Data Engineering is among the highly demanded jobs in the market these days. The data is considered as the fuel of the new age. The industries generate a huge amount of data from various sources and the work of a Big Data Engineer is to design, test, build, install, develop, and maintain the whole data management and processing systems.

A big data engineer requires a set of skills to effectively perform the tasks.

The data engineers are required to be well-versed in the data warehousing solutions, programming languages needed for analysis and statistical modeling, and building data pipelines.

Let’s dive into the latest skillset that boosts the career to land up in a top position in a reputed organization as a senior data engineer.

· Database systems (SQL and NoSQL)

A database is the core of data searching, storage, and organizing. There are two types of databases. They are:

• Structure Query Language (SQL) – It is a standard programming language that is helpful to build and manage relational database systems.

• NoSQL – NoSQL databases are non-tabular and can store huge volumes of structured, semi-structured, and unstructured data.

A senior data engineer should be familiar with their structure and language so that they can manipulate a software application called Database Management Systems (DBMS), which gives an interface to databases to store the data and for the retrieval.

· Programming language

The proficiency in any of the programming languages will be a great add-on for individuals who want to make a mark in their careers. The programming languages are helpful for to code the extract, transfer, and load (ETL) and data pipeline framework. The various programming languages are:

• Python – It is a popular programming language used for data analysis, modeling, and pipelines. And it is also very easy to learn due to its simple syntax.
• R- It is mainly used by data scientists and analysts to perform tasks that are related to data analytics. This is developed by the statisticians and has a steep learning curve.
• Java- This is majorly used in machine learning sequences, data architecture frameworks, and building data sorting algorithms.
• Scala- This is widely used in data processing libraries like Kafka – an open-source processing software platform. Scala is more concise and totally relies on a static-type system.

It is highly recommended to have good knowledge of at least one programming language as the main logic among them remains the same only the essence changes. If an individual wants to master their skills in programming languages, then taking up a best big data engineer certification will help them to enhance their skillset.

· Apache Spark

Apache Spark is an open-source distributed real-time processing framework that gives real-time stream processing, interactive processing, batch processing, a programming interface for programming clusters, and in-memory processing at very quick speeds, ease of use, and a standard interface. Apache Spark is also quite popular in roles that include big data analytics. It also stood on top of the list for the data processing layer, which is followed by AWS Lambda, Elasticsearch, MapReduce, Oozie, Pig, and AWS EMR. One must have knowledge on how to operate on the front end (SparkR), as well as on the back end followed by the Spark cluster and the Spark libraries.

· Cloud computing

Cloud computing technology is very important for processing and storing data as the offers better scalability and distributed access than an on-premises server. The popular type of cloud service for big data is AWS, Azure Data Lake, Google Cloud Platform, and Apprenda. Engineers must be familiar with the different cloud storage types, tools, security levels, and the service providers that are available through the cloud.

· Machine learning

The Machine learning (ML) algorithms are very helpful for big data engineers as they offer to process as well as to sort out huge amounts of data in a short span of time. The big data is a part of developing the machine learning algorithms as they “learn” it by data sets processing. Data engineers are required to have a basic knowledge about the machine learning algorithm building process as it gives them better insights to build accurate data pipelines.

· Apache Hadoop

Apache Hadoop is an open-source framework that gives the distributed processing of huge data sets across thousands of servers and devices at once with the help of simple programming models. It is designed to vary the degrees of scale-up based on the data and mode it runs in. Hadoop supports programming languages that include Python, R, Java, and Scala. Hadoop is not just a single platform; it is several tools that support data integration. The data engineers can take up a Hadoop certification so that they can learn more about Hadoop, modes that are used, tools that are available. Hadoop highly supports data integration and is very useful for big data analytics.