Big Data Frameworks You Should Know About

Best Practices, Guest Post

By Guest Author, Sai Digbijay Patnaik:

What is Big Data?

How much do we know about Big Data? Is Big Data different from regular data that we use in our day to day life? According to many studies, any character or symbol available in a raw form, that’s stored in any computational device, or can communicate through signs is known as data. Yet, until the time it takes for data to process, it is of no use anymore.

Thus, any data, structured, semi-structured, or unstructured, that’s created during the business processes, is known as Big Data. It stems from any data that we see on the internet, social media, emails, transactions, videos, or images in a digital format that is processed and stored. Big Data frameworks allow an organization’s enterprise data or data that we get from every other digital source to be processed, assessed, managed, controlled, and improved over the period.

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Why is Big Data Important?

Currently, Big Data is high on demand in the development and enhancement of enterprise software. The rapid and continuous growth of the volume of information from every available source has motivated Big Data technology to be highly popular, making it a socially- technological wonder.

In this digital age, there is a massive demand for data; hence, a large volume of data is required to be reviewed, structured, assessed, and processed to meet the growing data demand. As the growth of Big Data expands, it’s essential that if you want to make a successful career in Big Data, it will require you to learn the Big Data frameworks as well as taking the Big Data Hadoop certification training course.

Below, are a few relevant tactics to consider:

Big Data Framework

Big Data management requires tools that can not only store them securely but also organize, assess, analyze, and process them efficiently. Today, there are dozens of frameworks that you need to be familiar with if you are planning to make a career in Big Data analysis, depending on what role you are chasing.

Apache Hadoop

Apache Hadoop is an open-source, scalable, software programming framework that stores a collection of different technologies and codes in Java. Many popular Big data vendors have implemented Apache Hadoop such as Amazon Web Services, MapR, Hortonworks, and Cloudera.

Hadoop uses a MapReduce strategy that enables it to process Big Data at a very high speed. MapReduce is a programming model that is useful in batch processing and efficiently handles a large volume of data across parallel clustered systems. It’s scalable and can expand from a single server to thousands of machines.

Apache Spark

Popular as a next-generation batch processing framework, Apache Spark has the capability of stream processing. Spark is an apparent heir of the Big Data processing world. It also includes high-level APIs and platforms like Java, Scala, Python, and R on Spark to help run Spark applications. Spark mainly focuses on fast-forwarding batch processing workloads as it works with its full in-memory computation and processing optimization.

Apache Storm

Apache Storm is a free, open-source Big Data computation system, and the most accessible tool for Big Data analysis. It offers a distributed, fault-tolerant processing system with real-time computation capabilities. It has a record of processing one million 100 bytes messages per second and per node. Apache Storm is scalable and easy to operate for any vendor and undertakes parallel calculations that run across a set of machines.

Kaggle

Famous as the world’s largest Big Data community, Kaggle allows companies and researchers to publish their data & statistics. Kaggle is a significant contributor to the open data movement and also is exceptional at connecting data from other platforms. It’s the best platform to analyze data effortlessly.

Open Refine

Another powerful Big Data tool, OpenRefine, specializes in working with disorganized data. It helps cleaning and transforming cluttered data from a messy format to an organized one. It can also connect data by utilizing web services and external data. With the OpenRefine tool, one can process a large volume of data quickly. It can also import data in different formats and explore databases within a fraction of second.

Cloudera

Cloudera is a safe and secure advanced Big Data platform that’s fast, reliable, and efficient in data processing. With Cloudera, you can access any form of data across any environment within a single, scalable platform and achieve high-performing analytics.

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Pentaho

With Pentaho, Big Data tools can extract, process, prepare, and merge data. It offers users data access and integration for compelling data visualizations. Pentaho allows its users to design Big Data at the source to stream them accurately for analytics. It has unique capabilities that help it support the wide gamut of Big Data sources.

Apache Flink

An open-source stream processing Big Data framework, Apache Flink is a high performing, accurate data streaming application. It’s capable of performing on a large scale and can even run on thousands of nodes. Also, it can recover any fault-tolerant applications from failures. It provides accurate results, even for data that arrives late and is out of the direction. Apache Flink also supports third-party systems for sourcing of data.

Conclusion

Big data is not as simple as it seems. When it comes to big data, its features allow it to process through simple data filters, data mining, and refined tools. Also, it consists of large volumes of complex data that needs to be managed through a robust, seamless processing software since traditional data processing systems are incapable of doing so. The tools or frameworks that need to process Big Data requires features such as:

  • Privacy
  • Searching
  • Sharing
  • Storage
  • Capturing
  • Analysis
  • Querying
  • Updating
  • Transferring
  • Structuring
  • Organizing
  • Visualization, and
  • Data security

The frameworks that we have discussed above are amongst the most popular ones in the current market trends. You can work with the framework that best suits your business and data analysis requirements. Some more like HBase, Presto, Kafka, Impala, and Homegrown to are popular among many data analysts. In addition, the Big Data frameworks are also used to store data so that users can perform their tasks faster while increasing the speed of processing and analyzing data that’s presented.

By Guest Author,  Sai Digbijay Patnaik

Sai Digvijay is a content specialist for Big Data Hadoop courses at Simplilearn. He writes about a range of topics that include Cybersecurity, Data Science, Artificial Intelligence, and Machine Learning. He values curious minds and scrambles to learn new things.

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