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IRM UK 2-Day Seminar | Incorporating Big Data, Hadoop and NoSQL in BI Systems and Data Wareh
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This seminar gives you a unique opportunity to see and learn about all the new BI developments. It’s the perfect update for those interested in knowing how to make BI systems ready for the coming ten years.

6/13/2017 to 6/14/2017
When: 13th - 14th June 2017
Tuesday and Wednesday
Where: etc.venues Marble Arch
Garfield House,
86 Edgware Rd,
London W2 2EA
United Kingdom
Presenter: Rick F. van der Lans

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 IRM UK                                                   

Incorporating Big Data, Hadoop and NoSQL in BI Systems and Data Warehouses

Use code AEA10 to receive 10% AEA member discount when registering!!

Register On-line:
13-14 June 2017, London

Big data, Hadoop, in-memory analytics, Spark, 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, it’s 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, Spark, 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. It’s the perfect update for those interested in knowing how to make BI systems ready for the coming ten years.

Learning Objectives

In this course, you will learn:

  • 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
  • Which products and technologies are winners and which ones are losers.
  • How new and existing technologies, such as Hadoop, NoSQL and NewSQL, will help you create new opportunities in your organization.
  • How more agile data business intelligence systems can be designed.
  • How to embed big data and analytics in existing business intelligence architectures

Course Outline

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

Overview of Analytical SQL Database Servers

  • Are classic SQL database servers more suitable for data warehousing?
  • Important performance improving features: column-oriented storage, in-database analytics
  • Market overview of analytical SQL database servers, Actian Matrix and Vector, Dell/EMC/Greenplum, Exasol, HP/Vertica, IBM/Pure Data Systems for Analytics, Kognitio, Microsoft, SAP HANA and Sybase IQ, SnowflakeDB, Teradata Appliance and Teradata Aster Database

Hadoop Explained

  • The relationship between big data and analytics
  • The Hadoop software stack explained, including HDFS, MapReduce, YARN, Hive, Storm, Sqoop, Flume, and HBase
  • The balancing act: productivity versus scalability
  • Making big data available to a larger audience with SQL-on-Hadoop engines, such as Apache Drill and Hive, CitusDB, Cloudera Impala, IBM BigSQL, JethroData, MemSQL, Pivotal HawQ, ScleraDB, SparkSQL, and Splice Machine

Spark Explained

  • Spark is in-memory analytical processing
  • The interfaces: SQL, R, Scala, Python
  • Does Spark need Hadoop?
  • Use cases of Spark

NoSQL Explained

  • Classification of NoSQL database servers: key-value stores, document stores, column-family stores and graph data stores
  • Market overview: CouchDB, Cassandra, Cloudera, MongoDB, and Neo4j
  • Strong consistency or eventual consistency?
  • Why an aggregate data model?
  • How to analyze data stored in NoSQL databases

Data Virtualization for Agile BI Systems and Lean Integration

  • Data virtualization offers on-demand data integration
  • Seamlessly integrating big data and the data warehouse
  • Market overview: AtScale, Cirro Data Hub, Cisco Information Server, Denodo Platform, Informatica Data Services, RedHat JBoss Data Virtualization, Rocket, and Stone Bond Enterprise Enabler
  • Importing non-relational data, such as XML documents, web services, NoSQL and Hadoop data, and unstructured data
  • Differences between data virtualization and data blending

New Business Intelligence Architectures

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

Operational Business Intelligence

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

NewSQL Database Servers

  • NewSQL stands for high-performance transactional SQL database servers
  • Simpler transaction mechanisms to implement scale-out
  • What does the term geo-compliancy  mean?
  • Market overview: Clustrix, GenieDB, MariaDB, NuoDB, Splice Machine, Pivotal GemFire XD, and VoltDB

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
  • R as the new open source platform for analytics

Data Modelling for Big Data, Hadoop and NoSQL

  • Explanation of non-relational concepts, such as column families, hierarchies, sets, and lists
  • Is storing unstructured and semi-structured data really more flexible?
  • The differences between schema-on-read and schema-on-write
  • Rules for transforming classic data models to NoSQL concepts
  • Application needs influence database design

Summary and Conclusions


  • Business Intelligence Specialists
  • Data Warehouse Designers
  • Business Analysts
  • Technology Planners
  • Technical Architects
  • Enterprise Architects
  • IT Consultants
  • IT Strategists
  • Systems Analysts
  • Database Developers
  • Database Administrators
  • Solutions Architects
  • Data Architects
  • IT Managers

Speaker Biography

Rick F. van der Lans is an independent analyst, consultant, author, and lecturer specialising in data warehousing, business intelligence, big data, and database technology. He is Managing Director of R20/Consultancy. He has helpedmany large companies worldwide in defining their business intelligence and big data architectures. Mr. van der Lans is an internationally acclaimed lecturer. He is chairman of the European Business Intelligence Conference. His popular IT books have been translated into many languages and have sold over 100,000 copies. His latest book is entitled "Data Virtualization for Business Intelligence Systems". Rick writes blogs for well-known websites, such as and, and he has written numerous successful whitepapers.

Seminar Fee
£1,245 + VAT (£249) = £1,494

Register On-line:
13-14 June 2017, London

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