Latest Version of R Language

R version 3.6.3 (Holding the Windsock) prerelease versions will appear starting Wednesday 2020-02-19. Final release is scheduled for Saturday 2020-02-29.


Data Science Masters Programs

Data Science Masters Programs offer the chance to become a qualified Data Scientist.

Data Science is a fast growing discipline that offers highly rewarding careers both from a financial and intellectual standpoint

With the recent huge growth in Data, it is easy to see why there is an increased incidence of people who are interested in gaining a Masters in Data Analytics and to follow a potentially rewarding career path?

There is a great deal of potential when it comes to the Data Science and Data Analytics the people who find themselves in the careers that are related to such often find them to be very fulfilling in terms of intellectual reward and often social merit.

Data Science is a fast growing areas of Business Intelligence software and is an in demand skillset.

Having a Data Science Masters degree will certainly help in job hunting in this growing area.

Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years.

Some well known acacdmic instutions that offer Data Science courses are:

There are some free Data Science courses, for example EdX offer a Data Science Essentials course:

This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment. Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion.

SQL 2020 Release Date

SQL 2020 will be released in late 2020.

We are still waiting for SQL 2019 to be released but it worth lo0king at some of the key features that Microsoft are likely to include in the SQL 2020 Release date.

The latest incarnation of Microsoft hugely popular BI, database and analytics toolset is likely to further build on the foundations laid in SQL 2018 and offer improved integration with widely used open source Data Science toolsets like R and Python.

We also widely expect the further embedding of more Azure Managed instance functionality and a widening of the Data Science, Artificial Intelligence, Machine Learning, Robotics and Predictive analytics capabilities into SQL2020.

It’s also anticipated that Power BI will get a major revamp with probably the introduction of improved data governance and stewardship capabilities to ensure it keeps pace with the competition now that Power PBI versions

SQL 2020

An area that is ripe for exploitation by the main Business Intelligence vendors is Data Quality. This is an area that vendors increasingly provide expensive add on products for but there is a keen desire amongst many organisations for more embedded mainstream offerings in this area.

Wider use of Master Data Management tools and better Dev Ops integration with Azure

Data Quality is one key area that Microsoft have very few offerings to users in compaprisom with widely used Data Quality tools.

Power BI Version (PBI) Version History

The latest Power BI version (PBI) Desktop was released in October 2019 Update and is version (2.74.5619.621).

power bi version

The latest version of Power BI contains the following new features and enhacements

Power BI Version Reporting

Automatic page refresh for DirectQuery

Power BI Analytics

The new Q&A visual
Improved user experience for Q&A
Improved drop-down control
Red and blue underlines
Improved visual results
Natural language improvements for Q&A
Integration with Office / Bing thesaurus
Support for measure tables, and better handling of table names and ambiguity
Q&A tooling (preview)
Review questions
Teach Q&A
Review all changes made
Support for SSAS and Azure AS, including RLS


PowerApps visual now included by default
New xViz visuals

PBI Data connectivity

Sagra Emigo connector generally available
Azure cost Management connector updated
New Workplace Analytics connector

Data preparation

Query diagnostics
Data profiling enhancements

Template apps

Project Web App


New file format: .PBIDS
Performance improvements for modeling operations


Power BI Desktop is a free application you can install on your local computer that lets you connect to, transform, and visualize your data. With Power BI Desktop, you can connect to multiple different sources of data, and combine them (often called modeling) into a data model that lets you build visuals, and collections of visuals you can share as reports, with other people inside your organization. Most users who work on Business Intelligence projects use Power BI Desktop to create reports, and then use the Power BI service to share their reports with others.

The most common uses for Power BI Desktop are the following:

  • Connect to data
  • Transform and clean that data, to create a data model
  • Create visuals, such as charts or graphs, that provide visual representations of the data
  • Create reports that are collections of visuals, on one or more report pages
  • Share reports with others using the Power BI service

People most often responsible for such tasks are often considered data analysts (sometimes just referred to as analysts) or Business Intelligence professionals (often referred to as report creators). However, many people who don’t consider themselves an analyst or a report creator use Power BI Desktop to create compelling reports, or to pull data from various sources and build data models, which they can share with their coworkers and organizations.

R Version History

R Latest Version

The latest version of R programming language is version 3.4.0 code named  You Stupid Darkness (who gives these ridiculous code names!)

Pre release versions have already been released to beta testers and the full  final version  is scheduled for Friday 21st April 2017.

