Technologies, Trends, and Technologies

Big data, Hadoop, in-memory analytics, self-service BI, data warehouse automation, analytical database servers, data virtualization, data vault, operational intelligence, predictive analytics, and NoSQL are just a few of the new technologies and techniques that have become available for developing BI systems. Most of them are very powerful and allow for development of more flexible and scalable BI systems. But which ones do you pick?

Due to this waterfall of new developments, itís becoming harder and harder for organizations to select the right tools. Which technologies are relevant? Are they mature? What are their use cases? These are all valid but difficult to answer questions.

This seminar gives a clear and extensive overview of all the new developments and their inter-relationships. Technologies and techniques are explained, market overviews are presented, strengths and weaknesses are discussed, and guidelines and best practices are given.

The biggest revolution in BI is evidently big data. Therefore, considerable time in the seminar is reserved for this intriguing topic. Hadoop, MapReduce, Hive, NoSQL, SQL-on-Hadoop are all explained. In addition, the relation with analytics is discussed extensively.

This seminar gives you a unique opportunity to see and learn about all the new BI developments. Itís the perfect update for those interested in knowing how to make BI systems ready for the coming ten years.

Subjects

1. The Changing World of Business Intelligence

  • Big Data: Hype or reality?
  • Operational intelligence: does it require online data warehouses?
  • Data warehouses in the cloud
  • Self-service BI
  • The business value of analytics

2. Overview and Status of Analytical SQL Database Servers

  • Are classic SQL database servers still suitable for data warehousing?
  • Important performance improving features
  • Market overview of analytical SQL database servers, Actian Matrix and Vector, EMC/Greenplum, Exasol, HP/Vertica, IBM/Netezza, Kognitio, Microsoft, SAP HANA and Sybase IQ, Teradata Appliance and Teradata Aster Database

3. Big data, Hadoop and NoSQL for Data Warehouses

  • The relationship between big data and analytics
  • The Hadoop software stack explained, including HDFS, MapReduce, YARN, Hive, and HBase
  • Classification of NoSQL database servers: key-value stores,  document stores, column-family stores and graph data stores
  • The balancing act: productivity versus scalability
  • Market overview: Apache Hadoop and CouchDB, Cassandra, Cloudera, DataStax, IBM InfoSphere BigInsights, InfiniteGraph, MapR, Microsoft SQL Azure, MongoDB, Neo4j and Oracle Big Data Appliance
  • Making big data available to a larger audience with SQL-on-Hadoop engines, such as Apache Drill, Apache Hive, Apache Phoenix, Cloudera Impala, HP Vertica, JethroData, MemSQL, Pivotal HawQ, Spark SQL and Splice Machine

4. New Business Intelligence Architectures

  • Discussion of different BI architectures, including Kimballís Data Warehouse Bus, Architecture, Inmonís Corporate Information Factory, DW 2.0, the Federated Architecture, the Centralized Warehouse Architecture, the Data Virtualization Architecture, and the SaaS BI Architecture
  • Do we still need data marts?
  • What is the role of master data management in BI architectures?
  • Enforcing data quality in different BI architectures
  • Using data vault to create more flexible data warehouses
  • Data warehouse automation to create data warehouses and data marts faster

 5. New Forms of Reporting and Analytics

  • Mobile BI, Exploratory analysis, self-service BI
  • Collaborative analytics: the marriage of social networks and BI
  • Tools for embedded analytics
  • Investigative analytics and the data scientist

 6. Data Virtualization for Agile BI systems and Lean Integration

  • Data virtualization offers on-demand data integration
  • Open versus closed data virtualization servers
  • Market overview:  Cirro Data Hub, Cisco/Composite Information Server, Denodo Platform, Informatica Data Services, RedHat Jboss Data Virtualization and Stone Bond Enterprise Enabler
  • Importing non-relational data, such as XML documents, web services, NoSQL and Hadoop data, and unstructured data
  • Differences with data blending

7. Operational Business Intelligence

  • Analytics at the speed of business
  • Different forms of operational BI: operational reporting, operational dashboarding, operational analytics and embedded analytics
  • What is time-series analysis?
  • Integrating operational and historical data
  • The role of data replication, rule engines, complex event processing and ESBs

 8. Summary and Conclusions

Objectives

  • 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 embed big data and analytics in existing business intelligence architectures.

Related Whitepapers:

 Behind the Scenes of a BICC in the Cloud; March 2015; sponsored by Inergy

 Investigative Analytics using Xurmo's Hadoop-based Platform; February 2015; sponsored by Xurmo Technologies

 Migrating to Virtual Data Marts using Data Virtualization; Simplifying Business Intelligence Systems; January 2015; sponsored by Cisco

 Extending Business Intelligence with Text Exploration Technology; June 2013; sponsored by InterSystems Corporation. Link for German version, for Dutch version, and for Italian version

 Transparently Offloading Data Warehouse Data to Hadoop using Data Virtualization; November 2014; sponsored by Cisco

 Data Services: The Marriage of Data Integration and Application Integration; July 2012; sponsored by Talend Inc.

 Data Replication for Enabling Operational BI; June 2012; sponsored by Informatica Corporation