What is data workflow automation?

To start diving into the subject of Data Workflows automation, we need to explain what a workflow actually is. Put simply, a workflow is the way that information or data travels between objects or the steps of how it is processed.

We can already see that, since there are several steps to that processing, and each of them takes care of a specific task, such as a transformation, an aggregation, or any other data operation, there is value in automating these processes.

Why do we automate?

The reasons for that are obvious – automation will shorten overall processing time and eliminate human errors, which in turn will boost productivity and efficiency to increase profit in the end – and those are just a few benefits that come with it. Even more appealing is the fact that it can be applied in so many situations and environments since nowadays the world is heavily digitized, and information flows everywhere; thus the possibilities for implementing  automation are nearly endless.

There are countless automatization tools on the market that differ in the functionalities that they provide, but all serve the same purpose – to automate something. To choose one of those, deep knowledge about the process that is about to be worked on is required.

Let’s discuss how a workflow can be automated then.

How do we automate?

Once the tasks that are about to be automated are identified (and usually those tasks are manual ones, as the biggest gain from automation is from those), the goal of modification needs to be clarified. What do you expect to achieve? A faster process? More control? This decision will influence how tasks will be modified. Often these manual steps are very repetitive, so, depending on the environment that they work in, it should be relatively easy to code them.

Let’s take, for example, a hypothetical situation where spreadsheets from retailers are received by someone in the office. Then this person uploads them to some shared storage, from where another employee imports them into a relational database. This is only one part of the imaginary process, but already there are several visible points where automation can be easily introduced with high gain.

First, all retailers should send their spreadsheets to one designated mailbox. Next, a script will check this mailbox for excel attachments, download them, and upload them into storage. Subsequently, another script will take files from storage and upload them into a database. Additionally, those scripts can also check data quality, automatically inform the appropriate people about file errors  and fix simple problems.

The imaginary process above is just one example of how automatization can be implemented into a data workflow which includes repetitive manual steps.

How to automate data workflow?

Let’s now concentrate on some other examples of how to make data workflows more automated.

  1. Replace repetitiveness.
  2. Implement Data Quality to reduce the workload needed to fix errors in later stages.
  3. Introduce scripts for data input (from mailboxes, storage, external systems, etc.).
  4. Automate error reporting to improve process stability.

Automation tools.

Let’s head back to data workflows automation tools and briefly talk about some of them and how they can be used. Some of them might seem like simple schedulers, but in fact, those are powerful tools with multilayered logic. Since nowadays, most data processing is done in a cloud environment, let’s concentrate on the ones that integrate well there and, at the same time, are universal enough to work on most cloud platforms, as well as some cloud specific solutions.

  1. Airflow
    An open-source project, runnable on many existing cloud environments due to the fact that it is written in Python, a very customizable and powerful automation tool. Airflow can be easily deployed on Google cloud by using Cloud Composer.
  2. Oozie
    A multiplatform tool written in Java and JavaScript, designed to be used with Hadoop.
  3. Azure Data Factory
    This might be called an orchestration tool, but the number of connectors and plugins that it supports places it rather in the automation category. As its name suggests, it works in Azure cloud.
  4. Boomi
    A commercial solution for workflow automation provided by Dell which is quite advanced and configurable. It works as an iPaaS (integration platform as a service).

Management automation tools.

Apart from the data processing itself, data workflows can be automated on a totally different level – workflow administration. When everything works fine, no one pays any attention, but when something goes wrong, and data is not delivered – that’s when questions start to pop up.

On such occasions, some management automation could be beneficial. Something that monitors the environment, platform and automation tools as well. There are of course applications for that, let’s just name a few: OpenStack, Apache CloudStack and CloudHealth.

Once we have identified automation spots, chosen data workflow automation software and taken care of management automation, we are set to go.  Contact us to get more information how we can help You

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