R Version History

R 3.3.3 (March, 2017)
R 3.3.2 (October, 2016)
R 3.3.1 (June, 2016)
R 3.3.0 (April, 2016)
R 3.2.5 (April, 2016)
R 3.2.4 (March, 2016)
R 3.2.3 (December, 2015)
R 3.2.2 (August, 2015)
R 3.2.1 (June, 2015)
R 3.2.0 (April, 2015)
R 3.1.3 (March, 2015)
R 3.1.2 (October, 2014)
R 3.1.1 (July, 2014)
R 3.1.0 (April, 2014)
R 3.0.3 (March, 2014)
R 3.0.2 (September, 2013)
R 3.0.1 (May, 2013)
R 3.0.0 (April, 2013)
R 2.15.3 (March, 2013)
R 2.15.2 (October, 2012)
R 2.15.1 (June, 2012)
R 2.15.0 (March, 2012)
R 2.14.2 (February, 2012)
R 2.14.1 (December, 2011)
R 2.14.0 (November, 2011)
R 2.13.2 (September, 2011)
R 2.13.1 (July, 2011)
R 2.13.0 (April, 2011)
R 2.12.2 (February, 2011)
R 2.12.1 (December, 2010)
R 2.12.0 (October, 2010)
R 2.11.1 (May, 2010)
R 2.11.0 (April, 2010)
R 2.10.1 (December, 2009)
R 2.10.0 (October, 2009)
R 2.9.2 (August, 2009)
R 2.9.1 (June, 2009)
R 2.9.0 (April, 2009)
R 2.8.1 (December, 2008)
R 2.8.0 (October, 2008)
R 2.7.2 (August, 2008)
R 2.7.1 (June, 2008)
R 2.7.0 (April, 2008)
R 2.6.2 (February, 2008)
R 2.6.1 (November, 2007)
R 2.6.0 (October, 2007)
R 2.5.1 (July, 2007)
R 2.5.0 (April, 2007)
R 2.4.1 (December, 2006)
R 2.4.0 (October, 2006)
R 2.3.1 (June, 2006)
R 2.3.0 (April, 2006)
R 2.2.1 (December, 2005)
R 2.2.0 (October, 2005)
R 2.1.1 (June, 2005)
R 2.1.0 (April, 2005)
R 2.0.1 (November, 2004)
R 2.0.0 (October, 2004)
R 1.9.1 (June, 2004)
R 1.8.1 (November, 2003)
R 1.7.1 (June, 2003)
R 1.6.2 (January, 2003)
Installer for R 1.5.1 (June, 2002)
Installer for R 1.4.1 (January, 2002)
Installer for R 1.3.1 (September, 2001)
Binary files for R 1.2.2 (March, 2001)
Binary files for R 1.0.0 (February, 2000)

Magic Quadrant BI 2018 – Gartners Business Intelligence

Magic Quadrant BI 2018 – Gartners Business Intelligence

Before we examine the maturity model of BI solutions and the guidelines for its development, it is necessary to perform an analysis of the capabilities of BI solutions, as well as the market for these solutions.
As a starting point for this research we take Gartner’s “Magic Quadrants for BI platforms”,

Magic Quadrant BI 2018 – Gartners Business Intelligence

In it Gartner defines BI platforms as software platforms providing 12 capabilities, divided into 3 basic categories:
✓ Delivery of information;
✓ Integration;
✓ Analytics
Information technology is a highly dynamic field of research. As part of it, business
intelligence systems (BIS) also develop very quickly. In this paper we shall adhere to
the following definition of BIS: “BIS combine the activities of data mining and data
processing and knowledge management through analytical means in order to present
complex competitive information to consumers who draw plans and make decisions.”1
1. Analysis of today’s capabilities of BI platforms
Before we examine the maturity model of BI solutions and the guidelines for its
development, it is necessary to perform an analysis of the capabilities of BI solutions,
as well as the market for these solutions.
As a starting point for this research we take Gartner’s “Magic Quadrants for BI
platforms”, published in 2007 (fig. 1). In it Gartner defines BI platforms as software
platforms providing 12 capabilities, divided into 3 basic categories:
✓ Delivery of information;
✓ Integration;
✓ Analytics.
To the first category belong the capacities for:
✓ Generating reports;
✓ Navigation panes;
✓ Ad hoc queries;
✓ Integration with MS Office.
The second group includes:
✓ BI infrastructure;
✓ Metadata management;
✓ Developing environments;
✓ Workflows;
✓ Cooperation.
The third category covers the capabilities for:
✓ Online analytical processing;
✓ Visualization;
✓ Delivery of knowledge and forecasting;
✓ Maps of results.
Five years later Gartner published a new “Magic Quadrant for BI platforms”,
that is still current for 2012. In this document BI platforms continue to be viewed as
software platforms, delivering the capabilities described above. But two more features
have been added to the “delivery of knowledge” category, namely search-based BI
and mobile BI.
The basic capabilities of BI platforms have been widely discussed and studied, so for this reason they will not be considered in detail in this paper. More attention will
be paid to the two new characteristics, offered by Gartner.
The first opportunity is search-based BI. Essentially it is an application of a
search-index in structured and unstructured data sources and their division (organization)
into a classification structure of measures and dimensions, which consumers can easily
navigate and explore, using a Google-like interface.
The basic difference between search engines and data warehouses is that search
engines are very flexible and support any kind of format and type of information – be
it structured or unstructured. Thus search engines can cope with increasingly evolving
data structures. The indexing of both existing and new data (unknown so far) does not
require additional data modeling. Conventional data warehousing architecture has limited
capabilities for dealing with unstructured data that are necessary for facilitating decision-
making and search engines “fill this gap”. In comparison, data warehouses require
time not only for creating the warehouse model, but also for adding new data. Another
positive feature of search engines is their ease of “navigation” through contents. At
each step of the navigation, search engines provide different opportunities for filtering
the results according to contents into the multitude of data that have been indexed and
analyzed in nearly real time. Relational database management systems (RDBMS)
have no capacity for data analysis unless they possess some knowledge about different
type of data. That is, a search engine can easily follow any event that happened at a
certain moment in time, while using conventional RDBMS a search can be performed
only within strictly defined data fields

SAP Business Objects Business Intelligence 4.2 Service Pack 05 BOBJ

SAP Business Objects Business Intelligence 4.2 Service Pack 05  is due to be released at the end of 2o17.

