Golang for data analytics

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Golang for Data Analytics
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Introduction

• Organizations collect vast amounts of data.
• This mass of data tell facts that are relevant
  for key decision making.
• Data insights help businesses understand
  challenges and devise solutions.
• Due to this, demand for Data Analytics
  applications is on the rise.

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Golang

• Golang is a modern language which is procedural,
  imperative and modular.
• Googles Golang helps build scalable and
  efficient solutions.
• Go is suitable fit for Data Analytics solutions
  and at every step of the data analytics process.

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Data Gathering

• Data Analytics application should be able to
  collect and store vast amounts of error free data
  that takes into account logical, cost and privacy
  considerations.
• It should also be able to store incoming data
  that can be modeled and reported while also
  joining data from multiple sources in a logical
  manner.
• There are many Databases in Golang such as
  InfluxDB, Minio, CokroachDB. Go has several APIs
  for all of the commonly used datastores such as
  Mongo and Postgres. This kind of resource backup
  makes it easy for Golang Data Analytics
  applications to collect and organize data.

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Processing and Analyzing Data Sets

• The next step is to Process data sets to clean up
  messy raw data.
• Algorithms are applied to build and validate data
  models while performing machine learning/ deep
  learning.
• In Go the gonum organization powers data science
  computations by providing numerical
  functionality. Floats, Matrix, Stats, gograph are
  Golang projects related to data analytics,
  statistics and arithmetic.
• They help develop arithmetically sound and
  comprehensive Data Analytics applications.

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Visualizing and Communicating Results

• Good data visualization of results means sound
  decision making by users.
• Application should convey results of
  investigation in a way that makes sense and can
  be easily communicated.
• Golang projects such as gophernotes, dashing-go
  and gonum plotting make it easy to create
  powerful visualizations. Creating Custom APIs for
  this purpose and utilizing resources such as D3
  contribute to the comprehensiveness of Golang
  Data Analytics applications.

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Conclusion

• At Gowitek we have worked on several Data
  Analytics projects spanning industries such as
  Agriculture, Manufacturing, Healthcare, Retail
  and more.
• Scalable and efficient Data Analytics
  solutions strongly support business goals and
  solve core challenges. 

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