Consider that you manage an online store that is always open. Every minute or second, users can place orders and pay for the items. Your website will be processing a large number of customer transactions in real-time, including user IDs, credit card numbers, order IDs, and product and delivery details.
This all happens in the blink of an eye, thanks to online data processing systems, which include databases and platforms like e-commerce, CRM, and payment gateways.
In addition to carrying out regular tasks with data processing systems, your system should also be available to assess your business performance. For instance, you may want to analyze the sales of a particular product and compare it against the preceding month.
It implies you need to collect transactional data, process it using data processing tools, and then transfer it to the data warehouse so that analysts and other team members with access to BI interfaces can visualize the sales data.
Wait a minute… How do you transport relevant data quickly and reliably too? That is what data engineering is all about. To create a fast, productive, and efficient data pipeline, you need to have the necessary gear, software, and infrastructure built for it.
Before we go deep-dive into the processes, let’s take a step back and first understand why and how data engineering systems became an integral part of our digital ecosystem.
Human-operated to System-managed Data Pipelines
A Data Pipeline is a system that captures, organizes, and routes the data to different systems, to be utilized further to perform analysis. Within a data pipeline architecture, ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transfer) are subprocesses.
In many traditional industries, the data pipeline is still manually operated by data personnel deployed to update tables on daily-basis for business analysis. It leads to numerous human errors and data breaches, especially for organizations dealing with sensitive information like banks and insurance companies.
The transactional data were typically uploaded every night to ensure that the data is available on the next day and ready for use. However, this approach in data engineering soon became a bottleneck, especially for urgent transactions where businesses and users had to wait until the following day to act on their data.
With the changing landscape of the customer ecosystem and the increasing need for speed and convenience, immediate data access has become an integral aspect of every competitive business.
The volume and the variability of the data also expanded, pushing many companies to opt for cloud storage and computing, for scaling and cost optimization. The data’s engineers are also involved in constructing data pipelines to move data from on-premise data centers to cloud environments.
Today’s business ETL systems must also be able to ingest, enrich, and manage transactions as well as support both structured and unstructured data in real time from any source, whether on-premises or in the cloud.
To better understand ETL data pipeline, visit Factspan
