Big Data Engineer

Big Data has a major impact on businesses worldwide, with applications in a wide range of industries such as healthcare, insurance, transport and logistics, and customer service. A role in this domain places you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. This co-developed Simplilearn and IBM Big Data Engineering Master's Program is designed to give you in-depth knowledge of the flexible and versatile frameworks on the Hadoop ecosystem and data engineering tools like Data Model Creation, Database Interfaces, Advanced Architecture, Spark, Scala, RDD, SparkSQL, Spark Streaming, Spark ML, GraphX, Sqoop, Flume, Pig, Hive, Impala, and Kafka Architecture. This integrated program will also teach you to model data, perform ingestion, replicate data, and shard data using a NoSQL database management system MongoDB. The course curriculum will give you hands-on experience connecting Kafka to Spark and working with Kafka Connect.

Big Data engineers create and maintain analytics infrastructure and are responsible for the development, deployment, maintenance, and monitoring of architecture components, such as databases and large-scale processing systems. The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025, so this is the perfect time to pursue a career in this field. The valuable skills you’ll acquire with this online Big Data Engineer Certification Program will help you secure employment with companies as diverse as IBM, Coca-Cola, Ford Motors, Amazon, HCL, and Uber. Big Data engineers are employable across a variety of industries such as transportation, healthcare, telecommunications, finance, manufacturing, and many more. According to Glassdoor, the average annual salary for a data engineer is $137,776, with more than 130K jobs in this field worldwide.

The learning path ensures that you master the various components of the Hadoop ecosystem, such as MapReduce, Pig, Hive, Impala, HBase, and Sqoop, and learn real-time processing in Spark and Spark SQL. By the end of this Big  Data Engineer Master’s Program, you will: 

• Gain insights on how to improve business productivity by processing Big Data on platforms that can handle its volume, velocity, variety, and veracity
• Master the various components of the Hadoop ecosystem, such as Hadoop, Yarn, MapReduce, Pig, Hive, Impala, HBase, ZooKeeper, Oozie, Sqoop and Flume
• Become an expert in MongoDB by gaining an in-depth knowledge of NoSQL and mastering the skills of data modeling, ingestion, query, sharding, and data replication
• Learn how Kafka is used in the real world, including its architecture and components, get hands-on experience connecting Kafka to Spark, and work with Kafka Connect
• Get a solid understanding of the fundamentals of the Scala language, it's tooling and the development process
• Identify AWS concepts,terminologies,benefits and deployment options to meet the business requirements
• Understand how to use Amazon EMR  for processing the data using hadoop ecosystem tools
• Understand  how to use Amazon Kinesis for big data processing in real-time
• Analyze and transform big data using Kinesis Streams
• Visualize data and perform queries using Amazon QuickSight

This Big Data Engineer Master's program includes more than 12 real-life, industry-based projects on different domains to help you master concepts of Data Engineering, such as Clusters, Scalability, and Configuration. A few of the projects that you will be working on are mentioned below:

Project 1: See how large MNCs like Microsoft, Nestle, and PepsiCo set up their Big data clusters by gaining hands-on experience.
Project Title: Scalability-Deploying Multiple Clusters
Description: Your company wants to set up a new cluster and has procured new machines. However, setting up clusters on new machines will take time. Meanwhile, your company wants you to set up a new cluster on the same set of machines and start testing the new cluster’s working and applications.

Project 2: Understand how companies like Facebook, Amazon, and Flipkart leverage Big Data Clusters.
Project Title: Working with Clusters
Description: Demonstrate your understanding of the following tasks:

• Enabling and disabling HA for namenode and resource manager in CDH
• Removing Hue service from your cluster, which has other services such as Hive, HBase, HDFS, and YARN setup
• Adding a user and granting read access to your Cloudera cluster
• Changing replication and block size of your cluster
• Adding Hue as a service, logging in as user HUE, and downloading examples for Hive, Pig, job designer, and others

Project 3: See how banks like Citigroup, Bank of America, ICICI, and HDFC make use of Big Data to stay ahead of the competition. 
Domain: Banking
Description: A Portuguese banking institution ran a marketing campaign to convince potential customers to invest in a bank term deposit. Their marketing campaigns were conducted through phone calls, and sometimes the same customer was contacted more than once. Your job is to analyze the data collected from the marketing campaign.

Project 4: Learn how Telecom giants like AT&T, Vodafone, and Airtel make use of Big Data by working on a real-life project based on telecommunication.
Domain: Telecommunication
Description: A mobile phone service provider has launched a new Open Network campaign. The company has invited users to raise complaints about the towers in their locality if they face issues with their mobile network. The company has collected the dataset of users who raised a complaint. The fourth and the fifth field of the dataset have a latitude and longitude of users, which is important information for the company. You must find this latitude and longitude information on the basis of the available dataset and create three clusters of users with a k-means algorithm.

Project 5: Understand how entertainment companies like Netflix, Amazon Prime leverage Big Data.
Domain: Movie Industry 
Description: US-based university has collected datasets which represent reviews of movies from multiple reviewers as a part of the Research Project. To gain in-depth insights from research data collected you have to perform a series of tasks in Spark on the dataset provided.

Project 6: Learn how E-Learning companies like Simplilearn, Lynda, and Pluralsight make use of NoSQL and Big Data technology.
Domain: E-Learning Industry
Description: Design a web application for a leading E-learning organization using MongoDB to support read and write scalability. You can use web technologies such as HTML, JavaScript (JSP), Servlet, and Java. Using this web application, a user should able to add, retrieve, edit, and delete the course information using MongoDB as the backend database. 

The Big Data Engineer course is ideal for anyone who wishes to pursue a career in data engineering. There are no prerequisites to take this course, but prior knowledge of the listed skills and technologies are beneficial, including:

• Algorithms and data structures
• SQL
• Programming knowledge of Python and Java
• Cloud platforms and distributed systems
• Data pipelines