Fig. 2: Gaps between software and data engineering
Although data engineering has risen as a specific skill from the software engineering profession, there are very specialized activities and processes central to data, that make it a distinct and complete function in itself. The 3 tenets of people, process, and technology significantly transform to form data engineering practice to amplify the business value of analytics.
Most organizations typically approach data engineering using the traditional software engineering practices that they have established internally. This could adversely impact the business outcomes. Data engineering has a different development and training process. A regular software engineer can’t be just asked to perform the role of a data engineer.
Data processing cycle
Fig. 3: The Data Processing Cycle
Driven by the three core tenets, a typical data engineering lifecycle focuses on acquiring necessary data, organizing it, making it available for analyses, and subsequently deriving insights.
Preparing the acquired data involves using various tools to improve data. Common improvement parameters or indicators include:
- Data integrity
The objective of this exercise is to make the data ready for further analysis and involves various operations such as supplementing metadata, data correlation, data munging, and application data security strategy.
The next stage of the data preparation process involves making the data more insightful by focusing on context. This includes various operations like data labeling and data modeling.
Another critical data engineering functionality is creating and centrally managing cloud databases and storage systems like data lake, and enterprise data warehouses. Database-centric data engineering functions such as these are focused on populating analytics databases. In fact, data standardization, preparation, and enrichment are some of the earliest stages of this process. Once the data has been extracted and modified and reformatted, it is loaded into the data warehouse.
Analytics and action
Once the data has gone through these stages, it is ready to be visualized and analyzed. This marks the shift from observational data to actionable information. The first stage of this process is data insight which essentially refers to understanding data through analysis. This can be either real-time, interactive, or batch. Finally, the results of the data insights stage are used to drive actions, outcomes, and further assessments.
One critical aspect of data engineering that serves as a natural requirement across every stage of data processing is data governance. It is the process of managing the availability, integrity, security, and usability of data within internal systems on the basis of internal data policies and standards that control data usage. A well-thought-out data governance plan can help companies ensure that data is reliable and trustworthy across channels and is not misused.
Over time, efficient data governance has become a defining factor of a successful data strategy, considering how important it is for organizations to comply with stringent data control and privacy regulations.
The tools of the trade
The data engineering process is a complex and intricate affair and requires several tools to store and manipulate data. However, there is no one tool that can get the complete job done. The process, therefore, requires a combination of data engineering tools and technologies that need to be used either simultaneously or sequentially to get the desired results. Some of the different types of tools needed to develop a data pipeline include:
A holistic approach for data engineering initiatives
Essentially enterprises need to adopt an integrated platform for end-to-end data engineering initiatives instead of stitching together piecemeal solutions aligned to separate processes. To achieve this, an approach such as the one below can help.