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Table of contents
1.
Introduction
2.
Essential characteristics of Big Data Analysis
2.1.
Support for multiple data types:
2.2.
Handle batch processing and/or real-time data streams:
2.3.
Overcome low latency:
2.4.
Integrate with cloud deployments:
2.5.
Utilize what already exists in your environment: 
2.6.
Support NoSQL and other forms of accessing data:
2.7.
Provide cheap storage:
3.
Application Framework
3.1.
AppFabric
3.2.
Customized Approaches adopted by AppFabric
3.3.
OpenChorus
3.4.
Customized Approaches adopted by OpenChorus
4.
Frequently Asked Questions
4.1.
What is the best framework for extensive data?
4.2.
What are your views on the term "Big Data"?
4.3.
What are the many kinds of Big Data?
4.4.
What does the Big data framework imply?
5.
Conclusion
Last Updated: Mar 27, 2024

Characteristics of a Big Data Analysis Framework

Author Ayush Mishra
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Big Data assists businesses in gaining useful insights. It is used by businesses to improve their marketing efforts and approaches. It is used in machine learning projects, predictive modelling, and other advanced analytics applications by businesses.

Big Data analytics is a set of concepts and characteristics for processing, storing, and analyzing data when standard data processing software is too expensive, too slow, cumbersome, or not fit for the use case to manage the volume of records.

Big Data comprises any data that isn't processed by typical data storage or processing units. Several international corporations use it to process data and conduct business. Before replication, the data flow would reach 150 exabytes per day. 

Essential characteristics of Big Data Analysis

Big Data projects are conceptually tricky for businesses and frequently fail. Some essential characteristics of Big Data Analysis are :

Support for multiple data types:

 Many organizations are using, or plan to use, all forms of data in their big data deployments, including structured, semi-structured, and unstructured data.

Handle batch processing and/or real-time data streams:

Action orientation results from real-time data stream analysis, whereas decision orientation is better served by batch processing. As they expand to encompass various research, some consumers will require both.

Overcome low latency:

You'll need a framework that can satisfy the needs for speed and performance if you're working with significant data velocity.

Integrate with cloud deployments:

On-demand storage and compute capacity can be provided by the cloud. The cloud is increasingly being used as a "sandbox" for research. In a hybrid architecture, the cloud is becoming a vital deployment paradigm for integrating current systems with cloud deployments (public or private). Furthermore, big data cloud services that will benefit customers are beginning to develop.

Utilize what already exists in your environment: 

Using current data and techniques in the big data analysis framework may be necessary to gain the proper context.

Support NoSQL and other forms of accessing data:

While SQL will continue to be used, many firms are exploring more unique kinds of data access to offer faster reaction times or decision times.

Provide cheap storage:

Depending on how much data you can handle and keep, big data can entail a lot of storage. As a result, storage management and associated costs are critical factors.

While all of these properties are significant, the perceived and actual advantage of using a framework to create apps is a faster time to deployment.

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Application Framework

.We'll look at an example of an extensive data analysis application framework from different companies with all the customized approaches adopted by these companies.

AppFabric

The Continuity AppFabric is a framework that facilitates the creation and deployment of big data applications. Without recoding for the target environment, deployment can be a single instance, private cloud, or public cloud. 

The AppFabric is a collection of technologies created to abstract the complexities of low-level big data technology. The application builder is an Eclipse plug-in that allows developers to build, test, and debug their applications locally and in familiar environments.

Customized Approaches adopted by AppFabric

  • Support for streams for real-time analysis and reaction.
  • Data sets that represent queryable data and tables can be accessed through the Unified API.
  • A single API eliminates the requirement to write to large data infrastructures.
  • Reading and writing data without regard to input or output formats or underlying component characteristics.
  • Support for pluggable query processors and query interfaces for superficial results.
  • Event processing is based on transactions.
  • Deployment of multimodal applications to a single node or the cloud.

Because of the diversity of tools and technologies required to construct a big data environment, this technique will likely gain momentum for big data application development.

You should anticipate high-quality, stable programs that can be readily upgraded and deployed if a developer can write to a higher-level API, requiring that the "fabric" or abstraction layer manage the intricacies of the underlying components.

OpenChorus

OpenChorus is another fantastic example of an application framework. It facilitates collaboration and provides many other capabilities crucial to software developers, such as tool integration, version control, configuration management, and the speedy development of large data analysis apps.

EMC Corporation maintains Open Chorus, licensed under the Apache 2.0 license. Chorus is also available in a commercial version, which EMC creates and supports. Open Chorus and Chorus both have active partner networks and many individual and corporate donors.

Customized Approaches adopted by OpenChorus

  • Versioning, change tracking, and archiving are available in this repository of analysis tools, artifacts, and approaches.
  • Workspaces and sandboxes that community members can self-provision and maintain.
  • All data assets, including Hadoop, metadata, SQL repositories, and comments, can be federated.
  • Collaboration using social networking-style elements that promote discovery, sharing, and brainstorming.
  • Integration of third-party components and technologies is possible.

As big data becomes more prevalent, new types of application frameworks will emerge. Many of these will aid in creating mobile applications, while others will focus on vertical application areas. In any case, they're a vital tool for big data early adopters.

Frequently Asked Questions

What is the best framework for extensive data?

Apache Hadoop is perhaps the most widely used Big Data framework. Apache Hadoop is a system for processing massive data volumes across clusters of computers using simple programming techniques.

 

What are your views on the term "Big Data"?

Big data refers to more diverse data that arrives in higher amounts and moves faster.

 

What are the many kinds of Big Data?

Big data can be divided into three categories:

  • Structured Data
  • Unstructured Data
  • Semi-structured Data
     

What does the Big data framework imply?

The Big Data Framework is a framework for companies who wish to get started with Big Data or want to improve existing Big Data capabilities.

Conclusion

In this blog, we have discussed the characteristics of the Big Data Analysis Framework and customized approaches adopted by different companies.

We hope you have a better knowledge of Big Data Analysis Framework Characteristics due to reading this blog. You might also like to learn more about Big Data.

You can also consider our Data Analytics Course to give your career an edge over others.

Happy Learning!

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