WHAT ABOUT BIG DATA ENGINEERING?

According to DataReportal, currently over 5.32 billion people worldwide use mobile phones, accounting for 67% of the planet’s population and 4 out of 5 mobile phones are smartphones. There are many definitions of what Big Data Engineering is, and all of them relate to data and manipulation of it. This means that huge volumes of digital data are generated globally every day for both private use and various sectors of the economy.

Big Data Engineering is described as the practice of manipulating large datasets, involving the construction of data processing infrastructure, data storage, access, and formatting. 

What challenges does Big Data Engineering address?

Big Data Engineers can work in diverse fields such as finance, tourism, advertising, security, and e-commerce. In simpler terms, they work on projects or products that require handling large volumes of data, speed, or diverse data structures and formats.

Big Data Engineering addresses the following technical tasks:

• Building effective data processing pipelines (Data Pipelines).

Using different tools for various types of data; common frameworks include Apache Spark, Flink, Storm, Kafka, and cloud services like AWS, Google Cloud, and Azure.

• Data storage 

Using both relational (PostgreSQL, MySQL, MsSQL, Oracle DB) and non-relational databases (Cassandra, MongoDB, Neo4j), as well as other repositories like HDFS or cloud services.

• Data processing 

Involves transforming data into a structured format suitable for databases or other storage, including format conversion (if necessary), data cleaning, and anomaly detection. Common data formats in Big Data include Parquet, Avro, Protobuf, and CSV.

•  Infrastructure 

Big Data Engineers must deploy created solutions (Docker, Kubernetes), participate in CI/CD setup (Jenkins, TeamCity), determine the required number of resources for program execution, build metrics collection and logging mechanisms (Prometheus, Grafana).

Due to the vast amount of data, high-speed data streaming, non-standard size, and other features in Big Data, working with such data requires specialized skills.

Who is a Big Data Engineer?

Considering the points mentioned above, the question arises: who is a Big Data Engineer?

A Data Engineer is an individual who works as a data engineer, primarily focusing on collecting, processing, and storing data. The Data Engineer creates the foundation for the work by providing prepared data in a format optimal for the task. They use tools such as Python, Scala, SQL, NoSQL, Spark, and cloud technologies. Big Data Engineers need analytical thinking to process and reproduce large volumes of information.

According to the DOU, Data Engineers are among the highest-paid and most in-demand professionals in the IT services market. For effective development and meeting client needs, any growing company should have a Big Data processing expert on its team.

In conclusion, Big Data Engineering is a new technology gaining momentum in the IT field today. As the Big Data sector rapidly evolves, it opens up new opportunities for developers to explore this profession or develop new IT solutions.