728 x 90

12 Best Big Data Tools in 2020

12 Best Big Data Tools in 2020

List of the Best Big Data Tools in 2020: Today, almost every organization uses Big Data to gain a competitive advantage in the market. In this momentum, open-source big data tools for big data processing and analysis are the most useful choice for organizations given the cost and other benefits. Hadoop is the most successful

List of the Best Big Data Tools in 2020:

Today, almost every organization uses Big Data to gain a competitive advantage in the market. In this momentum, open-source big data tools for big data processing and analysis are the most useful choice for organizations given the cost and other benefits.

Hadoop is the most successful open source project in the big data industry. But this is not the end! There are many other vendors that follow the open-source path of Hadoop.

There are several software programs for Big Data. This software helps store, analyze, report, and do a lot more with data. Based on popularity and usability, we’ve listed the 12 best big data tools in 2020.

  1. Apache Hadoop
  2. CDH (Cloudera Distribution for Hadoop)
  3. Apache Spark
  4. Cassandra
  5. Apache Storm
  6. RapidMiner
  7. Knime
  8. MongoDB
  9. Datawrapper
  10. R programming tool
  11. Neo4j
  12. Apache SAMOA

 

1. Apache Hadoop

Hadoop

Apache Hadoop is a framework used for clustered file systems and large data management. It processes big data datasets using the MapReduce programming model.

Hadoop is an open-source framework written in Java and it provides cross-platform support. It is without a doubt the most powerful big data tool.

In fact, the majority of Fortune 50 companies use Hadoop. Some of the big names include web services from Amazon, Hortonworks, IBM, Intel, Microsoft, Facebook, etc.

Benefits:

  • The main strength of Hadoop is its HDFS (Hadoop Distributed File System) which has the ability to store all types of data – video, images, JSON, XML and plain text on the same file system.
  • Very useful for R&D purposes.
  • Provides quick access to data.
  • Very scalable
  • Highly available service based on a cluster of computers.

Disadvantages:

  • Sometimes, disk space issues may be encountered due to its 3x data redundancy.
  • I / O operations could have been optimized for better performance.

Pricing: This software is free to use under the Apache license.

 

2. CDH (Cloudera Distribution for Hadoop)

CDH (Cloudera Distribution for Hadoop)

CDH aims at enterprise-class deployments of that technology. It is totally open source and has a free platform distribution that encompasses Apache Hadoop, Apache Spark, Apache Impala, and many more.

It allows you to collect, process, administer, manage, discover, model, and distribute unlimited data.

Benefits:

  • Full distribution Cloudera Manager manages the Hadoop cluster very well.
  • Easy to implement.
  • Less complex administration.
  • High security

Disadvantages:

  • Few complex user interfaces feature graphics on the CM service.
  • Several recommended approaches for installation seem confusing.

Pricing: CDH is a free software version of Cloudera. However, if you want to know the cost of the Hadoop cluster, the cost per node is around $ 1000 to $ 2000 per terabyte.

3. Apache Spark

Apache Spark

What makes this open-source big data tool unique is that it fills Apache Hadoop’s data processing gaps. Interestingly, Spark can handle both batch data and real-time data.

Because Spark does in-memory data processing, it processes data much faster than traditional disk processing. This is indeed a positive point for data analysts who manipulate certain types of data to achieve faster results.

Apache Spark is flexible to work with HDFS as well as other data stores like OpenStack Swift or Apache Cassandra. It’s also fairly easy to run Spark on a single local system for easy development and testing.

Spark Core is the heart of the project, and it facilitates many things like:

Benefits:

  • Distributed task transmission
  • Planning
  • I / O functionality

Disadvantages:

  • Problem with a small file
  • No file management system
  • Problem with small files

Pricing: This tool is free.

4. Cassandra

Cassandra

Apache Cassandra is an open-source, distributed NoSQL DBMS designed to handle huge volumes of data spread across many servers. It uses CQL (Cassandra Structured Language) to interact with the database.

Apache Cassandra is one of the best big data tools that mainly deals with structured data sets. It provides a highly available service with no single point of failure. Plus, it has certain capabilities that no other relational database and NoSQL can provide.

