Why we need data integration and what can we do?

The first question is why we need data integration?

Let me give you an example here to answer this question.

Every company has many departments, and different departments use different tools to store their data. For example, marketing team may use hubspot tool.

Now, we have different departments which store different types of data in a company.

However, insightful information is needed to make business decisions through those large amount of data.

What can we do?

Maybe we can connect all the databases everytime to generate reports. However, it will cost us large amount of time, then the term of data integration is raised.

What is data integration?

Data integration is a process in which heterogeneous data is retrieved and combined as an incorporated form and structure.

There are some advantages of data integration:

  1. Reduce data complexity
  2. Data integrity
  3. Easy data collaboration
  4. Smarter business decisions

There are also some well-known tools can do data integration, e.g., Microsoft SQL Server Integration Services (SSIS), Informatica, Oracle Data Integrator, AWS Glue etc.

This blog we will talk about SSIS, which is one of the most popular ETL tool in New Zealand.


  1. Data can be loaded in parallel to many varied destinations
  2. SSIS removes the need of hard core programmers
  3. Tight integration with other products of Microsoft
  4. SSIS is cheaper than most other ETL tools

SSIS stands for SQL Server Integration Services, which is a component of the Microsoft SQL Server database software that can be used to perform a broad range of data integration and data transformation tasks.

What can SSIS do?

It combines the data residing in different sources and provides users with an unified view of these data.

Also, it can also be used to automate maintenance of SQL Server databases and update to multidimensional analytical data.

How is works?

These tasks of data transformation and workflow creation is carried out using SSIS package:

Operational Data–>ETL–>Data Warehouse

In the first place, operational data store (ODS) is a database designed to integrate data from multiple sources for additional operations on the data. This is the place where most of the data used in current operation is housed before it’s tranferred to the data warehouse for longer tem storage or achiiving.

Next step is ETL(Extract, Transform and Load), the process of extracting the data from various sources, tranforming this data to meet your requirement and then loading into a target data warehouse.

The third step is data warehousing, which is a large set of data accumulated which is used for assembling and managing data from various sources of answering business questions. Hence, helps in making decisions.

What is SSIS package?

It is an object that implements integration services functionality to extract, transform and load data. A package is composed of:

  1. connection
  2. control flow elements(handle workflow)
  3. data flow elements(handle data transform)

If you want to investigate more about SSIS, check it out on Microsoft official documents.

If you are interested in or have any problems with SSIS, feel free to contact me.

Or you can connect with me through my LinkedIn.

Some Cloud Computing Fundamentals

What is cloud computing?

The practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer.


  1. You own the servers
  2. You hire the IT people
  3. You pay or rent the real-estate
  4. You take all the risk

Cloud providers:

  1. Someone else owns the servers
  2. Someone else hires the IT people
  3. Someone else pays or rents the real-estate
  4. You are responsible for your figuring cloud services and code, someone else takes care of the rest.

Different kinds of hosting:

  1. Dedicated Server: One physical machine dedicated to single a business. Runs a single web-app/site. (Very expensive, high maintenance, high security)
  2. Virtual Private Server: One physical machine dedicated to a single business. The physical machine is virtualized into sub-machines runs multiple web-apps/sites.
  3. Shared Hosting: One physical machine, shared by hundreds of businesses. Relies on most tenants under-utilizing their resources. (Very cheap, Very limited)
  4. Cloud Hosting: Multiple physical machines that act as one system. The system is abstracted into multiple cloud services. (Flexible, Scalable, Secure, Cost-Effective, High Configurability)

Common Cloud Services

A cloud provider can have hundreds of cloud services that are grouped various types of services. The four most common types of cloud services for infrastructure as a service(laaS) would be:

  1. Compute: Imagine having a virtual computer that can run application, programs and code.
  2. Storage: Imagine having a virtual hard-drive that can store files
  3. Networking: Image having a virtual network being able to define Internet connections or network isolations
  4. Database: Imagine a virtual database for stoing reporting data or a database for genetal purpose web-application

The term ‘cloud computing’ can be used to refer to all categories, even though it has ‘compute’ in the name.

Benefits of Cloud Computing

Cost-effective: You pay for what you consume, no up-front cost. Pay-as-you-go(PAYG) thousands of customers sharing the cost of the resources.

Global: Launch workloads anywhere in the world, just choose a region

Secure: Cloud provider takes care of physical security. Cloud services can by secure by default or you have the ability to configure access down to granular level.

Reliable: Data backup, disaster recovery, and data replication, and fault tolerance.

Scalable: Increase or decrease resources and services based on demand

Elastic: Automate scaling during spikes and drop in demand

Current: The underlying hardware and managed software is patched, upgraded and replaced by the cloud provider without interruption to you.

