Business Intelligence and Data Warehousing

Delivery: In-class at our Toronto academy

Duration: 3 weeks


IBM estimates that we create 2.5 quintillion bytes of data every day. The days of using spreadsheets to manage a company’s data are long gone. The big data revolution has brought profound changes to how companies collect, store, manage and analyze their data.

Business Intelligence (BI) and Data Warehousing (DW) refers to a set of methods and techniques that are used by organizations for tactical and strategic decision making.

Business Intelligence (BI) and Data Warehousing (DW) are sometimes used interchangeably, but are different:

  • Typically BI includes end user tools for query, reporting, analysis, dashboarding etc.
  • DW is simply a consolidation / repository of an organization’s  electronically stored data from a variety of sources .


Both concepts depend on each other:

– BI almost always assumes a Warehouse (WH), Operational Data Store (ODS) or Data Mart (DM) exists with timely, trusted information;

– A DW depends on end user tools that turn data into information.



Rise in the big data trend in the organization is leading to growing demand for analytics, which is projected to augment market growth. Increasing demand for high speed analytics and low latency along with growing role of business intelligence in enterprise management is expected to drive the market demand.

Data professionals are in short supply. Many businesses are struggling to find talent, even as more people enter the field. The number of data professionals in Canada has increased by 48% over the past five years, making it the fastest-growing job category in the country. And demand isn’t letting up.

A survey conducted last year by IDC found that 53% of large Canadian organizations said lack of talent was the biggest impediment to successful completion of big data projects. This will be an absolutely booming industry for the next 10 years.

In 2015, Big Data Consortium released a similar report, estimating the Canadian gap at between 10,500 and 19,000 professionals with data and analytical skills, and 150,000 with deep analytical literacy.

Consulting firm Mc­Kinsey and Co. estimates that the U.S. currently faces a shortage of up to 190,000 people with analytical expertise and 1.5 million managers with the skills to understand and act on what big data can reveal.

The global data warehouse market is predicted to grow by 8.3 percent in 2017-2022 surpassing a total market value of $20 billion by 2022 (source: Data Warehouse Market Forecast 2017-2022, Market Research Media, September 2017 ). North America is projected to be the dominant region for the market owing to the availability of technologically advanced infrastructure. Asia Pacific and Europe are expected to be emerging region for the market.


The training program provides an overview of the major facets of developing and using a Data Warehouse (DW). Focus will be on ETL & Data Integration Solutions and Business Intelligence tools (with special focus on DataStage, Pentaho, Tableau which are market leading solutions / tools).

Students will work on a major project that allows them to develop:  a project plan and business case for a data warehouse, a dimensional model, a data staging process and a data access process. Projects  on SQL , DataStage will also be undertaken.


Class 1:

> ETL Background:

– What is it?

– Source Types

– Destination Types


– Data quality

– Data lineage

– Metadata information

> Introduction to Datastage

> Introduction to Pentaho

> Databases:

– Database Overview


– Postgres SQL

note:  following Software Installations  will be made on student’s personal  Laptop in the beginning of the first class:  Database Installation (Postgres); ETL Software installation (Pentaho); BI Tool installation (Tableau)


Class 2:

> Database (SQL Continued):

– SQL Advanced concepts

Sample projects

> ETL In-depth dive:

– Data Extractions Sources



> Target stages

– Files

– Datasets

– DB


Class 3:

> ETL In – depth:


– Lookups (Normal / Sparse)

– Joins

– filters

– pivot functionality

– aggregation

> IBM DataStage Overview


Class 4:

> IBM DataStage continued

> Mini project


Class 5: 

> Reporting Tools:

– Connect to your data.

– Edit and save a data source

– Understand Tableau terminology.

– Use the Tableau interface / paradigm to effectively create powerful visualizations.

– Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations.

– Represent your data using the following visualization types:

– Pie Charts and Bar Charts

– Dual Axis and Combo Charts with different mark types.

Business Intelligence