How does Python Help to Build a Data Pipeline for Analytics?

How does Python Help to Build a Data Pipeline for Analytics?

Streaming data that changes quickly is quite daunting to observe and manipulate. As such, businesses that deal with such huge amounts of data find it hard to analyse as analysis id taxing, hard to make sense of, and need to move data between at least two data management systems. For this movement to happen, the many steps involved can instead be merged into a data pipeline. With a data pipeline, all the data under analysis is put in one place and bears the same format, and it is reproducible. In building a data pipeline from scratch, it is best to use Python.

One ubiquitous case in which a data pipeline can be used is when you are making sense of the information on your website’s visitors. This information is very important and especially when given through Google Analytics. For instant a simple data pipeline that seeks to calculate the number of visitors to a particular website each day will consist of log files whose raw logs are deduplicated and written to database then used at the ‘count IPs per day’ stage. These visitor counts are then fed into the data dashboard for viewing. Skills required to create a data pipeline include knowledge of SQL databases, Python and R languages. When building your data pipeline in Python ensure that it consists of the following processes and parts.

  • Gathering Data from Its Sources

These represent where the data is being accessed from and can either be cloud sources and application APIs for site traffic, NoSQL, Haddoop, operational systems, and RDBMS among others. However, in accessing data from these various sources, security controls are observed and for facilitation purposes, data statistics and schema can be gathered to the sources as well. Platforms such as Rookout data collection and pipelining offer services including both gathering data and data pipelining.

  • The Merging Stage

In this stage of building a data pipeline, data from different sources is combined. As such, this stage involves specifying the criteria and logic that determines how the data is going to be combined.

  • The Extraction Stage

This stag deals with discrete data elements consisting of multiple values grouped together in categories that need to be extracted or masked.

  • The Standardization Stage

In this stage, you need to establish standards and attributes such as units of measure, colour, codes and sizes that the data is given on a field by field basis.

  • The Rectification Stage

In this stage, you need to create an error detection and correction mechanism such that errors such as invalid fields can be corrected or reviewed to ensure the data is as correct, accurate and in the best presentation form.

  • The Loading Stage

This involves loading the extracted scrubbed data on an analysing system for analysis based on performance and reliability.

  • Automating

There is a constant inflow of data each day especially when it comes to web traffic. Therefore, it is prudent to ensure that the processes involved in the data pipeline are automated such that they are performed many times, either continuously or on a schedule.

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