Extract better insights with data automation software

With the preponderance of data being generated and processed today, data automation software has become a mainstay of modern business practices. However, where do you start when it comes to capitalizing on the right kind of software for your business? Read on to find out more.

What is data automation?

Data automation refers to any activity which uploads, processes, or otherwise handles data using automatic tools, rather than performing them manually. A data automation example would be updating a database programmatically instead of the engineering team manually uploading or reformatting data. This can be achieved with a number of different data automation tools.

The benefits of data automation

The benefits of data automation are manyfold:

• Increased speed – a machine can carry out data operations much faster than humans, with time savings increasing as datasets become larger
• Improved data quality – less manual processing results in fewer human errors
• Better scalability – any changes required can be quickly propagated throughout the data pipeline, whereas manually updating tasks requires the work of data experts
• Better use of talent – automation takes care of repetitive tasks, such as standardization and validation. This frees the data engineering team to focus on more productive work, such as high-level analysis which can inform mission-critical initiatives.
• Lower cost – all of the above result in a lower total cost incurred for processing data. Producing more accurate datasets, quicker, will speed up business analytics, which in turn results in a faster turnaround for more profitable activities.

The four types of data automation pipeline

1. Batch data pipeline – process or transfer the entire dataset from source to destination in one go. This is either carried out periodically or at predefined intervals. For example, you can transfer data from a CRM system to a data warehouse on a weekly or monthly basis.
2. Streaming data pipeline – process or transfer data continuously as it is created at the source. For example, you can use a streaming data pipeline to move real-time data from multiple sources into ML (Machine Learning) algorithms for analysis to make product recommendations.
3. Change data capture pipeline – rather than process or update the whole dataset, these pipelines only process or update the differences made since the last sync.
4. Source data automation – this refers to the practice of extracting data from a source system in real time. For example, scanning ticket QR codes at an event to authorize entry and update the guest list in real time.

Can Python be used for automation?

It absolutely can. In fact, many sophisticated tools are built on top of the Python programming language, making it a vital element in data automation software. For example, you can automate the ETL (Extract, Transform, Load) process automation using Python can achieved with the following steps:

1. Hire engineers – this may be a single engineer or a team
2. Procure the credentials – these are needed to access the data sources, and must be provided to your python environment
3. Write the extraction code – to connect the various data sources
4. Produce the cleaning code – to clean and standardize the raw data
5. Write the transformation code – this applies data “recipes” (i.e. business rules) to process the data
6. Create the loading code – to load data into the target destination
7. Perform ETL testing – test the code and analyze the results
8. Set up monitoring and alerting – to manage your server’s resources and the scaling of the data

Which is the best software for automation?

Often, the difficult question is not the ‘why’ of automation, but the ‘how’. Do you build a solution from scratch, pay for a ready-made tool, or use some mixture of the two? To answer this question, you’ll need to look at some data automation tools.

If you’re building your own data automation software, or at least introducing your own components, you may benefit from the flexibility of free and open-source tools. Big automation software names include Prefect and Airflow. You can use these to build a complete data automation pipeline, or include them as part of it.

On the other hand, if you’re looking for a ready-made solution, perhaps with some customized code, then data automation platforms can help, and often provide the most cost-effective path. There are a plethora of tools out there, but the important thing is to set out your needs first, so you can compare options and choose the most suitable software.