Growing Significance of MongoDB in Data Science Field

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May 9, 2014
Growing Significance of MongoDB in Data Science Field
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What is Data Science?

Data science is the study of the generalizable extraction of knowledge from data. It incorporates varying elements and builds on techniques and theories from many fields. Data Science is not restricted to only Big Data, but the fact that data is scaling up,  makes Big Data an important aspect of data science.

Growing Requirement for Data Scientists:

A data scientist is a devout practitioner of data science.  They solve complex data problems through employing deep expertise in some scientific discipline. It is generally expected that data scientists will be able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects are not required.

Good data scientists are able to apply their skills to achieve a broad spectrum of end results. Some of these include:

  •  The ability to find and interpret rich data sources
  • Manage large amounts of data despite hardware
  • Software and bandwidth constraints
  • Merge data sources together
  • Ensure consistency of data-sets
  • Create visualizations to aid in understanding data
  • Build mathematical models using the data
  • Present and communicate the data insights/findings to specialists and scientists in their team

Data scientists are an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and analysis, that can help businesses gain a competitive edge.

According to IBM’s James Kobielus, core data scientist aptitude includes Curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature and these are widely distributed throughout work forces everywhere.”

  •  As more data discovery, acquisition, preparation, and modeling functions are automated through better tools, today’s data scientists have more time for the core of their jobs: statistical analysis, modeling, and interaction exploration
  • Data scientists are developing fewer models from scratch. That’s because more and more big data projects run on application-embedded analytic models integrated into commercial solutions
  • Open source communities and tools will greatly expand the pool of knowledgeable, empowered data scientists at disposal, either as employees or partners.

Why Data Scientists should learn MongoDB?

MongoDB® provides a mechanism to store and retrieve data in relaxed consistency model with advantages like horizontal scaling, higher availability and faster access.

  •  MongoDB® (from humongous) is reinventing data management and powering Big Data as the world’s fastest-growing database.
  • Designed for how we build and run applications today, MongoDB® empowers organizations to be more agile and scalable.
  • It enables new types of applications, better customer experience, faster time to market and lower costs.

Please read why mongoDB® is emerging as the number 1 NoSQL database in the industry and the real world use cases of MongoDB for more information. 

A broadly adopted NoSQL database, MongoDB® is used by companies including foursquare, eBay and Disney for agile, scalable application development.

What is Precog and how does it work with MongoDB?

Precog is a data science platform that enables developers and data scientists to perform advanced analytics and statistics using Quirrel, the “R for Big Data” language.

  •  The Precog data science platform offers an end-to-end solution for programmatic Big Data analysis: from capturing and storage, to cleaning and enrichment, to deep analysis designed to power intelligent, insightful features inside applications.
  • Precog is ideal for heterogeneous data, normalized and denormalized data, whole data analysis, complicated analysis and data integration.
  • Precog for MongoDB® bundles Precog’s core data science platform and Labcoat, Precog’s interactive data analysis tool into a free package that anyone can download and deploy on their existing MongoDB® database.

Why is MongoDB the perfect choice for developers?

  •  MongoDB® developers create software that developers love to use.
  • Quirrel is designed to analyze JSON, which is natively supported by MongoDB®.
  • MongoDB® has a basic query and aggregation framework, but to do more advanced analytics, you have to write lots of custom code or export the data into a RDBMS, both of which are very painful.
  • Precog for MongoDB® gives the ability to analyze all the data in MongoDB® database, without forcing one to export data into another tool or write any custom code.

How evolving platforms are suited for MongoDB:

 Pentaho’s newly released Business Analytics 5.0 platform introduces over 250 major improvements, including expanded support for MongoDB®.

  •  The integration lets customers take advantage of the document database to more easily meet increasing requirements for big data in businesses today.
  • According to Pentaho, Business Analytics 5.0 is the first BI solution to offer full support for MongoDB® cluster replication and failover.
  • The platform also lets users direct how reads and writes are routed to database nodes, and leverage native MongoDB® features such as replication and data aggregation to accelerate querying.
  • MongoDB® promises to make data more accessible for business users while improving developer productivity via automatic document sampling, schema generation and other user-friendly functions that are built into Business Analytics 5.0.

As the MongoDB® ecosystem continues to grow, tools like Pentaho Business Analytics 5.0 provide critical capabilities for the enterprise to help make it easier to both orchestrate data movement between other systems and MongoDB®, using drag and drop tools, and provide business reporting.

How is MongoDB  emerging as the DB platform of choice for advanced data sciences algorithms to be carried out efficiently?
  •  MongoDB® is growing its ecosystem with new partnerships and open standards.
  • MongoDB® rolled out a Hadoop connector, that lets users reduce data movement and optimize performance by storing MongoDB® binary JSON (BSON) backup files in HDFS.
  • The software also lets data scientists use SQL-like Hive queries instead of native MapReduce, which can be somewhat difficult to grasp.
  • The new connector is designed to make MongoDB® more viable for Hadoop-based data warehouses, ETL workflows and near real-time services that require a steady stream of data.

Edureka provides a comprehensive data science course for those who wish to become a data scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study.  Edureka also provides MongoDB course that helps you master NoSQL databases. This course is designed to provide knowledge and skills to become a successful MongoDB® expert.

Got a question for us? Mention them in the comments section and we will get back to you.

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Introduction to MongoDB

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