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Big Data Projects

Big Data Projects for Engineering Students

As you know, big data technology is in a constant growth phase as well as evolution in these days. Based on estimation, the entire revenue came from big data can reach $203 billion by the year 2020 and also it can be predicted that there will be around more than 4, 50,000 big data-related jobs all over the world. Nowadays, students, fresher’s, professionals and also entrepreneurs need to be updated with the powerful data mining algorithms and technologies for best growth in this decade. Therefore, in this blog, I list some powerful big data technologies and trends to must consider in 2019-2020. It will help to be more successful with time.

Big Data Project Trends

Apache Spark

Without any doubt, Apache Spark is widely used in Apache projects. In fact, it is the best choice for incredibly fast big data processing. Also, it attains built-in capabilities for real-time data streaming, machine learning, graph processing, and SQL, etc. The main reason for its popularity is which it is optimized to run in-memory and allow interactive streaming analytics. It is possible to analyze the huge amount of historical data with live data to make the decisions in real-time. For one example such as predictive analytics etc.

TensorFlow

Particularly, for machine learning, it is the popular open-source library which allows for more advanced analytics at scale. This technology is flexible to support experimentation with machine learning models and system-level optimizations with huge-scale distributed training and interference. Tensor Flow is the most popular among people. The reason is that there was no single library that deftly catches the depth and breadth of machine learning and possesses such potentials before Tensor flow.

Apache Carbon Data

The fast analytics on big data platforms such as Spark and Hadoop, Apache Carbon Data is an indexed columnar data format. In fact, it solves the problem of querying analysis for various use cases. Furthermore, you can access through the single copy of data and use only the computing power needed with carbon since the data format is so unified. Therefore, it makes our queries run faster.

Kubernetes and Docker

Both the Kubernetes and Docker are automated container management technologies. As well as, both do fast deployments of applications. In the final analysis, the fastest growing investment in powerful big data technologies is in banking, healthcare, insurance, securities and telecommunication sectors, etc.

Final Thoughts

ElysiumPro provides final year projects based on big data analytics projects. Our project titles are unique and innovative. These projects are very useful for final year engineering students those who want to make their career in the big data field.

What are big data tools?

Top big data tools that are used to store and analyze data such as

  • Apache Hadoop
  • Sqoop
  • PolyBase
  • Presto
  • Hive
  • Microsoft HDInsight
How do you analyze big data?

Analyzing big data in few steps such as

  • Begin with clear business objective
  • Determine the data quality
  • Be objective as possible
  • Visualize the data
  • Use technology to sift and organize data
What are the benefits of using big data?

Using big data, there are some benefits such as

  • Understanding the potential of data-driven marketing
  • Generating customer offers depend on their buying habits
  • Improving customer engagement and also increasing customer loyalty

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