The demand for skilled Data Engineers (or Big Data Engineers) is projected to rapidly grow. No wonder that’s the case: no matter what your company does, to succeed in today’s competitive environment, you need a robust infrastructure to both store and access your company’s data, and you need it from the very beginning.
What exactly does a Data Engineer do, though? And how does one become a Data Engineer? In this article, we’re going to talk about this interesting field, and how you can become a Big Data Engineer.
What does a (Big) Data Engineer do?
Data Engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance and testing of architectures, such as databases and large-scale processing systems. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.
Engineers are expected to have a solid command of common scripting languages and tools for this purpose, and are expected to use this skill set to constantly improve data quality and quantity by leveraging and improving data analytics systems.
The difference between Data Engineer and Data Scientist
While there is a certain amount overlap when it comes to skills and responsibilities, these two positions are being increasingly separated into distinct roles.
Data Scientists are much more focused on the interaction with the data infrastructure rather than the building and maintenance thereof. They are often tasked with conducting high-level market and business operation research to identify trends and relations - as part of this, they use a variety of sophisticated machines and methods to interact with and act upon data.
Data Scientists are often well-versed in machine learning and advanced statistical modelling, as they are expected to take the raw data, and turn it into actionable, understandable content with the help of advanced mathematical models and algorithms. This information is often used as an analysis source to tell the “bigger picture” to the decision makers.
So what makes a data scientist different from a data engineer? Generally speaking, the main difference is one of focus. Data Engineers are much more focused on building infrastructure and architecture for data generation; Data Scientists are focused rather on advanced mathematics and statistical analysis on that generated data.
Big Data Engineers Key Skills
Tools and components of data architecture
Since Data Engineers are much more concerned with analytics infrastructure, most of their required skills are, predictably, architecture-centric.
In-depth knowledge of SQL and other database solutions
Data Engineers need to understand database management, and as such, in-depth knowledge of SQL is hugely valuable. Likewise, other database solutions, such as Cassandra or Bigtable, are great to know if you plan on doing freelance or for hire engineering, as not every database is going to be built in the recognizable standard.
Data warehouse architecture and ETL tools
Data warehousing and ETL experience is essential to this position. Data warehousing solutions like Redshift or Panoply, as well as familiarity with ETL Tools, such as with StitchData or Segment is hugely valuable. Similarly, experience with data storage and retrieval is equally vital, as the amount of data being dealt with is simply astronomical.
Hadoop based Analytics (Hbase, Hive, Mapreduce etc)
Strong understanding of apache Hadoop-based analytics are very common requirements in this space, with knowledge of Hbase, Hive, and Mapreduce often considered a requirement.
Speaking of solutions, knowledge of coding is a definite plus here (and also possibly a requirement for many positions). Familiarity, if not outright expertness, is very valuable in Python, C/C++, Java, Perl, Golang, or other such languages.
While mainly the focus of data scientist, some level of understanding of how to act upon this data is also invaluable for Data Engineers. For this reason, some knowledge of statistical analysis and the basics data modeling are hugely valuable.
While machine learning is technically something relegated to the Data Scientist, knowledge in this area is helpful to construct solutions usable by your cohorts. This knowledge has the added benefit of making you extremely marketable in this space, as being able to “put on both hats” in this case makes you a formidable tool.
Various Operating Systems
Finally, intimate knowledge of UNIX, Linux and Solaris is very helpful, as many maths tools are going to be based in these systems due to their unique demands for root access to hardware and operating system functionality above and beyond that of Microsoft’s Windows or Mac OS.
How can I become a (Big) Data Engineer?
Data Engineering typically requires a more hybrid approach to education than other, more traditional careers. While teachers often have a degree specifically in teaching, Data Engineers often have a Computer Sciences or Information Technology degree that was then further parlayed with vendor specific Certification programs and training materials.
As such, your degree, while important, is only part of the story - getting the proper certifications can be hugely valuable. There are a few Data Engineering-specific certifications:
- Google’s Certified Professional - Data Engineer - this certification establishes that the student is familiar with Data Engineering principles and can function as either an associate or a professional in the field.
- IBM Certified Data Engineer - Big Data - this certification focuses more on big data specific applications of Data Engineering skill sets rather than general skills, but is considered a gold standard by many.
- CCP Data Engineer from Cloudera - specific to Cloudera’s solutions, this certification shows the student has experience in ETL tools and analytics.
- Secondary certifications, such as the MCSE (Microsoft Certified Solutions Expert), cover a wide range of topics, but have specific sub-certifications such as MCSE: Data Management and Analytics
- Data Industry events are also an excellent source of information and an excellent opportunity for networking with your peers that is truly necessary to excel as a data engineer.
There are, of course, online courses that purport to offer significant training in this field. Udemy offers numerous courses in Data Engineering and data science, and other sites, such as EdX and Memrise offer similar coursework. Some sites, such as DataCamp, are heavily focused specifically on data science and engineering, while others, such as Galvanize, are more broad-based.
While these solutions can help you get your feet in the water, so to speak, they come with the caveat that they rarely dispense or confer certification, and at best, many only offer a certificate or diploma. As such, while they are great for general learning, they should not be considered a replacement for actual certification or accredited diploma issuance.
Hopefully this piece has illuminated the specific talents, skills, and requirements expected of a Big Data Engineer. While the field is rapidly growing, it is fraught with obstacles - therefore, attaining the best education possible while filling any gaps in skill sets with proper certification is key.
In addition, you can learn more about Redshift architecture? Check Redshift architecture.