It is essential that young graduates and anyone interested to make it big in the field surrounding data-related careers, knows the right step as a measure to begin with this challenging and interesting process. In this article, we explore a specific job role/career called data engineering. In a previous article, we discussed the differences between a data analyst and a data engineer in brief. Here, we take a deeper pragmatic approach of what the job role entails without delving much into intricate technicalities.
What exactly does a data engineer do?
Data engineers create and work with the infrastructural aspects of data generation and their architecture. They handle tasks such as data collection, data storage, data management among many others. The primary focus of their role is database management and big data technologies. Despite having all these qualities to skillfully juggle with, they need to ensure that data and the database architecture provide accurate solutions and cater to business requirements of the clients/customers.
The Requisite Skill Sets
Need for Structured Query Language (SQL)
Structured Query Language(SQL) is the gold standard when it comes to managing databases. SQL is, by far, the standard and most used programming language used for the purpose. Originally developed by IBM in early 1970s, SQL uses concepts of relational algebra to handle data-related tasks. Hence,the term “relational database” began to gain popularity which consequently led to database management systems (DBMS) and relational database management systems (RDBMS).
In today’s world, the functions and concepts of SQL have been modified and extended on various similar platforms and languages, but the core syntax followed in SQL such as clauses, statements etc. are still relevant and applicable to other database languages. Therefore, the beginner needs to have a solid grasp of SQL or other database languages along the same lines (Cassandra, Microsoft Sybase, MySQL, Oracle PL/SQL to name a few) before implementing and managing databases on a business level.
Data Warehousing and ETL: Tools to obtain sensible data
Data warehousing is a process of deriving business data from the vast amount present in a data warehouse. It involves three key functions namely data cleaning, data integration and data consolidation. The data warehouse is the hub of all business-generated data obtained from multiple sources.
In order to perform operations/calculations on various data sources, an ETL tool is used. The word ETL is an acronym for Extract,Transform and Load. In simple terms, data is made available for a specified time duration, pulled from sources and transformed by applying functions/rules on the data and then loaded into a data warehouse.
These two concepts are explained in short just for understanding. A beginner should have wide knowledge in ETL concepts and understand the nuances in the terms used in the context. A list of material resources related to data warehousing and its application in relevant fields such as data analytics, from Oracle are available here.
Big Data technologies: tackling massive data in short span
Once the beginner has a strong command over the topics mentioned above, he/she can explore with tools to specialise furthermore into big data technologies such as data analytics and business intelligence. There are a vast number of tools available to learn for big data implementation. However, it is expected to learn the most popular ones business-wise. A few of the popular big data tools are mentioned below:
- Apache Hadoop
- Apache Spark
- Apache Hive
The big data ecosystem is very vast, and it would be wrong to say that only one tool would fulfill across important areas such as data mining, data visualisation, cloud computing, data aggregation and many more. It is suggested that the beginner has a broader outlook towards learning various tools.
Programming is another field of expertise required for data engineers. Although it is not expected to ace programming in one go, it should nevertheless be ignored either. The learner can be proficient in languages such as C/C++, Java, Python among many others. This will help in the long run when job functions become flexible.
In addition to learning the above mentioned skill sets, beginners can expand their knowledge base by getting hands-on training from technology experts in the data industry. They can enroll for certification programs offered by tech companies. The two popular ones in the field are mentioned below.
- Google Cloud Certified Data Engineer Program : This professional course by Google will provide training right from creating and maintaining databases to even using machine learning for data processing.
- IBM Professional Certification Program : IBM’s take on data engineering is exhaustive and lucrative in this professional program aimed at gaining data engineering skills and expertise on an advanced level.
In addition to these programs, there are a plenty of options for training resources such as Coursera, edX, Udemy and many more on the online platform. It is upto the learners’ interest and dedication towards acing data engineering to take up these courses. It is highly suggested he/she inculcate a learning mindset before starting with the journey of data engineering as dealing with huge amounts of data is no easy task.
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I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.