Posts

Multiple SQL Server Instances in the Same VM vs Setup in Separate VMs

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In my day job mostly I focus on design, consulting the development team to Implement Data Warehousing and Analytics solutions plus experimenting some advanced analytics stuff like Machine Learning. By creating tabular models in our solution ecosystem, I make sure everything right in terms of performance of the data models we creating and end users get great experience by analyzing their own data easily by using Self Service BI tools like Microsoft Power BI. Recently, I got an email from one of the client I’m working with, for few clarifications regarding setup the development and production environments. His initial question was ‘Do you need to setup two separate VMs for Development and Production or Is it fine to install two instances in same VM?’
By looking at the problem, one can say why does it matter for the implementations, and the end result would be the same, right?.... 😕 Yes, it is true in some cases, But not for all the scenarios. I know there will be differences in terms …

SL Data Community November Meetup - Session 1: Streaming Analytics in Power BI Questions and Answers

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SL Data Community November event concluded successfully on yesterday(29th of November) with 50+ attendees even with the bad weather condition and heavy traffic congestion. I hope everyone went home safely.  Typically, SL Data Community meet-up is happening in every month. It includes two sessions and being treated as a common place where we can share our knowledge, experiences, and thoughts about technologies (mostly data related) which we are earning while working in the Corporate world.  
This time the first session was conducted by myself. The topic was Real Time Streaming Data Analytics using Microsoft Power BI




In the session, I spoke regarding How to analyze streaming data using Microsoft Power BI and demonstrated it. At the end, the session was opened for the Questions and Answers. These are the questions I was asked by the audience. I thought it is better to write it up in a blog post by assuming this is going to be some common questions anyone could have regarding Power BI an…

Value Encoding in Vertipaq Engine

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If you can remember my last blog post regarding Vertipaq engine inside SSAS Tabular, I’ve discussed three algorithms which are in use when process the model. Processing in the sense perform data loading from the relational source and load into the tabular structure. In here data compression is taken place in order to save the memory footprint. It is really important because all the data we had in the data warehouse or source relational database after processed the model load into the memory. So by the compression save the huge amount of memory space and it will utilize your hardware optimum way while faster scans because the data model is smaller than the original.
These are the steps taking place when we process the tabular model from SSDT or via SQL Server Management Studio.  Read the data from the source database and transform into columnar structure or vertipaq data structure while data encoding and compression occurs.Creating of dictionaries and indexes for each column.Creation o…

Power BI Documentation Is Now Online

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Power BI documentation is now Online. It is a great opportunity to learn all the in and outs about Microsoft Power BI. FTW 😊 The documentation contains six main sections.

Power BI Service: Tutorials for get start with Power BI Service
Power BI Desktop: Guide for Power BI Desktop + Power BI desktop updates etc 
Power BI Mobile Apps: Getting to deeper on Power BI Mobile apps
Power BI developer: To lean about development stuff like embedding, custom visuals
Power BI Report Server: Getting start with Power BI report server
Guided Learning: Best for the once who are going to lean Microsoft Power BI from the scratch.



Reference: MSDN

Vertipaq Engine in SSAS Tabular Database

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If you can remember my last blog post, SSAS tabular has two architectures to store data either DirectQuery or In-memory mode. Today in this blog post I’m going to discuss more detail about in-memory or Vertipaq engine.
In reality, SSAS tabular database engine can get requests from the client either MDX or DAX query.  Inside the Tabular database it contains two layers of calculation engines,
Formula Engine: Whenever a new request coming from the client despite its DAX/MDX, from Analysis services it parse and transforming them in query plans and finally execute them by formula engine.
Storage Engine: To perform calculations , from formula engine it performs one or more requests to the storage engine where the data is stored, which could be either in-memory (Vertipaq) engine or external relational engine (directQuery) depending on the mode you are using to build the model. In this post I'm mainly focus on Vertipaq Engine. It has a copy of data read from the data source when you perfor…

SSAS Tabular DirectQuery Mode VS In-memory Mode. Which Mode is Suite for Your Requirement?

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When we decided to implement the solution using SSAS Tabular, one of the key consideration have to do is, whether are we going to do the implementation with DirectQuery mode or in-memory(Import) mode. Because the hardware requirements are entirely different for each modes. Should I go with DirectQuery mode or stay with In-memory mode the decision totally based on your business requirement. You will realize it at the end of this post. However the good thing is, you can toggle back and forth between DirectQuery and In-memory at any time.
How to Switch Between DirectQuery and In-memory Mode
You can switch between these modes during the development in SSDT and even after the deployment using SSMS.
In SSDT Environment Right click on the Model.bimGoto Properties Select the Property DirectQuery Mode
In Management Studio, Select the Tabular database Right click and go to propertiesSelect model tab and change the property DirectQuery mode. 
What is In-memory mode

This is the default configuration…

Step-by-Step Twitter Sentiment Analysis Using Power BI Streaming Dataset, Microsoft Flow and Azure Text API

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Sentiment Analysis is known as Opinion mining or emotion AI which is a branch of Natural Language Processing and text analytics where systematically identify, extract, quantify, and study affective states and subjective information. This kind a analysis widely apply to analyse the product or service reviews, voice of the customer, survey responses from online and social media feeds to analyze the attitude of the customer.

Basically from the sentiment analysis the output would be either Positive, Negative or Neutral.  There are various algorithms and methods to do a sentiment analysis out there. In this post here I'm doing a sentiment analysis for iPhone 8 product by analyzing twitter feeds. Because, I wanted to know what others are thinking about the latest phone released by Apple. In order to do this task I'm using, 
Microsoft Azure cognitive services : Text Analytics (to run the sentiment analysis algorithms to get out the results)  Microsoft Flow : to extract the twitter fee…