For every company, data is a strategic asset. The data a company generates and stores is unique to them, similar to fingerprints. As the amount of data being produced by operational systems continues to grow unabated, companies are having an increasingly difficult time processing the data for the business intelligence and data science teams to analyze and make predictions. Data engineering teams have switched or are switching to Apache Spark, a distributed, scalable processing engine. But, getting the most out of Spark requires programming, typically in PySpark or Scala. This coding requirement limits the number of people that can do data engineering on Spark as well as increases the variability of data pipelines as different people have varying levels of programming expertise.
The solution to these issues is low-code data engineering (low-code DE), which is a visual way to develop, deploy, and manage data pipelines, all in a drag and drop manner. Low code means that anyone that wants to do data engineering does NOT need to know how to program, which will increase productivity and allow more people to do this work.
There are four main characteristics that are vital to low-code data engineering:
1100% Native Spark Code
While the pipelines are created using a drag and drop visual interface, actual code needs to be submitted to Apache Spark to process the data. 100% native Spark code means that the visual representation will generate either PySpark or Scala code. This is extremely important as it means that you can then run this code on any Spark deployment, whether in the cloud or on-premises. 100% native Spark code eliminates proprietary vendor lock in! It also means your Spark experts can look at the code and ensure the code is performant and accurate.
2Extensibility and Standardization
Most companies have data transformations and/or sources that are unique to them. One issue with low-code DE is that the out-of-the-box visual representations don’t have your business logic (which is to be expected). The visual representations need to be extensible to encapsulate your custom data frameworks. There is a second, major benefit to extensibility, which is standardization. Writing code is a manual process and is heavily dependent on the expertise of the programmer, which adds a lot of variability. When everything, from run-of-the-mill to custom transformations are available as visual representations, the code generated is standardized. This standardization means anyone can look at the code and understand what it is doing, without having to account for the quirks for the original programmer.
3Operational Efficiency
Developing data pipelines, especially complex ones, is a team sport. As multiple data engineers work together, it becomes vital that they can collaborate without getting in each other’s way. This is where data pipelines need to utilize software engineering best practices for mission-critical applications. Since low-code DE should generate 100% native Spark code, this can be stored in your private Git repository. When individual data engineers are working on a portion of a pipeline, they check out the code, make their changes to a branch, and then merge it. Testing ensures the changes don’t break the data pipelines and CI/CD will dramatically reduce the risk in moving from development to production. All of these capabilities will make it efficient for companies to grow from 100s to 1,000s to 10,000s data pipelines under management, without an associated 100x increase in data engineers to do so.
4High-Quality Data
“Garbage in, garbage out” is extremely true for data pipelines. While data analysts and scientists are looking at “processed” data, they often need to be able to track particular data values all the way back to the source. Tracking this lineage easily reduces the time they need to spend figuring out if the data is valid. This gives them the confidence that they aren’t building their dashboards or models on suspect data, invalidating their work. In addition, data sets can be used in multiple applications, dashboards, reports, and models. Lineage is needed to understand all the downstream implications of data changes.
With companies flooded by data and the associated demand for even more analytics and machine learning, low code is a “must have” for data engineering. A visual drag and drop interface to interactively develop, deploy, and manage data pipelines means more data practitioners enabled to do data engineering, each of them more productive. In addition, companies can scale the number of data pipelines they are managing due to operational excellence. Lastly, better data quality enables downstream data analysts and data scientists to trace the lineage of data sets back to the source, giving them more confidence in the data pipeline.
