Top 5 Big Data Tools For Data Analysis
Nghi Tran-Hoang-Phuong

Top 5 Big Data Tools For Data Analysis

Big Data has become an integral part of businesses today and companies are increasingly looking for people who are familiar with Big Data tools. Top 5 Big Data Tools For Data Analysis Employees are expected to be more competent in their skill sets and to demonstrate talent and thought processes that match the company’s particular duties. The popular so-called in-demand skills have been phased out, and if there’s one thing that’s trendy right now, it’s Big Data analytics. So, if you’re someone looking to switch to Big Data analytics and confused about the Big Data analytics tools you should learn to make a successful jump, here’s a comprehensive list to consider.

1. Big Data Tools: HADOOP

Expert data scientists understand that Big Data is incomplete without Hadoop. Hadoop is an open-source Big Data analytics technology that provides huge storage for a variety of data types. Hadoop’s incredible processing power and ability to perform a wide range of activities means you’ll never have to worry about hardware failure. Working with Hadoop requires knowledge of Java, but it is well worth the effort. Knowing Hadoop will put you ahead of the competition when it comes to hiring. Pros:
  • Hadoop’s core strength is its HDFS (Hadoop Distributed File System), which holds all types of data, video, images, JSON, XML and plain texts across the same file system.
  • Very useful for research and development purposes.
  • Offers easy data access.
  • Extremely scalable
Cons:
  • Data redundancy can often cause disk space problems.
  • For improved efficiency, I/O operations should have been optimized.

2. Big Data Tools: XPLENTY

All data sources are brought together in this cloud-based Big Data Analytics application for integrating, analyzing, and preparing data. Its user-friendly graphical interface lets you work with ETL, ELT, or replication. Xplenty is a comprehensive set of tools for building low-code and no-code data pipelines. It offers marketing, distribution, and development options. Pros:
  • It is a cloud network that is elastic and scalable.
  • You can immediately access a range of data stores and a diverse collection of data transformation components.
  • By using the rich expression language of Xplenty, you can incorporate complex data preparation functions.
  • It offers a customized and flexible API component.
Cons:
  • There is no option for monthly subscription.
Top 5 Big Data Tools For Data Analysis

3. CDH (CLOUDERA DISTRIBUTION FOR HADOOP)

On its free distribution site, CDH includes Apache Hadoop, Apache Spark, Apache Impala, and many other open-source Big Data Analytics tools. It allows you to collect, store, manage, discover, model, and distribute an infinite amount of data. Pros:
  • Complete and accurate distribution.
  • The Hadoop cluster is very well managed by the Cloudera Manager.
  • Simple to deploy.
  • The administration is less complicated.
  • High security and administration
Cons:
  • Few complicated user interfaces like CM service charts.
  • Several suggested installation methods are confusing.

4. Big Data Tools: R

R is a statistical analysis tool that is one of the most comprehensive Big Data analytics tools available. Open-source, free, multi-paradigm, and diversified software ecosystems exist. C, Fortran, and R are the programming languages. Data processing, data manipulation, analysis, and visualization are some of the most common use cases for statisticians and data miners. Pros:
  • The greatest value of R is the immensity of the ecosystem package.
  • Unparalleled Graphics and charting features.
Top 5 Big Data Tools For Data Analysis

5. CASSANDRA

Apache Cassandra is free of cost Big Data analytics tools designed to handle large quantities of data across many commodity servers, offering high-availability. The open-source NoSQL DBMS uses CQL (Cassandra Structure Language) to interact with the database. Pros:
  • There is no single failure point.
  • It manages huge data really quick.
  • It has log-structured storage and linear scalability.
Cons:
  • Extra troubleshooting and maintenance work is required.
  • It could have boosted the clustering.
  • There is no row-level locking feature.
Related Article: https://www.jigsawacademy.com/8-big-data-tools-need-know/