SAP BusinessObjects BI (also known as BO or BOBJ) is a suite of front-end applications that allow business users to view, sort and analyze business intelligence data. The suite includes the following key applications:

  • Crystal Reports — Enables users to design and generate reports
  • Xcelsius/Dashboards — Allows users to create interactive dashboards that contain charts and graphs for visualising data
  • Web Intelligence — Provides a self-service environment for creating ad hoc queries and analysis of data both online and offline
  • Explorer — Allows users to search through BI data sources using a GUI interface. Users do not have to create queries to search the data and results are shown with a chart that indicates the best information match.

This version is expected to offer a number of innovations to users and the key focus of this release is around

  • Enterprise – scalability, multiple users
  • Agility
  • Smart pillar – not just look at preformatted reports, integrating with simple predictive capabilities
  • Big data – HANA Vora
  • Cloud – investing in “all in data” in the cloud
  • Hana integration with Hadoop

SAP Business Objects Business Intelligence 4.2 Service Pack 05

SAP Business Objects Business Intelligence 4.2 Service Pack 05

SAP S4 Hana BI Big Data Enterprise Cloud Architecture

SAP HANA platform has been available since 2010, and SAP applications like SAP ERP and the SAP Business Suite have been able to run on the SAP HANA database and/or any other database since launch.

The SAP S4 HANA platform was released on in February  2015 and SAP S4HANA was billed as being SAP’s biggest update to its ERP strategy and platform in over two decades.

The feedback from analysts was that it was perceived as a transformational shift but raised questions about the functionality, availability, pricing and migration surrounding SAP S4 HANA.


By the end of 2016, SAP announced that 5,400 customers had implemented SAP S4 HANA but other analysts disputed the viabiltiy of some of these figures given that it included many customers who were actually running Proof of Concept / trial projects rather than customers that were actually live.

Although many SAP customers have heard of HANA SAP still faces challenges getting the user base to understand what the various options are for migration and implementation.

SAP S4 HANA is basically SAP’s well known ERP in the cloud and is powered by the HANA in-memory database and the cloud version of S/4Hana is designed for hybrid scenarios combining on-premises and cloud software.


YellowFin BI Latest Versions

YellowFin BI Latest Versions

YellowFin BI Latest Versions – Yellowfin BI is a business intelligence software application that provides a range of Business intelligence dashboard reporting and data analysis functionality. Yellowfin BI allows reporting from data stored in relational databases, multi-dimensional cubes or in-memory analytical databases.

YellowFin BI is based in Headquartered in Melbourne, Australia,

The latest version of Yellowfin BI is 7.3+plus  – build 20170608 – released on 30 June 2017

Yellowfin BI Latest Versions

YellowFin BI Version History

Yellowfin BI Version 6.2
Yellowfin BI Version 6.3
Yellowfin BI Version 7.0
Yellowfin BI Version 7.1
Yellowfin BI Version 7.2
Yellowfin BI Version 7.3
Yellowfin BI Version 7.3 Plus





Tableau Hyper Engine Release Date Review

Tableau Hyper

Tableau Hyper – Tableau acquired Hyper in March 2016. Hyper was a German, academic based startup developing a high performance, in memory optimal database engine.

Hyper’s high-performance database system is being integrated into Tableau’s product offerings and will bring a range of of new capabilities to Tableau customer base. Hyper will replace the ageing Tableau Data Engine (TDE).

Tableau Hyper Data Engine

This new functionality will enable existing Tableau users to undertake

  • Faster analysis of Tableau data-sets
  • Improving Tableau’s big data strategy by providing support for large unstructured data sets
  • Improved data integration, data transformation, data aggregation and data blending
  • Richer analytics, such as k-means clustering and window functions
  • Tableau Hyper will also extend the hybrid data model.
  • Unification of analysis and transactional systems
  • Hyper will also provide tools for the harmonisation, cleansing  and transforming complex and large data sets
  • The aspiration is that Hyper provides a so-called “instant analytics” capability that will automatically display various contextual details as users interact with their data. This will be served by the in memory processing database engine.

Hyper will also retain connectivity to the 50 or so data sources that Tableau supports in version 10 – this covers disparate data source such as Amazon Redshift, Google BigQuery, Snowflake, and SQL 2017 server.

Tableau Hyper
Tableau Hyper Release Date

The beta for Hyper is already underway (early 2017) and the Tableau Hyper Release Date is expected to be Q4 2017 and be released with Tableau Version 11. Hyper will  replace the Tableau Data Engine (TDE) by end of 2017.