Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become.Hadoop is open or free source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big.

Our team of instructors are committed to creating an excellent learning experience for users that can be both affordable and easily accessible. We are focused to make Big Data concepts much more easier to the audience and produce expert software, test professionals.

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• Web Developers
• Frontend Developers
• Aspiring Developers
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• Tech Enthusiasts and Career Changers

• Programming languages
• Machine learning, AI, and Natural Language Processing (NLP)
• Quantitative analysis
• Data mining
• Data Structure and algorithms

A data engineer needs to have a thorough understanding of the underlying technologies that make up cloud computing. They would need to know their way around IaaS,PaaS,SaaS implementation.

Data engineers need to process and visualise datasets. They also need to be skilled in exploratory data analysis or EDA to ensure ETL/ELT work and skilled in working with tools like SSRS, Excel, PowerBI, Google Looker, PowerBI etc. 

A big data engineer should be familiar with Python’s libraries SciPy, NumPy, sci-kit learn, pandas, etc. They should also be familiar with the terminology and algorithms. 

A data engineer should know how to work with key-value pairs and object formats like Avro, JSON, or Parquet in the open-source Apache-based or MongoDB and Cassandra. 

Data engineers must operate with real-time streams, data warehouse queries, JSON, CSV and raw data. Hyper automation focuses on improving the quality of work, increasing decision-making agility, and accelerating business processes. They require skills to run value-added tasks.