If you are a growing data-driven organization, you might have been working to harvest large amounts of data to extract valuable insights from it. This can be costly and inefficient unless the data science team adopts the repeatable solutions to common problems. Although the specifics of organizations may vary, the basic principles remain the same. There are some common features that you can encapsulate into a data pipeline. Let’s look at a common problem and see how we overcame it.
Our team members at Traindex manually performed recurring tasks. These tasks included data cleaning, model training, testing, and so on. By performing these tasks manually, the engineer worked on the same thing again and again. This resulted in slow throughput, human error, and lack of flexibility and centralization.
To overcome this, we envisioned a data pipeline to do all the above tasks with minimal human intervention. We developed and deployed such a pipeline, and it has proven itself to be a gust of fresh air. In this article, we’ll look at what data pipelines are, the benefits of using data pipelines in a corporate setting, and finally, what an event-driven data pipeline is.
A pipeline is nothing more than a set of steps performed in a particular order in simple terms. A data pipeline is a set of processes performed on data from a source later moved to the destination, also known as the sink. The source could be anything from online transactional databases to data lakes, and the sink or the destination could be anything from data warehouses to business intelligence systems. The most common data pipeline is ETL, which extracts, transforms, and loads the data. The transformation process could include anything depending on the business. Here is a detailed data pipeline diagram:
ETL pipeline is a type of data pipeline that performs operations in batches and is sometimes referred to as a batch data pipeline. Batch data processing was very common for a long time. Now there are different types of processing available like streaming and real-time processing. This architecture of the data pipelines has a lot of variety according to your business needs. For example, stream analytics for IoT applications keeps the data flowing from hundreds of sensors and real-time data analysis.
Now that we have understood what a data pipeline is let's discuss why it is important to use data pipelines in modern data-oriented applications.
In modern data-driven organizations, almost all actions and decisions are based on insights gathered from data. Every department of the organization has certain authorizations, restrictions, and data needs. Often the organizations have a single entity that manages the requirements of everyone resulting in a data silo. In such situations, getting even simple insights becomes difficult and leads to data redundancy within departments. The effort required to obtain essential data also handicaps the organization.
Well-thought-out data pipelines result in easy and fast access with right permission roles to data throughout the organization. Anyone from any department can access their desired data with no intervention or interference.
Based on the previously mentioned point, fast access to the data results in quick data-driven decisions. Data supports such choices, and they are less likely to go south.
Well architectured data pipelines can automatically scale up or down according to the users'/organizations' needs. This reduces admins' headache to keep a constant eye and manually add or remove resources as per requirements.
Well-written data pipelines improve data quality. The data becomes more reliable, and executives can make better decisions based on it.
Automated data pipelines run independently and need minimal maintenance and human intervention, thus less paid workforce. Also, their autonomous nature allows them to remove unused resources and save costs.
Since we now understand what a data pipeline is and its benefits, let us see how we crafted a pipeline according to our needs at Traindex.
Based on the problem we discussed at the beginning of this article, we decided on an event-driven pipeline. It runs based only on certain events. We wanted our pipeline to automatically run the data processing jobs, followed by training a machine learning model on the preprocessed data. We also wanted it to run some tests once it’s completed based on a specific event, which in our case, was an upload event.
Moving data to a specified data storage by the user or engineer generates an event. Once they complete the upload, it triggers our pipeline. Scheduling is not optimal for this use case because we don’t know when this raw data will be uploaded in our storage. It can be frequent or occasional, so we went for the event-driven approach.
We learned the importance of mining large datasets efficiently to get the best insights on time to stay ahead of the competition. Modern-day data-driven organizations should consider setting up data pipelines to provide their teams with correct and useful data a click away. Data pipelines can also automate data-driven and recurring tasks like data preprocessing, model training, and testing on a schedule or based on specific events. We hope you have found this article useful, and you may consider crafting some data pipeline solutions for your organization. You can consult your data engineering problems with us at firstname.lastname@example.org