Next blog I will write some fundamentals about Microsoft Azure, which is the cloud provider service of Microsoft.

If you are interested in or have any problems with cloud computing, feel free to contact me.

Or you can connect with me through my LinkedIn.

What is normalization and what are normalization forms?

Normalization is a technique that decomposes the table to reduce the redundacy of data.

There are 3 normalization forms we need to check and follow to normalize tables.

1st Normalization Form:

  1. Each coloum of the table should have a single value
  2. Each column should belong to a same domain
  3. Two colums should not have a same name
  4. It need not to be a proper order, e.g., we need not to sort the records according to the date of DOB.

2nd Normalization Form:

  1. The table should be satisfy 1st Normalization Form
  2. All the non-key attributes myst be functionally dependently on primary key

3rd Normalization Form:

  1. The table should be satisfy 2nd Normalization Form
  2. There is no transitive dependency for non-prime attributes

The transitive functional dependency is as follows:

A is functional dependent on B and B is functional dependent on C. So, C is transitive dependent on A through B.

If you are interested in or have any problems with Business Intelligence, feel free to contact me.

Or you can connect with me through my LinkedIn.

DAX Cheat Sheet

What is DAX?

It is the programming language for Power Pivot, SSAS Tabular and Power BI.

It resembles Excel because it was born in PowerPivot. But it has no concept of <row> and <column> and has different type system.

The most important, it has many new functions.

The most two are measure and calculated column:

Measure is used to calculate aggregates, e.g., Sum, Avg and evaluated in the context of the cell in a report or a DAX query.

Calculated column evaluates each row and is computed at the low level within the table it belongs to.

Some Common Dax Expressions:


  1. Return the value in result_columnName for the row that meets all criteria specified by search_columnName and search_value
  2. LOOKUPVALUE(Result Column Name, Search Column Name, Search Column value)


  1. Return a subset of a table or expression
  2. FILTER(<table>,<filter>)


  1. Return all the rows in a table, or values in a column, ignoring any filters that may have been applied
  2. ALL(<table> or <column>)


  1. Returns a related value from another table
  2. RELATED(<column>)


  1. Evaluates an expression in a context that is modifies by specific filters
  2. CALCULATE(<expression>,<filter1>,<filter2>)

If you are interested in or have any problems with DAX or Business Intelligence, feel free to contact me.

Or you can connect with me through my LinkedIn.


What is a view?

A view is a virtual table based on the result set of an SQL statement.

Here is an example.

create view [sales].[v.salesbyperson2]
  salespersonid, round(totaldue,2) as salesamount

What is a synonym?

A synonym, like the name, is an alternate name we create for another database object. 

Here is the syntax:

create synonym [synonym_name]
for [server_name].[database_name].[schema_name].[object_name]

What is the trigger?

A trigger is a special kind of stored procedure that automatically executes when an event occurs in the database server. 

Regardless of whether or not any table rows are affected.

Here is an example:

create trigger reminder1
on sales.customer
after insert, update
as raiserror ('notify customer relations', 16, 10);

If you are interested in or have any problems with SQL or Business Intelligence, feel free to contact me.

Or you can connect with me through my LinkedIn.

How to do web scrapping in Power Query in Power BI?

On web, there are public data sourse which can be imported and transformed to make insight reports or dashboards.

There are two circumstances:

This kind of importing of tables is super easy.

For example, we can try to make an easy one.

Here is the web link: https://en.wikipedia.org/wiki/Demographics_of_China. What we want is to import the tables from a Wikipedia webpage.

The simple steps are as follow:

Open Power BI Desktop–>Click Get Data–>Click Web–> Paste the URL in the dialog and click OK.

So, there is the screenshot:

In Navigatot dialog, we can select the corresponding table that we want to import and transform it.

This kind of importing from web in Power BI is simple. Because Power Query can identify the table in HTML and tables are in the <table>…</table> tags.

However, many tables in HTML are different.

If we meet this kind of circumstance, we will end up seeing the Document entity in the Navigator.

Next blog, we will demonstrate how to extract the relevant elements in HTML with Power Query in Power BI.

The tricky part id how to find the correct path in the HTML source tree, which needs you to have a basic but not prior HTML knowledge.

To motivate you to keep reading, here is the final web scarping screenshot which i made recently:

If you are interested in or have any problems with Power BI or Power Query, feel free to contact me.

Or you can connect with me through my LinkedIn.

Create a SSIS Project in Visual Studio 2015


  1. SQL Server Data Tools 2015(install a shell of Visual Studio 2015)
  2. Visual Studio 2015

What is SSIS?

SSIS stands for SQL Server Intergration Services. SSIS is to do the ETL(Extract, Transform and Load) task for data warehouse.

