It is now apparent that every data team, irrespective of company size or complexity of their environment, needs data engineers. Even though data teams now comprise a variety of specialized roles, data engineers ensure we have access to reliable data when we need it so we can maximize the ROI of our data investments and optimize our business decision-making. What do these Engineers do and why are they so valuable? We will answer these and other questions in this article, and explore this critically important role that is rapidly becoming one of the most important parts of every modern data team.
What is a Data Engineer?
Data engineers create systems that gather, handle, and transform unprocessed data into information that data scientists and business analysts may need to evaluate in a number of contexts. Their ultimate objective is to open up data so that businesses can utilize it to assess and improve their performance.
The "engineering" element is the key to answering the question, “What is a data engineer?”. Engineers create and construct things. Data engineers plan and construct pipelines that transform and ship data into a format that is highly usable when it is received by data scientists or other end users. These pipelines are required to gather data from various unrelated sources and combine it with data from other sources into a single warehouse that acts as a source of truth for all of the data.
You've probably heard or read about Gartner's finding from 2017 that 85% of big data projects fail. This was mostly caused by the absence of trustworthy data infrastructures. Data could hardly be relied upon for important business choices. That’s a hugely significant amount of waste, yet hardly anything has changed since Gartner published this. In 2019, Deborah Leff, CTO for data science and AI at IBM, said that 87%of data science projects never reach production. Gartner reaffirmed its forecast that only 80% of initiatives would fail as of this point. Similar statistics were found in a New Vantage Report by EditSign.
Why is Data Engineering So Crucial Right Now?
Most businesses are in the middle of their digital transformation as they move data and workloads to the cloud and find ways to digitize processes. This is leading to the creation of unfathomable amounts of new sorts of data, as well as far more complex data, being created, analyzed, and processed at a higher frequency. While it was previously clear that data scientists were required to make sense of it all, it was less clear who would need to manage and guarantee the quality, security, and accessibility of this data in order for data analysts and scientists to do their jobs efficiently.
Historically, it has been difficult to place responsibility for who ensures data quality and availability. The confusion used to be between data scientists and everyone else in the organization to provide data, yet the skill set and expectations for their position may not have included this. As a result, data modeling was not being carried out properly. Data scientists weren’t utilizing data consistently, and there would be duplication of effort. Companies failed because these problems made it difficult to get the most out of their data efforts. Additionally, it resulted in a high turnover rate for data scientists that continues to this day.
It was necessary to address these issues with a role that managed quality and availability so that the entire organization could benefit from their data investments.
What Skills Do Data Engineers Need?
Data engineers need to have specific skills to build software solutions for data. At the same time, it's probably unrealistic to expect data engineers to be familiar with the wide variety of tools and technologies necessary to achieve this at scale, especially as these tools are constantly changing. The necessary skills also depend on the industry in which the data engineer is working. Jeff Hale, a published export writer and educator on the topics of data science and data engineering, recently conducted an analysis of the most sought-after skills required of data engineers across three job platforms.
In the past, the data effort for most enterprises was on building traditional data warehouses, providing BI reports, and improving platforms, all in an environment where this was expensive and not very extensible. Today we are building with new tools for modern data environments which are far more complex and require the work of different skill sets. If your on-premises data center runs out of storage space, you'll need to buy another expensive appliance before you can add data or calculate capacity. It takes months, effort, energy, and money. In the modern world of data, you can easily set up another cloud-based service within minutes and quickly expand your data processing power. And to do that, you need the resources and knowledge of an effective data engineer.
Today, we build data lakes and real-time data streams. Data engineering is required to build a pipeline that fills these lakes. Pipelines connect data from sensors, devices, social media, ERP systems, and third-party data marts. But it's not just about leaving old sources. Pipelines are needed to move data from legacy systems, existing warehouses, and legacy applications to where they can be consumed. However with the number of moving parts in this complexity of items, it requires specialized engineering skills to accomplish all of it harmoniously. Some of the typical skills that data engineers bring to the table include:
- Stream processing with modern frameworks like Apache Kafka, Apache NiFi
- Data Lake technologies such as Hadoop, AWS S3, GCS based cloud Lake Houses, Data Lakes and Data Warehouses
- Data Transformation frameworks such as Apache Spark and DBT
- Also orchestration of pipeline technologies such as Apache Airflow, Prefect and Dagster
How Hard is it to Get a Unicorn?
It's not a surprise therefore that data engineering is a coveted skill set right now and people are looking for data engineers all over the world. By that same measure, it is clear that it's very tough to find these exceptionally talented people, and the density of these data engineers can change the outcome of Data-Driven Transformations. We have oftentimes seen the evolution of software engineering talent into data engineering talent, but it will for sure need some patience on the part of engineers and the enterprise as well.
Learn more about how data engineers manage data environments with a tour of the Acceldata data observability platform.