Tuesday, August 2, 2016

Data Science - Education

With the rapid growth of Big data technologies , there has been an exponential growth of data science and its related technologies. This has has also led to the demand for data scientists and also the jobs related to data science are very lucrative. Microsoft has been steadily expanding it s cloud based offerings and also getting into big Data related technologies and efforts. Since there is tremendous need for Data science skills, Microsoft has come forward to offer a curriculum  totally devoted to Data Science. This curriculum is offered via edx.org.  There are a total of 9 courses and the price per course ranges from $49-$99. There is also a final project for which around 6-10 hours is required. One can check the link below for all the details:
https://www.edx.org/microsoft-data-science-curriculum
The courses cover from Use Microsoft Excel to explore data to Iimplement a machine learning solution for a given data problem. Each of the course can be done a auditing course or one can upgrade to get a validity certificate on passing the course. Each of the courses have Labs ,Quizzes and discussion forums, the discussion forums can be use to get questions answered related to the concepts being discussed. I hope the courses provide the much needed insights into Data Science.


Sunday, June 19, 2016

Hadoop, Big Data...

I was provided an opportunity to write an blog article at analytics vidhya about big data related technologies. Analytics vidhya is very well maintained website about big data technologies. All the articles are well researched and presented. Please see the Link below for my article.

http://www.analyticsvidhya.com/blog/2016/06/started-big-data-integration-hdfs-dmexpress/

Hope the readers find the above article interesting and useful...


Thursday, April 21, 2016

SQL BI Conference 2016 - Pass Business Analytics 2016

With the advent of newer technologies and tools ins area of Data Analytics, Data Science, Big Data and Data Visualizations, the Pass Business Analytics 2016 conference to held in San Jose from May 3-4 provides lot of interesting sessions and topics of discussion. Please do check out the web site below for further details:
http://passbaconference.com/2016/Home.aspx
It is a tremendous opportunity to grow your network and skill base, with lot of talented speakers/experts presenting on very interesting topics. There has been a lot of momentum in the Microsoft BI space especially with PowerBI and its related set of tools. There are very useful sessions with Power BI and Power Pivot provided by folks from the company:
 http://www.powerpivotpro.com/. They provide a lot of training in Power BI and PowerPivot, also provide consulting on projects that would need PowerBI and PowerPivot.
When you get a chance, check them out...

Wednesday, April 13, 2016

Data Services

With increasing amounts of data and information, one of the challenges business face is how to access the data and how the data needs to be stored. One of the concepts that can be used to effective and efficient provisioning of data is the use of  Data Services. One of the requirements to having a good data service is to make sure the underlying data is clean and properly modeled. There is a lot of effort that needs to going to making sure the data is properly cleansed and correctly modeled in the data store/data marts. Once this is accomplished, using data services provides advantages to the consuming application. The concept of data service brings in the notion that the Data quality is done in a central place which includes cleansing and enriching data. The advantages of Data services include 1)Agility - This allows customer to access data quickly and also doesn't place the burden of having knowledge of the underlying data. 2)Cost Effectiveness - Data analyst/providers can build the foundation of quality/modeled data and the presentation layer can be determined by the consumers.
Different applications/consumers could use the standard web services and access the underlying data.
3) Data Quality - Data Access is controlled data services, which in turn allows data quality to improve at the central location. 4)Consistency - Using data services could drive consistency in accessing data from the data store and eliminate different data provisioning approaches.
There are vendors who provide data service tools, they can be categorized into
1. Volume Based Approach
2. Data Type Based Approach.
Hope this blog post provides an insight into Data Services.

Monday, April 4, 2016

SQL Server 2016 - Query Store

Performance tuning is a constant topic of discussion, also one of the top priority items when it comes to good code development, better performing queries provide quicker response times, users/customers are satisfied. There have been constant improvements in the area of performance tuning features in SQL Server, also in the later releases of SQL Server, the execution plans were greatly improved. It is very important for developers to understand execution plans so that they improve queries that cause bottlenecks in the application. One of challenges have been to effectively store execution plans and queries so that they could be reviewed for bottlenecks and improve them. In SQL Server 2016 the concept of query store has been introduced. This Query store feature helps one to gain insight into the query plans and statistics.
In order to enable query store feature in SQL Server 2016, one can use Transact-SQL:

ALTER DATABASE AdventureWorks2012 SET QUERY_STORE = ON;

The query store feature in SQL Server 2016 can also be enabled through SQL Server Management Studio, by choosing the database properties/query store page.
Quoting from Microsoft MSDN Page: I am listing a couple of scenarios for using query store feature:
  • Quickly find and fix a plan performance regression by forcing the previous query plan. Fix queries that have recently regressed in performance due to execution plan changes.
  • Determine the number of times a query was executed in a given time window, assisting a DBA in troubleshooting performance resource problems.
The MSDN link for Query Store is listed below: The page below provides a more completion explanation of query store.

Friday, April 1, 2016

Data Gravity...

With the advent of Big data, cloud technologies , data science there has been lot of interests in moving towards cloud based solutions for Business Intelligence and other applications. More companies/business are looking towards cloud deployments or showing interest in cloud based solutions. While all this has been going on, there also has been a tremendous growth in volume of data and information being shared and requested. The data volume has also bought into perspective the notion of whether data needs to be present on-premise or should data be moved to the cloud. While researching the ideas being suggested around data volumes, i came across the concept of data gravity. Jen Underwood , Data Analytics and BI expert, has written an excellent article covering the concept of data gravity. Please see the link below for the full article.
http://www.jenunderwood.com/2016/02/27/hybridbi/
She covers the concept of data gravity, the type of solutions available and also some of the vendors who offer solutions. The concept of on-premise vs cloud based BI/Data storage provides interesting insights and would be very useful for folks who are planning their data storage and provisioning strategy. The concept of stretch databases are also covered in the article above. An interesting read on data gravity and types of solutions being offered.

Wednesday, March 30, 2016

Data Modeling...

In today's world of ever growing data and information, one of the areas that has been kind of battling the existence has been data modeling, there are wide ranging opinions about the validity of data modeling both positive and negative. One of the viewpoints favoring data modeling has been to provide a context around the data that needs to be accessed and used, how it can be stored and resented to users. There are number of situations where how the data being organized has to be presented to the business in a concise fashion. There are different types of models within data modeling like conceptual models, logical models and physical models. What do these type of mean and how they differ from each other is very concisely presented in the article below. Quoting from the article: "My uses of conceptual, logical, and physical come from the Information Engineering (IE) methods of data modeling". The article has been written Karen Lopez: Senior Project Manager and Principal Consultant, InfoAdvisors, Inc. 
http://www.datamodel.com/index.php/articles/what-are-conceptual-logical-and-physical-data-models/

I hope the above article provides good explanation especially for folks who are getting into the area of data modeling.