Who is Big Data Engineer | How to become a Big Data Engineer?

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Who is Big Data Engineer?

Typically, the Big Data Engineer builds what the big data solutions aarchitect has designed to solve the the particular problem . They develop, maintain, test and evaluate big data solutions within the organization. Ofte times, they are also involved in the design of big data solutions, because of the experience they have with Hadoop based technologies such as MapReduce, Hive MongoDB or Cassandra. A big data engineer builds large-scale data processing systems, is an expert in data warehousing solutions and should be able to work with the latest (NoSQL) database technologies.

This job role is also very similar to normal Data Engineer.  Once we need to work with massive dataset then people sometimes add a new words called Big data before Data Engineer’s role.

As I already discussed this is challenging job role. It has much more overlapping with normal Data Engineers and Big Data Analyst. Sometime you may get similarity with Data Architect also. But Big Data Engineer mainly focus on scalable data storage, protection, accessing massive data efficiently wherever a Data Architect gives the blue print and select proper resources for a project when Big Data Engineers really follow the blue print to make it success.

To be a good Big Data Engineer you need to enough knowledge in below fields:

Hadoop-Based Technologies

  • MapReduce: MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.
  • Hive: Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
  • Pig: A high-level platform for creating programs that runs on Apache Hadoop
  • Impala: Cloudera Impala is Cloudera’s open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop.

SQL Based Technologies Like PostgreSQL and MySQL

  • Teradata: A relational database management system of the same name, which it markets as a data warehouse
  •  Amazon Redshift: Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools.
  •  Microsoft Data Warehouse: Perform in-database R analytics, consolidate disparate data sources, and innovate with built-in mobile BI using SQL Server for data warehousing.

NoSQL Technologies

  • Cassandra: The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
  •  MongoDB: MongoDB is a free and open-source cross-platform document-oriented database program.

Machine Learning Libraries

  • Apache Mahout: Free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification.
  • Apache Spark MLlib:MLlib is Apache Spark’s scalable machine learning library, with APIs in Java, Scala, Python, and R.


Data warehousing Concepts

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.


Also read  Different Job roles in Data Science