Although it may not seem like it, data engineering is a relatively recent development.
Not long ago, database software solutions met the data needs of most companies. The data these applications generated was small and usually siloed. Typically, a single database administrator could handle almost all of a business’s data management needs.
Not anymore. Companies are adopting digital transformation, and every aspect of business operations is managed by many diverse systems, amassing a vast amount of data. This change heralded the era of Big Data.
Storing, analyzing, and visualizing this data for business intelligence and operations requires a complete infrastructure. Provisioning and maintaining that infrastructure in-house are not trivial tasks and significantly increase the barrier to entry for companies to leverage this data.
Increasingly, these datasets have made their way to the Cloud, giving rise to public cloud solutions that help outsource this complexity and enable pay-as-you-go models.
As more forms of data needed both structured and unstructured analysis, the terminology changed to reflect this new complexity; databases grew into data warehouses, data lakes, and data lakehouses.
And as big data grew, so did business demands. Users don’t care how massive the database is. They want light-speed data aggregation and instant availability.
A single database administrator could no longer handle these complexities, and the role got further specialized. Companies began splitting the role of the database administrator into three specialties: data scientists, data analysts, and data engineers.
Let’s look at the responsibilities for each of these roles, how they figure into a big data roadmap, and the importance of data engineering overall.
What Is Data Engineering?
Data engineering is the process of turning massive amounts of raw data into a format that can be used by data analysts, data scientists, and other business users. Data engineering is critical when dealing with big data.
People often use the metaphor “drinking from the firehose” when talking about big data. More accurately, big data is the flow from the fire hydrant; data engineers are the hose that makes it useable, and data analysts are the nozzle on the hose that focuses the flow. Data scientists are the ones constantly designing more efficient nozzles.
Data engineering creates raw data analyses, predictive models, and short- and long-term trend forecasts. Without data engineering, making sense of big data would be impossible.
Big data doesn’t necessarily mean enterprise-level business. You may have a small or midsized business and still consume enormous amounts of data from external systems, your customers and users, field teams, sensor arrays, and other sources.
Data engineering allows you to implement big data initiatives and realize your overall big data strategy. With data engineering, you can:
- Make accurate, timely decisions
- Create reliable measurements and data models
- Predict future trends with high-quality forecasting
What Do Data Engineers Do?
Data engineers are responsible for designing, building, and maintaining the massive, complex infrastructure needed to collect and store your data. They also create and maintain the data pipeline that allows you to capture raw data from different inputs, process it, and store it in a manner that allows data analysts and data scientists rapid access.
Data engineers have several tools at their disposal. Which tools they use depends on the particulars of your business application and big data project plan, but they might include one or more of the following:
- Relational and non-relational databases
- Extract, Transform, and Load (ETL) tools
- Cloud platforms
- Apache Hadoop stack
- Apache-based big data clusters
- Programming languages (Python, Java, Scala, etc.)
Along with these tools, data engineers need a roadmap that provides a broad view of your business needs and a detailed assessment of its infrastructure. They also need a scalable framework for sharing the data after they’ve cleaned and structured it.
If all that weren’t enough, data engineers are also responsible for data security and integrity, and for supporting and maintaining the data pipeline.
Considering all that they do, it’s clear that data engineers a critical part of your company’s big data implementation plan. Without data engineers, data analysts and data scientists couldn’t do their jobs.
The Role of Data Analysts and Data Scientists
While data engineers form the foundation of your big data plan, data analysts and data scientists build upon that foundation.
Data analysts are your data manipulation and business intelligence (BI) experts. They select relevant data sets and then clean, de-dupe, sort, and otherwise prepare the data for use. In addition, they manage data visualization and business analysis, help identify patterns within a data set, and create your data optimization roadmap.
Analysts review your data and figure out how to use it to solve problems. They formulate insights about your customers and can even devise ways to boost your profits. In a nutshell, data analysts provide insights that can transform how your business grows successfully.
Data scientists are masters of artificial intelligence and deep learning frameworks. Using these advanced tools, they can formulate advanced insights from layers and streams of data. They can also build effective, self-sustaining forecasting mechanisms.
Whereas a data analyst may spend more time on routine analysis and regular reporting, a data scientist may design how data is stored, manipulated, and analyzed. In other words, a data analyst interprets existing data, whereas a data scientist invents better ways of capturing and analyzing the data.
These two roles, working together with data engineers, make it possible to implement big data initiatives and manage your data pipeline.
What Is a Data Pipeline?
A data pipeline is a complex software system made up of multiple components, including automated ETL tools, scripts, and programs. The system receives data from one or more inputs, processes it, and then sends it to a corresponding destination.
The data pipeline’s purpose is to ensure an unimpeded, continual flow of data to users (usually your data analysts and data scientists).
Data pipelines can be built in many ways, with differing amounts of custom code. Depending on your needs, you may be able to set up your pipeline using commercial workflow automation tools like Apache Airflow or Azkaban. However, you may require custom development if you have more complex needs.
A word of warning: Implementing a big data pipeline takes effort. If your business relies on ongoing data collection and analysis, consider hiring an experienced data engineer or finding a trustworthy vendor that knows big data best practices and offers reliable support services.
Data Warehouses vs. Data Lakes vs. Data Lakehouses
Data warehouses, data lakes, and data lakehouses are three approaches to storing and using big data.
Data warehouses store data in a central repository for use in reporting and data analysis. New data written to a data warehouse must strictly adhere to predefined schemas and ETL rules. This is called a Schema-on-Write approach. Warehouses store data in read-only mode, giving users fast access to structured historical data on multiple levels.
Data lakes store data in highly scalable cloud databases in a completely unstructured, raw form—no schemas or ETL rules. Instead, the schema is imposed when the data is read. This is called a Schema-on-Read approach.
Data lakes give you greater flexibility when accessing the data because you don’t need to know any database schemas. And because the data gets stored in its native format, you don’t need to transform or convert it. Another advantage? Because your data is stored in the cloud, you can balance your spending and don’t have to pay for CPU time you don’t need.
As you probably guessed, data lakehouses combine the two previous data storage methods. A data lakehouse can store both structured and unstructured data, letting you work with unstructured data and only need a single data repository (rather than a warehouse and lake).
Data lakehouses allow you to apply a data warehouse’s structure and schema to the type of unstructured data stored in a data lake. In other words, your data users can access and start using the information faster.
You can also use intelligent metadata layers with a data lakehouse. These layers sit between the unstructured data and the data user in order to categorize and classify the data. Metadata layers effectively structure the data by identifying and extracting features from it, allowing it to be cataloged and indexed as if it was structured data.
Possibly the most significant advantage to data lakehouses is that they can be coupled with AI and machine learning to deliver smart analytics. Data lakehouses are also less costly to scale. They are “open,” meaning you can query the data with any tool and from anywhere instead of being locked into applications that can only handle structured data.
Big Data Requires Data Engineering
Big data is increasingly available to businesses of all sizes, allowing even small companies better intelligence for making critical decisions, providing new services, and responding to market demands.
Data engineering is vital to any big data strategy. As data processing systems become more complex, more businesses will develop custom solutions for streamlining their ETL operations and solving their data engineering challenges.