The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. Data engineers need to have a solid understanding of commonly used scripting languages and are expected to support the steady evolution of improved Data Quality, and increased quantity, by leveraging and improving data analytics systems. Data engineers are also responsible for creating the steps and processes used in modeling, mining, verification, and acquisition.
The demand for skilled data engineers is projected to rapidly grow. In the modern world, businesses and organizations require a robust Data Architecture for storing and accessing data. Data engineers are needed when an organization expands into using Data Science. Consequently, there has been a recent run on data engineers.
An organization may assume it can develop the data engineering skills and experience needed while working through a project. According to Kevin Safford, a senior director at Umbel, they’re usually wrong. He added:
“If you don’t have specific hard-earned, on-the-ground experience with building a data pipeline, a Data Management system, data analytics, and all of the intermediate code to make the data available and accessible and to assure that the data is correct, to assure that the analysis that you’re doing is correct — if you don’t have that specific expertise, then it may seem like those are the types of things you can figure out as you go. And I’ve seen a lot of people make those assumptions. They’re pretty much always wrong and they pretty much always make the same mistakes.”
Data Engineer vs. Data Scientist
The skills and responsibilities of data scientists and data engineers often overlap, though the two positions are increasingly becoming separated into distinct roles. Data scientists tend to focus on the translation of big data into Business Intelligence, while data engineers focus much more on building the Data Architecture and infrastructure for data generation. Data scientists need data engineers to create the environment and infrastructure they work in.
A data scientist is focused more on interacting with the infrastructure than building and maintaining it. Data scientists are given the responsibility of taking raw data, and turning it into useful, understandable, actionable information. Data scientists work with big data, and data engineers work with data infrastructures and foundations.
A data foundation supports all types of reporting and analytics. The goal of a data engineer is to provide trustworthy, integrated, and up-to-the-minute data to support reporting and analytics. A robust data foundation offers organizations tremendous benefits, making them more efficient in their behavior and decision-making. Useful benefits include:
- Improving organizational communication and collaboration
- One-stop shopping for data
- A single version of the records kept
- Support of a common understanding of information across the enterprise
By not implementing an efficient data foundation, a modern organization increases its own security risks, and supports inefficiencies within the organization. A poor data foundation can provide multiple answers to the same question and support less-than intelligent business decisions.
Big Data Engineering Skills
Data engineers need a good understanding of Database Management, which includes an in-depth knowledge of Structured Query Language (SQL). They build infrastructures, tools, frameworks, and services. Some believe data engineering has become more similar to software engineering and app development than Data Science. Other useful skills include:
- Experience with Apache Hadoop, Hive, MapReduce, and Hbase.
- Machine learning (ML) is primarily the focus of data scientists, but some understanding of it is also important for data engineering. ML is closely associated with big data. (ML has streamlined the processing of big data, and supports many techniques for handling big data, and making sense of it.)
- Coding knowledge is definitely a plus. Familiarity with C/C++, Java, Python, Perl, Golang, or other languages can be very useful. A good understanding of Linux, UNIX, and Solaris is also very helpful, as these systems come with significant root access to operating system functionality and hardware.
- ETL (Extract, Transform, and Load) experience is a necessity for this position. ETL is a data warehousing process used for pulling data out of source systems and then storing it in a data warehouse. A familiarity with ETL tools, such as Segment or Oracle Warehouse Builder, and data storage solutions, such as Panoply or Redshift, is quite valuable.
ETL (Extract, Transform, and Load)
In the world of computing, ETL is used in databases and warehouse construction. Extracting, Transforming, and Loading became popular during the 1970s. Data extraction describes data being extracted from homogeneous or heterogeneous data sources. Data transformation expresses data being translated into the proper structure, or format, for purposes of storage (and later, research and analysis). Data loading is the process of downloading the translated data into a data mart, a data store, or a data warehouse.
A well-designed ETL system can extract data from source systems, and enforce data consistency and quality standards. It can also deliver data in a ready-for-presentation format that allows developers to build an application, with end users deciding its value.
ETL systems traditionally integrate data from several applications and from different vendors and computer hardware. Separate systems, which contain the original data, are often operated and controlled by different people. A manager of the payroll accounting system, for example, may combine the data from sales and purchasing.
A data warehouse is used for storage, reporting, and data analysis. It is essential in the development of modern Business Intelligence. Data warehouses are used for the centralized storage of integrated data coming from one or more sources. They store both current and historical data, which is used for developing analytical reports.
Without data warehouses (or their updated architectural counterpart data lakes), the processing of big data — and every activity associated with Data Science — becomes ridiculously expensive or unscalable. Without an intelligently designed data warehouse, analysts could easily report different results, after researching the same question. They could also inadvertently attempt to research the production database (while lacking a data warehouse), and cause delays or outages.
Becoming a Data Engineer
Generally, a data engineer comes with an Information Technology or Computer Science degree combined with certifications and other training. Data engineering schools normally approach education with greater flexibility, due to the more individualized demands of each work environment.
The degree and specialized training are important, but are not enough by themselves. Additional certifications can be extremely valuable. Useful data engineering certifications include:
- CCP Data Engineer (Cloudera’s Certified Data Engineer credential) — this provides proof of experience with ETL tools and analytics.
- Google’s Certification — this establishes familiarity with basic Data Engineering skills.
- IBM Certified Data Engineer (for Big Data) — this communicates experience in working with Big Data applications.
Secondary certifications are also available. For example, the MCSE (Microsoft Certified Solutions Expert) covers a broad range of topics, and applies sub-certifications to specific topics, including MCSE: Data Management and Analytics; MCSA: Business Intelligence Reporting; and MCSA: Microsoft Cloud Platform. Additionally, data industry events can provide an excellent source of training and education (and provide an excellent opportunity to network). Online courses can also offer useful training for specific situations as well, there are many available.
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