Unemployment rates are at a record low and the economy is at boom. Companies worldwide are facing a shortage though; they need skilled professionals. To narrow it down, the skill crunch in tech profiles is a looming reality. Big data analytics is one area with a milieu of vacant positions and skill crunch.
While it would be swell to get data scientist and data engineering skills in the same folder, it’s a prudent choice to pursue one of the specializations as you take the first steps. In its premise data scientist and data engineer may look synonymous, but they are different functions in big data.
Who are Data Engineers?
We are starting with the assumption that you know what big data is even if you are at your layman knowledge level here. It is after all the next big thing. Jumping to the point, data scientists are all about interactions with data infrastructure. The skills that go in this blender are statistics, mathematics and a strong concept of machine learning. Data scientists have to take up a chunk of market and operations research, so, logic and reasoning counts as a crucial skill set.
There is a mention of data infrastructure. So, there should be a big chunk going in architecting it, maintaining it and generating data from it. This is where the premise of data engineers come in play. It goes without saying that you need a strong concept of popular scripting language and the tools used to create strong data analytics infrastructure. The thing with languages and tools is that they need a steady skill update. You start with a degree in Computer Science or Information Technology. As you proceed you need to score high on data engineering certification that validates your expertise so that you can always be in sync with the vendor approved tool and languages.
Master Database Solutions
Moving forward, it won’t be a tough guess for you that data engineering requires an in depth knowledge of database solutions while they are creating data infrastructure. Include SQL in the highest priority list. If you are trying your hands out in freelancing or as an engineer for hire then throw in the knowledge of other platforms like Bigtable and Cassandra in the mix. After all your clients won’t go for the same platform.
Great Knowledge of Data Warehouse and ETL
Next in line is data warehousing and creating extraction-transformation-loading (ETL) architecture. Choose your pick among the industry popular like Amazon Redshift, Teradata, Paraccel and Cloudera while you chose to learn about data warehousing solutions. Merge the knowledge with the understanding of ETL tools like Informatica-Power Center and Oracle Data Integrator. Keep in mind the storage and data retrieval aspect as you are going to deal with data that is astronomical in proportions.
Hadoop based analytics are a big part of the entire ecosystem. Make sure you have a thorough knowledge of associated tools like Hbase, HIVE, Sqoop and Pig to name some.
Code it like a Pro
Get your coding game on a jet speed as when you deal with multiple platforms and architecting an infrastructure of a humungous amount of data an in depth knowledge of C/C++, Java, Python, Golang, Pearl etc. is a big plus and at times a requirement.
Get the Complete Picture
It helps to build if you know where the road is heading. A sound understanding of machine learning and operating systems will help you to see the entire picture. Operating systems like UNIX, Linux and Solaris form the base for many mathematics tool.
We have mentioned it before that you start with a degree in computer science or information technology. The area of work is dynamic and requires a hybrid qualification. Procuring data engineer certification helps you collect multi-faceted expertise that you know by now is a mandatory check box.
While you browse and enroll in certification courses, keep a tab on events that have data industry as focus area. Many of them add to your credits and haul your growth as a data engineer.
Where are you placed when it comes to preparing for entering data engineering arena?