Golang for Data Analytics
Golang Databases and projects such as InfluxDB, Gonum, and Gophernotes allow vast Data gathering, comprehensive analysis, and powerful visualizations. Analytics applications for Manufacturing, Retail, Agriculture and other domains can help analyze data collected from IoT Devices to assist organizations take more sound and efficient decisions.
Data Gathering
The process of data Analytics begins with Data Collection. Any Data Analytics application should be able to collect and store vast amounts of data. Collected data must be free of errors, should be able to take logical, cost and privacy considerations. An ideal Analytics solution should also be able to store incoming data such that further modeling and reporting is possible. It must further enable joining data from multiple sources in a logical manner.
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Databases in Golang such as InfluxDB, Minio, CokroachDB are proof that Golang is suitable for data gathering and organization. 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.
Processing and Analyzing Data Sets
The following sequential step is to Process data sets to clean up messy raw data. This step helps to move data further into the data modeling process. Usually, this cleaned data is stored as a fresh data set for programmatic pre-processing. Algorithms are then applied to build and validate data models while performing statistical analysis and machine learning. In Golang the gonum organization powers data science computations by providing numerical functionality. Floats, Matrix, Stats, gograph are all great Golang projects related to data analytics, statistics and arithmetic. These help Golang programmers develop arithmetically sound and comprehensive Data Analytics applications.
