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

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.