By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data-driven white label SEO
    Does Data Mining Really Help with White Label SEO?
    7 Min Read
    marketing analytics for hardware vendors
    IT Hardware Startups Turn to Data Analytics for Market Research
    9 Min Read
    big data and digital signage
    The Power of Big Data and Analytics in Digital Signage
    5 Min Read
    data analytics investing
    Data Analytics Boosts ROI of Investment Trusts
    9 Min Read
    football data collection and analytics
    Unleashing Victory: How Data Collection Is Revolutionizing Football Performance Analysis!
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Dealing with Disruptive Data: Advancing BI Connectors and Integrating SQL and NoSQL Databases
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Dealing with Disruptive Data: Advancing BI Connectors and Integrating SQL and NoSQL Databases
Big DataBusiness IntelligenceUnstructured Data

Dealing with Disruptive Data: Advancing BI Connectors and Integrating SQL and NoSQL Databases

SAsInSumit
Last updated: 2016/04/07 at 3:31 PM
SAsInSumit
6 Min Read
SHARE

Don’t count on databases to have uniform data models. Far from it, you’ll find different databases storing data in a plethora of shapes and sizes. This leads to a number of challenges when it comes time for business intelligence (BI) professionals to pull useful data from non-uniform database structures. The developers building applications versus those building intelligence have vastly different preferences in how to access data.

Don’t count on databases to have uniform data models. Far from it, you’ll find different databases storing data in a plethora of shapes and sizes. This leads to a number of challenges when it comes time for business intelligence (BI) professionals to pull useful data from non-uniform database structures. The developers building applications versus those building intelligence have vastly different preferences in how to access data.

Being able to manage data in whatever form is the heart of a modern challenge that companies of all sizes can expect to face, if they haven’t already. Enterprises need to have the BI tools that can access a full range of databases from structured to unstructured data models. This is especially true when applications become mission critical within the organization and the choice of technologies is often dictated by enterprise BI requirements for the executive leadership.

More Read

RN coders for hosptial data

RN Coders Can Improve Hospital Data Strategies

Big Data & AI In Collision Course With IP Laws – A Complete Guide
4 Ways AI Can Enhance Your Marketing Strategies
Translating Artificial Intelligence: Learning to Speak Global Languages
Upskilling for Emerging Industries Affected by Data Science

For example, BI connectors and enhanced query languages have been developed for MongoDB and Cassandra to help address this. This query capability is layered on top of these NoSQL databases to build standard-based connectivity, processing non-tabular and unusually-shaped data and drawing valuable intelligence from it. BI connectors for NoSQL are hugely advantageous when dealing with different database systems, enabling BI professionals to retrieve data across diverse environments, but there is still room for improvement – especially around integrating data from NoSQL databases.

While SQL databases are relatively more established data storage systems, alternative NoSQL databases have recently been increasing in their popularity thanks to their scalability and flexible data models. MongoDB is currently the most widely downloaded NoSQL database, but it’s not alone. Among the other well-engineered NoSQL databases worth noting are Cassandra, MarkLogic and Couchbase. These feature sophisticated data intelligence interfaces: Couchbase supports the N1QL data access layer, a solution to the problem of accessing and processing JSON data, while MarkLogic supports XQuery, a very powerful and composable layer; Cassandra offers CQL, which is similar to SQL in that data is organized in tables containing rows of columns and supports prepared statements that allow developers to parse a query once and execute it multiple times with different concrete values.

With this trend, there arises a central need to improve NoSQL query languages and BI connectors across all NoSQL databases. The bottom line is that SQL and NoSQL databases both function to store data, while their divergent approaches make each suitable for different types of projects.

NoSQL has been getting more advanced support for query languages, and there has been a correlated increase in NoSQL technologies. Some vendors, like Progress Software, are building vendor-agnostic BI connectors that provide the ability to work reliably across all NoSQL technologies. And here’s the beauty behind it: In putting logical schema on top of unstructured and semi-structured databases, BI connectors act as a data source for SQL based platforms while actually being an open source NoSQL database.

These BI connectors provide normalized SQL access for NoSQL data models to certain collections for compatibility with existing BI applications, and their flexible data models handle data that lacks uniformity from row to row, storing data in JSON-like documents that are able to meet the latest demands for modern application development. The NoSQL database BI connectors provide compatibility with BI ecosystems that are expecting SQL, structuring with deeply nested data that does not have equivalents in the relational models. Before the concept of normalization for NoSQL databases, select third party vendors were building unreliable BI connectors that flattened the data model.

The industry demands better, and so is heading toward improving queries and complex analytics through standards based SQL connectivity in complex data environments. And in these cases, NoSQL BI connectors are used with different tools to achieve more elegant analytics. This includes data visualization (pulling data out of the connector, then running the visualization and using predictive analytics tools layered on to garner sophisticated data) and data federation (technologies such as Microsoft SQL Server Linked Server that allows NoSQL standards to look like SQL server databases).

These disruptive data sources indicate that BI is getting more complex. However, in a world of non-uniform data, BI connectors that layer on new databases to establish much-needed standard connections enable valuable enterprise data to be pulled from varying database structures. New BI tools keep advancing the state of the industry, making unstructured and semi-structured data seem much less threatening when it comes to integrating SQL and NoSQL databases.

SAsInSumit April 7, 2016 April 7, 2016
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

RN coders for hosptial data
RN Coders Can Improve Hospital Data Strategies
Big Data
cloud technology in education
How Cloud Technology Can Be Integrating in Schools
IT
big data and IP laws
Big Data & AI In Collision Course With IP Laws – A Complete Guide
Big Data
ai in marketing
4 Ways AI Can Enhance Your Marketing Strategies
Marketing

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

RN coders for hosptial data
Big Data

RN Coders Can Improve Hospital Data Strategies

6 Min Read
big data and IP laws
Big Data

Big Data & AI In Collision Course With IP Laws – A Complete Guide

5 Min Read
ai in marketing
Marketing

4 Ways AI Can Enhance Your Marketing Strategies

7 Min Read
machine,translation
Artificial Intelligence

Translating Artificial Intelligence: Learning to Speak Global Languages

10 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?