Overview

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.

All public courses are available as in-house training. Contact us for more information.

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, Edge Intelligence, Exasol, Greenplum, 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, Apache Hive, Apache Impala, Apache Phoenix, Dremio, IBM BigSQL, JethroData, MemSQL, Pivotal HawQ, 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, DataVirtuality UltraWrap, Denodo Platform, IBM Data Virtualization Manager, RedHat JBoss Data Virtualization, Stone Bond Enterprise Enabler, and Tibco Data Virtualization
  • Importing non-relational data, such as XML documents, web services, NoSQL and Hadoop data, and unstructured data
  • Differences between data virtualization and data blending
  • Are SQL-on-Hadoop engines becoming data virtualization tools?

New Business Intelligence Architectures

  • Discussion of different BI architectures, including Kimball’s Data Warehouse Bus, Architecture, Inmon’sCorporate Information Factory, DW 2.0, the Federated Architecture, the Centralized Warehous 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 and Translytical 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
  • Combining transactions and analytics = Transalytical database server
  • Market overview includes MemSQL, SAP HANA, and SpliceMachine

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

Who It's For

  • 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

Rick van der Lans

Independent Analyst, Consultant, Author and Lecturer

R20/Consultancy

Rick van der Lans is a highly-respected independent analyst, consultant, author, and internationally acclaimed lecturer specializing in data warehousing, business intelligence, big data, and database technology.  He has presented countless seminars, webinars, and keynotes at industry-leading conferences. For many years, he has served as the chairman of the annual European Enterprise Data and Business Intelligence Conference in London and the annual Data Warehousing and Business Intelligence Summit in The Netherlands. Rick helps clients worldwide to design their data warehouse, big data, and business intelligence architectures and solutions and assists them with selecting the right products. He has been influential in introducing the new logical data warehouse architecture worldwide which helps organizations to develop more agile business intelligence systems.  Over the years, Rick has written hundreds of articles and blogs for newspapers and websites and has authored many educational and popular white papers for a long list of vendors. He was the author of the first available book on SQL, entitled including Introduction to SQL, which has been translated into several languages with more than 100,000 copies sold. More recently, he published his book Data Virtualization for Business Intelligence Systems. In 2018 he was selected the sixth most influential BI analyst worldwide by onalytica.com.

DG Keynote: Plenary Keynote: Challenges of Developing an Enterprise Data Marketplace

Testimonials

"Hugely engaging."

Gary Connolly

Head of Business Intelligence, Ascenden

"Rick was well informed and managed the group very well. The examples were great. I will recommend this to other architects on my team. This was a great course. Well paced and Rick kept it light interesting."

Deepti Chidambaram

Information Architect, Transport for London

Fees

  • 2 Days
  • £1245
  • £1,245 + VAT (£249) = £1,494
Group Booking Discounts
Delegates
2-3 10% discount
4-5 20% discount
6 + 25% discount

Cancellation Policy:
Cancellations must be received in writing at least two weeks before the commencement of the seminar and will be subject to a 10% administration fee. It is regretted that cancellations received within two weeks of the seminar date will be liable for the full seminar fee. Substitutions can be made at any time.

Cancellation Liability:
In the unlikely event of cancellation of the seminar for any reason, IRM UK’s liability is limited to the return of the registration fee only. IRM UK will not reimburse delegates for any travel or hotel cancellation fees or penalties. It may be necessary, for reasons beyond the control of IRM UK, to change the content, timings, speakers, date and venue of the seminar.

Venue

  • etc.venues Marble Arch
  • Garfield House
  • 86 Edgware Rd
  • London W2 2EA

Accommodation

London Accommodation: IRM UK in association with JP Events Ltd has arranged special discounted rates at all venues and at other hotels nearby the venue. Please visit the JP Events website for further information.

Email: jane@jpetem.com Tel +44 (0)84 5680 1138 Fax +44 (0)84 5680 1139.

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