Automating Data

In today’s world, where information holds the highest precedence, it won’t be wrong to say, “Data is the new gold.” Every move we make now is a data point; running an errand to the supermarket where we earn points by scanning the app with our details filled out, making a reservation at a hotel, or signing up to watch a movie online, data today is digitized.

Importance of Digital Data

Until a decade ago, when paper records used to hold information, storing wasn’t the only problem; it needed to be organized for quick and easy access, but the major issue began when the number of records increased. 

Data in digital form has a lot of significance, it is easy to manage, and with infrastructure backed by high-capacity storage, cloud storage, storing data is a few clicks away, and these storages come with a very efficient way of indexing the data that enhances the overall data management process. 

With smartphones being as powerful as a computer, accessing data and information nowadays takes less than a minute. But even though digital data is easier to manage and access, the problem sets in as every business platform works with millions of unique data points.

Data Engineering

In order to address the billions of datasets, data engineering brings along a series of sequential processes: 

• Data collection: Data collection is the process of fetching or gathering of data from different sources.
• Data cleaning: Fetched data can have several discrepancies that need to be addressed. 
• Data transformation: This is the process that comes after the data has been collected. This process is all about transforming the data to various formats for further use.
• Data storing: After the data has been transformed to the preferred format (which might vary depending on the use case and the project), it is stored in data warehouses or data lakes, depending on the type of data.

DevOps and Data

To figure out how to automate data, let's dive into each of the processes in the same order:

• The first step towards making sense of data is by collecting data. Relevant data needs to be gathered from different sources, and there are various ways to fetch data: 
  •  Rest APIs
  • Using alternative approaches with programming languages such as Python
  • Query languages such as SQL
• Data preprocessing follows data extraction, and the first step in preprocessing is data cleaning. The gathered data might have lots of irregularities, such as duplicates and missing fields.
  The following processes could help address the discrepancies in data:
  • Data Field Removal
  • Imputation
• After the data is cleaned, it needs to be transformed to maintain consistency and into a format that can be used for further processing.
• The cleaned data needs to be transformed such that it can be fed as an input for further processing. The data might need to be converted into a readable format. Python programming language with packages like pandas and JSON can efficiently convert data into various formats.
• Data loading is the process where cleaned and transformed data is loaded into a data warehouse or data lake solution. Solution-specific libraries make it easier to automate the loading of data programmatically.

DevOps allows us to build data systems where we can implement:

• Extraction with Python and SQL from data sources
• Cleaning with Python package like pandas (dropna, fillna methods)
• Transforming to JSON or CSV programmatically with Python
• Loading to data storage solutions with HTTP methods like Python requests
• Scheduler (like CRON or Celery) to schedule the run of the processes from start to finish 

With DevOps bringing together all the processes, automation becomes feasible. An automated pipeline, such as ETL, using tools like Jenkins, enables the flow of data in a continuous manner without manual intervention, increasing run frequency and, in turn, efficiency.