SSIS can also update data warehouse, clean and mine data, create ‘packages’, manipulate data, etc.

To design ETL task in Visual Studio, we use the data flow in Visual Studio.

What is data flow?

A data flow defines a flow of data from a source to a destination.

Before designing the data flow, we can try to drag a data flow and drop it in the control flow.

What is a control flow?

It is a flow we can use to control the flow for different tasks. It provides the logic for when data flow components are run and how they run.

For example:

  1. perform lopping
  2. call stored procedures
  3. move files
  4. manage error handling
  5. check a condition and call different tasks

It defines a workflow of tasks to be executed, often a particular order (assuming your included precedence constraints).

we can rename the data flow task in control flow.

For example, we can drag OLE DB Source, so we can connect to Relational Database.

Then we can follow the Microsoft official toturial to do it step by step.


If you are interested in or have any problems with SSIS, feel free to contact me.

Or you can connect with me through my LinkedIn.

Dimensional Modelling

First of all, a concept of Data Warehouse is supposed to be clear. Data Warehouse is not a copy of source database with a name prefixed with ‘DW’.

DW is we can store data from multiple data sources to be used for historical or analysis report. Some data sources are .csv format, some are google docs, etc. So we need to aggregate them into one data source.

This is called Data Warehouse.

How to design it? The procedure is Dimensional Modelling.

What is the difference between relational and dimensional?

It is same as the difference like normalised VS denormalised.


  1. Minimal data redundancy
  2. Optimised for fast read and fast write
  3. Current data only
  4. Realtime data


  1. Redundancy data storage for performance
  2. Fast read only
  3. Non-realtime data
  4. Current and historical data

The next part is about fact table and dimension table in dimensional design.

Fact table:

  1. Data that can be measured
  2. Contains surrogate key, linking the associated measures or facts

Dimension table:

  1. Descriptive information

Some types of dimensional models:

  1. Accumalating snapshot table
  2. Aggregate fact
  3. Fact table
  4. Factless Fact table
  5. Snapshot table

If you are interested in or have any problems with Dimensional Modelling, feel free to contact me.

Or you can connect with me through my LinkedIn.

SQL Correlated Subqueries

Recently I often made some mistakes about subqueries, so I wrote this blog about correlated subqueries.

Firstly, here is a SQL practicing website: https://sqlzoo.net.

It is free and easy for SQL beginners to do SQL exercise step by step.

A correlated subquery works like a nested loop: the subquery only has access to rows related to a single record at a time in the outer query.

The technique relies on table aliases to identify two different usages of the same table, which means one usage is in the outer query and another one is in the subquery.

Here is a table called world, which is an example on the sqlzoo website:

Afghanistan Asia 652230 25500100 20343000000
Albania Europe 28748 2831741 12960000000
Algeria Africa 2381741 37100000 188681000000
Andorra Europe 468 78115 3712000000
Angola Africa 1246700 20609294 100990000000

Question: Find the largest country (by area) in each continent, show the continent, the name and the area.

SQL answer using subquery:

SELECT continent, name, area 
FROM world x
WHERE area >= ALL
    (SELECT area FROM world y
     WHERE y.continent=x.continent AND area>0)

One way to interpret the SQL line in the WHERE clause that references the two table is “… where the correlated values are the same”.

In this example, we can tell “select the country details from the world table where the area is larger than or equal to the area of all countries where the continent is the same”.

If you are interested in or have any problems with SQL, feel free to contact me.

Or you can connect with me through my LinkedIn.

An Introduction to Power BI (Power BI 101)

This article we will provide an introduction to Power BI.

It is a Microsoft business analytics service to provide interface to create reports and dashboards with interactive visualisations.

It has an advantage: easy to use (self service BI).

First of all, Power BI official guided learning material.

It’s very structured and good training tutorial.

As I talk in previous blog about this learning platform, it is fun to get points and badge.

What is the workflow of Power BI?

  1. Bring data in using Power BI desktop, manipulate data and build reports.
  2. Publish it to Power BI service.
  3. Share reports and dashboards to others.

Here we go to the first step:

Download the Power BI desktop.

Power BI desktop version provides data warehouse capabilities:

  1. ETL
  2. Calculates column
  3. Measures

After we download it, we can find there are five panels in Power BI interface:

  1. Fields: where the datasets
  2. Data: view and manipulate the data
  3. Reports: place visualizations to build reports
  4. Dashboards: choose the graphs to use
  5. Relationships: view/change relationships in the dataset

Through these panels, we can manipulate data and build reports.

Once the report is completed, we can publish it onto Power BI Service on the cloud.

Reference sources:


If you are interested in or have any problems with Power BI, feel free to contact me.

Or you can connect with me through my LinkedIn.