Know all about the best Big Data Engineer Career Path

Big Data Engineer plays an active role in managing the data, which is churned out by the digital activities. They effectively build, develop, test as well as maintain data storage architecture such as databases and large-scale data processing systems.

A data engineer constructs continuous pipelines, which run to and from huge pools of filtered data by creating and maintaining data workflows. Let’s dive into the latest big data engineer skills that boost the career to land up in a top position in a reputed organization.

Get proficient in the right Programming Languages

As there is no specific university curriculum for data engineering, so the data engineer is someone who is a software engineer first. So, one should have good knowledge of the programming languages which are highly used by the data engineers. SQL is the main programming language that data engineers use extensively to create as well as to manage the relational databases. After that, they use Python or R programming languages, which are very helpful for modeling and statistical analysis.

In-Depth Knowledge on Databases

One of the best big data engineer career path is to have strong knowledge of the database languages and tools. The data engineers work with databases that consist of unstructured and structured data. One should be well-versed to collect, store and query the information using databases in real-time. SQL and NoSQL databases are vastly used in the industry today.

Data engineers use the SQL programming language to change and transport the information from a data source (such as a relational database) to a data warehouse with the help of ETL pipelines. The relational databases are basically tables that have rows and columns of structured data. They also tune databases for quicker analysis and create table schemas.

The unstructured data gets stored in a NoSQL database in the form of documents. The NoSQL database querying needs a proprietary language, which varies from SQL.

Learn Automation and Scripting

Most of the works that a big data engineer does are related to transforming and analyzing the data, which can be automated especially when the work consumes a longer time and when it is repetitive. For the automation of the tasks, one should have knowledge of the scripting language syntax, operations, and product configurations that include escalations, workflow processes, and actions. The scripting language is mainly used for the automation to gather the data from a data set or for certain works in a program.

Understand Data Processing Techniques

Data processing is the method where the raw data is converted into an analyzable way. The commonly used engine for parallel data processing is Apache Spark, which is also very useful for large datasets. The Apache Spark offers an easy-to-use API by using abstractions like Data Frames for the parallel processing tasks on clusters of machines.

The Apache Spark data processing framework uses batch processing that consists of collecting data points that are grouped in a specified time interval. The batch processing is considered when there is no requirement for real-time data.

For keeping business intelligence up to date, stream processing is used. As it deals with a continuous stream of data collection that one wants to process in the real-time, Apache Spark has an extension called Spark Streaming to do stream processing.

Gain in-depth insights on the concepts of Cloud Computing

One of the essential big data engineer skills is to have a good knowledge on cloud platforms is they centralize the processing power and gives firms the capability to store unlimited amounts of data virtually, without the associated costs for on-premise storage solutions. The other services that cloud platforms offer that are helpful for data engineers is computations, secured cloud storage, cluster management, and Massively Parallel Processing (MPP) databases, which run across various machines and use parallel processing to work on.

The most popular cloud platforms for organizations are Amazon Web Services, Microsoft Azure, and Google Cloud Platform. It is important for today’s data engineers to know how these cloud platforms work. Few job descriptions demand for the familiarity of a specific cloud platform.

Conclusion

Big Data Engineer is among the rapidly growing career path. Data engineers get to work with ever-evolving technologies, various creative challenges, and also enjoy greater job satisfaction. Know the right skill sets, which you need to enhance your career as a data engineer.