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Golang for data analytics

Golang for Data Analytics Applications is a suitable choice because of its standard official libraries which enable easy data parsing, sorting, analyzing and visualizing.

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Golang for data analytics

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  1. Golang for Data Analytics

  2. 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.

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

  4. 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.

  5. 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.

  6. 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.

  7. 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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