Why designing an intelligent data engineering pipeline is important for data teams

Why designing an intelligent data engineering pipeline is important for data teams

Every organization today generates massive amounts of data and how they utilize that data it crucial to their success. If even half of these organizations are digitally mature and are able to take advantage of their data, then they are already ahead of their competitors in multiple ways. Data plays an important role for strategic business decision-making when it is gathered, stored, and processed in the right way. What comes next is visualizing and generating reports on the processed data to gain insights on current and future business performance.

It may sound easy, but it is not because there are a lot of efforts an organization’s data team must take to reach that level of digital maturity. One of the main issues data teams are currently facing is managing the volume and variety of data that is being generated through various processes in their organization. As many methods and principles of data science are used to collect, clean, and streamline data for business purposes, data engineering is an advanced step which opens new avenues for organizations to process their data. One of the focus areas under data engineering services is building data pipelines for future data analytics.

It goes without saying that an organization needs to have the fundamental infrastructure, data products, and data engineers in place to take advanced steps towards building a resilient data pipeline. Using data pipelines in the right way helps data teams create clean data sources and generate insights useful to their organization. Let us take a look at what exactly is a data pipeline, the components of it, and why it is important for organizations for rapid and real-time processing of data.

A data engineering pipeline simply consists of tools and specific activities to develop a workflow that ingests, cleans, stores, and modifies data coming from disparate data sources. The output data retrieved is used for real-time data analysis or other data dependent business functions. Data pipeline development eliminates multiple manual steps such as linking data sources, combining that data, and implementing data processing steps that limits an organization’s ability to utilize their processed data quickly and smoothly.

There are 3 main elements that data engineer pipeline consists of and those are – a source, data processing steps, and a destination. A source is from where data comes in such as CRMs, ERPs, IoT sensors, etc. Data processing steps include extraction of data and then filtering, grouping, and transforming it based on different business needs. This data is then deposited in the destination, such as data lakes or data warehouses, for further data analysis. There can be multiple data pipelines between all the three elements as the focus is on creating and transforming data sets that are pre-loaded at the time of data utilization.

Modern data engineering pipeline provides organizations with multiple benefits as they are agile in delivering useful data that lowers costs compared to traditional solutions. Faster decision-making is achieved by data heads when powerful data pipelines send usable data to destination. And handling peak business demands gets easier because quick access to data insights is available due to implementation of resilient data pipeline.