SQL Server vs MongoDB vs MySQL vs Hadoop and HBase NoSQL

SQL Server vs MongoDB vs MySQL vs Hadoop and HBase NoSQL

Microsoft SQL Server is one of the mainstream databases used in most operational systems built using Microsoft technology stack. One of the biggest shortcoming is the inability to support horizontal scaling / sharding. So the next logical choices that are most nearest to SQL Server would be MySQL.

In case you are looking for horizontal scaling / sharding, that would mean that you are gearing up to deal with Big Data. MongoDB is the arguably the first logical step in NoSQL world, in case if someone is considering to experiment with NoSQL to handle BigData. At the stage, one is faced with the requirement to compare all these databases.


Many forms of analysis depends upon data from third-party, and platforms like Windows Azure Marketplace are based on the same principle.

Social Analytics is widely used to forecast the impact on the business and extract insights to counter the same. The interesting question here is, what is the data source that can be used to calculate / derive sentiments of customers related to the respective business ? A majority of this data would come from social / professional / collaboration forums. Examples of such sources are Facebook, YouTube, Twitter, LinkedIn, PInterest, IMDb, Blogs etc. Anyone would agree that the analytics derived from unstructured data created by the public interaction on social media can be expected to be much more close to precision than even any data mining algorithm. But the big question here is, the amount of data – very very very big data. On a daily basis, there are 400 million Tweets, 2.7 billion Facebook Likes, and 2 billion YouTube views. Even these figures might have been outdated today.

Say an organization is influenced by Sharepoint 2013 enhancements related to social media collaboration, and intends to add an ability to derive sentiment analysis in their client offering. Let’s say that as a starting source, Twitter is selected as the source of data, and all the public tweets for a particular product would be analyzed and the results would be stored for future use.

The first challenge is that according to a study, Twitter generates approximately 1 billion tweets in less than 3 days. So how to deal with processing such a huge amount of unstructured data and just consider the kind of infrastructure required to handle this processing. To proceed with the case study, let’s say that we live in the age of cloud and we just signed up on AWS and have beefed up a fat Amazon EMR that uses Hadoop and HBase NoSQL database.

Elasticsearch is also a very powerful value addition to any relational dbms like SQL Server, Oracle, DB2 etc, provided it’s used wisely. Before we look at how to use elasticsearch with SQL Server, we should look at “Why to use elasticsearch with SQL Server”. This question holds the key to the answer.
SQL Server hold data either in relational form or in multi-dimensional form (through SSAS). Full Text Search (FTS) in SQL Server is capable of providing some out-of-box search feature, but when search queries requires exhaustive searching over huge datasets, and add some complexity in the search definition itself, one can evidently see performance impact there. Elasticsearch is primarily a search engine, but loaded with features like Facets and Aggregation framework, it helps solve many data analysis related problems. For example, everyone of us would have visited sites like Amazon.com, Ebay.com, Flipkart.com etc. Whenever we search for a product, it builds all the dynamic categories, ranges and values on the fly. For such features, a product like elasticsearch can be extremely helpful. One such real project example can be read from here.
One of the best explanations on setting up elasticsearch JDBC river with SQL Server, can be read from here.

One point to keep in view is that, if you setup a river and you restart elasticsearch server, the river would execute the query set for the river again. This could result in reloading of the entire data in the index. In case if the IDs are being fetched from the source, all existing records would get updates. But if IDs are autogenerated in elasticsearch, this would result in new records, which would ultimately lead to duplicate data. So use the river cautiously. You can also delete the river once data is loaded into the index, in case its a one time activity for one time data migration.


Microsoft Certified Solutions Associate (MCSA)




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