This article is about Data Science
7 Big Data Technologies for the Future
By NIIT Editorial
Published on 17/09/2021
Big Data is an umbrella title that encompasses data-centric operations under one roof. The marketplace for big data analytics is projected to be worth $103 billion by 2023. Such is its value add that as many as 97.2% of organizations are investing in big data and AI projects. Having been around for some time, the learner community is also well-acquainted with terminologies like Hadoop, NoSQL, Apache Spark, and TensorFlow. Therefore, as the interest to learn data science continues to see new highs, we bring you the top 5 big data technologies that will make an impact in 2021 and beyond.
What is Big Data?
Data sets that are so huge in volume that they cannot be processed with conventional data processing software are called big data. It has 3 main characteristics namely velocity - it comes at immense frequency, volume - it has exponential depth, and variety - it offers diverse insights.
Types of Big Data Technologies
Big data technologies refer to the software that is used for curating, processing, analyzing, and extrapolating information from raw data, that old school data management software can never handle on its own. Based on the nature of work, big data technologies can be divided into two categories:
Operational Big Data Technologies
Operational technologies in this sector refer to the everyday data that is generated when people interact with a particular platform or indulge in outcome-oriented activities. Common examples include surfing on social media platforms like Facebook, Instagram, etc. to using net banking, and interacting with OTPs.
Analytical Big Data Technologies
Such technologies refer to the software used for carrying out complicated, analytics-oriented assignments to extract insights from operational data. This is used by data-driven businesses for processes such as time series analysis, statistical computation, etc.
Top Big Data Technologies in 2021
Hadoop processes batch information with map-reduce architecture and is considered to be one of the best-suited technologies to handle big data. It is designed to process data in a distributed processing environment due to which it can analyze information from multiple machines at greater speed and low cost. Written in Java programming language, Hadoop is considered the backbone for big data operations.
It is used for data storage. MongoDB uses schema documents and is a NoSQL-based database. It is different from traditional RDBMS in that it is based on a cross-platform document-oriented design. It is preferred for storing operational data as it uses documents similar to JSON with schema.
TensorFlow allows developers to build and deploy ML applications. It is an open-source library particularly for machine learning and artificial intelligence. A solid reason for that is that TensorFlow has the capabilities to train neural networks. Its design is based on data flow and differentiable programming. Nowadays, you can easily find TensorFlow as part of leading courses on data analytics due to its applications in big data.
Maintained by the Cloud Native Computing Foundation, Kubernetes is an open-source tool used for automating full spectrum computer application tasks from deployment, to scaling and management.
It is a distributed streaming platform that has multifaceted capabilities of processing real-time data. Kafka shows semblance with enterprise messaging systems as it can play a three-sided role of content publisher, subscriber, and consumer at the same time. Written in Java, Kafka offers many advanced level properties such as registry, schema, KSql, and Ktables to name some.
This coding language is majorly used for statistical computing operations. Data analysts use it to create, run and simulate scenarios to make informed decisions. Multinational companies like Barclays, Bank of America, and American Express.
Courses on data analytics are flooded with tutorials on Tableau. That is because it is a fairly popular data visualization tool used by data-rich divisions to analyze insights. The worksheets and pictorial dashboards that you can create here are a top-up on excel and loved by working professionals. As a result, Tableau is in high-demand among professionals who want to learn data science.
Blockchain is a digital ledger that is designed to record transactions, and undertake digital asset management. They are almost immutable due to which they are recommended for archiving data and establishing trust. Valuable assets can be tracked easily on blockchains. Only permissible members get the rights to access blockchain records.
Interested in Big Data? Make Data Science Your Field
Data science is transforming business outcomes with applications of big data. In tandem with the latest hiring expectations, NIIT has launched machine learning courses in both full-time and part-time modes for learners. Relevant online courses available on the NIIT Digital platform are mentioned below:
- Advanced PGP in Data Science and Machine Learning (Full Time)
- Advanced PGP in Data Science and Machine Learning (Part Time)
- Data Science Foundation Program (Full Time)
- Data Science Foundation Program (Part Time)
Explore these new-age program offerings for a rewarding future!
Data Science Foundation Program (Full Time)
Become an industry ready StackRoute Certified Python Programmer in Data Science. The program is tailor-made for data enthusiasts. It enables learner to become job-ready to join data science practice team and gain experience to grow up as Data Analyst.
Visualise Data using Python and Excel
6 Weeks Full Time Immersive