Top Technologies to learn to excel in Big Data Engineering

big data engineering

The data landscape has been segmented by enhancing dynamics over the past few years. Until the turn of the millennium, data warehouses and relational database management systems (RDBMS) were still considered the best way for data storage and preparation. But there is spread and dynamic of the Internet, in particular, broke through this unique position.

On the other side, the amount of data multiplied, interest was now increasingly focused on semi-structured and unstructured data. So, we had arrived in the big data engineering era. The next push was caused by the data flows generated by mobile devices and sensors such as Internet of Things. It was no longer just a matter of coping with the enormous increase in data input but recognizing events from many data points in real-time and react to them.

The data engineering gives professionals to develop the data pipelines to help train and deploy the ML models. They also develop more technically demanding continuous data pipelines that offer applications with Artificial Intelligence (AI) and Machine Learning (ML) algorithms.

Once focused on developing pipelines to support traditional data warehouses, the data engineering teams are in the process of developing more technically demanding continuous data pipelines that feed applications with AI and ML algorithms. These data pipelines are very affordable, quick, and are quite dependable regardless of the kind of workload and use case.

In this article, we will understand about the top technologies that every big data engineer must know and master for his daily work.

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. Hadoop highly supports data integration and is very useful for big data analytics.

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.

Individuals who want to excel in big data engineering 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. It works well for data sets that need a programmatic approach, like file formats that are vastly used in healthcare insurance processing.

Spark is also distributed, flexible, quick. It gives an in-memory computational engine, as well as facilities for real-time data streaming. This offers the big data engineer tocreate stream processing in the same way they write batch processing. Spark also supports mid-query fault-tolerance and also actively recovers when there is a failure.

Airflow

Airflow is an open-source tool, which programmatically author, schedule, and monitor data workflows. With the help of Airflow, the users would be able to author workflows as Directed Acyclic Graphs (DAGs). DAG is the set of tasks required to complete a pipeline organized to reflect their relationships and interdependencies.

Airflow is natively integrated to work with data engineering systems that include Hive, Presto, and Spark. It works best with workloads, which follow the batch processing model.

Few other latest technologies

A big data engineer must have a good knowledge of the latest technologies that are essential to perform tasks. Following are a few important tools that the they use:

  • Apache Hive
  • Apache Beam
  • Apache Cassandra
  • Apache Oozie
  • Apache NiFi
  • Apache Flink
  • Apache HBase
  • Apache Impala
  • Apache Kafka
  • Apache Crunch
  • Apache Apex
  • Apache Storm
  • Heron
  • Hue

One can also start with the three giants in the market: Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Learning these latest technologies will help an individual to offer the best inputs, which can be used for developing scalable data pipelines.

By aamritri

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