How to become a Big Data Engineer

The data engineer is an engineer who works alongside data analysts, data architects, and data scientists. Data engineers maintain and build data pipelines, they work on warehousing big data in such a manner that makes it more accessible for the people whenever they want to deduce the data. They build a huge reservoir for the data and play an important part in managing and maintaining these reservoirs alongside churning the data out for the various digital activities. Their work often includes developing, testing, constructing, and also maintaining the data storing architecture (such as the database or the large-scale data processing system).

• Develop and apply a combined infrastructure of data management and data processing
• Collect, explain, manage, analyse, and visualize large data sets to change information into insights with the help of multiple platforms
• Make decisions on hardware and software design requirements
• Build prototypes and concept proofs for the chosen solutions
• Plan, develop, build, and manage data pipelines
• Transform unstructured data collected from different data sources to achieve the functional & non-functional business requirements
• To optimize performance, automate processes, optimize data delivery and re-design whole architecture
• Manage and transform large-scale data with the help of Big Data Frameworks & NoSQL databases
• For data analysis, build whole infrastructure to consume, transform, and store data
• Collect and operate raw data at scale
• Plan and create required data applications with the help of certain tools and frameworks
• Read, excerpt, transform, stage, and load required data to chosen tools and frameworks
• Collaborate with engineering department to assimilate the work into production process
• Develop policies for data retention.

Big data engineers have extensive coding experience in general purpose and high-level programming languages such as Python, R, SQL, and Scala, as well as extensive knowledge of Java. When you compare different job descriptions for big data engineers, you'll notice that the majority of them are based on knowledge of specific tools and technologies. To create, design, and manage processing systems, a big data engineer must learn multiple frameworks and NoSQL databases. Frameworks for big data processing The type of data analysis performed by frameworks for computing over data in the system can be used to classify them. So we have batch-only Hadoop, stream-only Storm and Samza, and a hybrid Spark/Flink.

• The Hadoop ecosystem Hadoop is the most popular big data framework for batch workloads because it is not time-sensitive, making it less expensive to implement than others. Its ecosystem includes tools such as HDFS, a Java-based distributed file system; MapReduce, a framework for writing applications that process HDFS data; YARN, a workload management and monitoring operating system; Pig and Hive querying tools; and the HBase NoSQL database.
• Frameworks for real-time processing Kafka is a stream processor that big data engineers use to run concurrent processing and move large amounts of data quickly. However, when used in conjunction with Hadoop, Kafka can also perform batch processing on the stored data. However, it is most commonly used with real-time processing frameworks such as Spark, Storm, and Flink. Spark is used by big data engineers for mixed workloads that require faster batch processing and micro-batch processing for streams. Furthermore, Spark's ever-expanding algorithm library makes it a go-to big data ML tool. Technologies based on NoSQL To handle, transform, and manage big data, big data engineers use NoSQL databases in conjunction with big data frameworks. NoSQL databases, with their quick iteration and Agile structure, allow for the storage of large amounts of unstructured data.
• HBase. HBase, a column-oriented NoSQL database built on top of HDFS, is an excellent choice for scalable and distributed big data stores. • Cassandra. Cassandra, another highly scalable database, has the major advantage of requiring little administration.
• MongoDB. MongoDB is a schema-free NoSQL database that allows schemas to evolve as the application grows. Machine Learning Toolkit for Big Data The following tools, in addition to SparkML, assist big data engineers in integrating Machine Learning into their big data infrastructure.
• H2O. This is a complete solution for collecting data, building models, and delivering predictions. It is compatible with the Hadoop and Spark frameworks and includes development environments such as Python, Java, Scala, and R
• Mahout. Scalable machine learning on big data frameworks is now possible. Mahout is linked to Hadoop, but it also runs independently, allowing stand-alone applications to migrate into Hadoop and vice versa – Hadoop projects can branch off into their own stand-alone applications.

1. Technology
2. Finance
3. Retail
4. Telecommunications
5. Energy
6. Government
7. Education