Benefits:

  • No single point of failure.
  • Handles big data very quickly.
  • Structured log storage
  • Automated replication
  • Linear scalability
  • Simple Ring Architecture

Disadvantages:

  • Requires extra effort in troubleshooting and maintenance.
  • Improvement of Clustering.
  • The row-level locking feature is not there.

Pricing: This tool is free.

5. Apache Storm

Apache Storm

Apache Storm is a real-time distributed framework that reliably handles unlimited data streams. The framework supports any programming language.

Its architecture is based on custom jaws and bolts to describe information sources and manipulations to enable batch and distributed processing of unlimited data streams.

Benefits:

  • Very fast and Error tolerance
  • Written in Clojure
  • Supports multiple programming languages
  • Reliable on a large scale.
  • Ensures data processing.
  • It has multiple use cases – real-time analysis, log processing, Extract-Transform-Load (ETL), continuous computing, distributed RPC, machine learning.

Disadvantages:

  • Difficult to learn and use.
  • Debugging difficulties.
  • Using Native Scheduler and Nimbus are becoming bottlenecks.

Pricing: This tool is free.

6. RapidMiner

RapidMiner

Rapidminer is a cross-platform tool that offers an integrated environment for data science, machine learning, data preparation, text mining, predictive analytics, deep learning, application development, and prototyping.

It is supplied under different licenses which offer small, medium, and large proprietary editions as well as a free edition that accepts 1 logical processor and up to 10,000 rows of data.

Benefits:

  • Open-source Java kernel.
  • The convenience of frontline data science tools and algorithms.
  • Optional GUI functionality with code.
  • Integrates well with APIs and the cloud.
  • Excellent customer service and technical support.

Disadvantages:

  • Improvement of Online data services.

Pricing: The commercial price for Rapidminer starts at $ 2,500.

7. Knime

Knime

KNIME stands for Konstanz Information Miner which is an open-source tool used for business reporting, integration, research, CRM, data mining, data analysis, text mining, and business intelligence.

It supports Linux, OS X, and Windows operating systems.

Benefits:

  • Simple Extract Transform Load (ETL) operations
  • Integrates very well with other technologies and languages.
  • Highly usable and organized workflows.
  • Automation of many manual jobs.
  • No stability problem.
  • Easy to install.

Disadvantages:

  • Improvement of data processing capacity.
  • Uses RAM a lot.
  • Could have allowed integration with graph-oriented databases.

Pricing: The Knime platform is free. However, they offer other commercial products that extend the capabilities of the platform.

8. MongoDB

MongoDB

MongoDB is a NoSQL document-oriented database written in C, C ++, and JavaScript. It is free to use and is an open-source tool that supports multiple operating systems: Windows, OS X, Linux, Solaris, and FreeBSD.

Its main features include aggregation, ad hoc queries, uses BSON format, sharing, indexing, replication, javascript server-side execution, Schemaless, capped collection, MongoDB management service (MMS), load balancing, and file storage.

It is ideal for businesses that need fast, real-time data for instant decisions.

Benefits:

  • Easy to learn.
  • Supports multiple technologies and platforms.
  • No difficulties during installation and maintenance.
  • Reliable and low cost.

Disadvantages:

  • Limited analytics.
  • Slow for some use cases.

Pricing: The SMB and Enterprise versions of MongoDB are chargeable and prices are available on request.

9. Datawrapper

Datawrapper

Datawrapper is an open-source data visualization platform that helps its users to generate simple, precise, and integrable charts very quickly.

Benefits:

  • Works great on all types of devices – mobiles, tablets, or desktops.
  • Fast and Interactive
  • Collect all graphics in one place.
  • Great customization and export options.
  • Does not require any coding.

Disadvantages:

  • Limited color palettes

Pricing: It offers free service as well as customizable paid options.

10. R

R

It is one of the most widely used open-source tools in the big data industry for statistical analysis of data.

The best part about this big data tool is – although used for statistical analysis, as a user you don’t need to be an expert in statistics. R has its own Comprehensive R Archive Network (CRAN) public library, which includes more than 9,000 modules and algorithms for statistical analysis of data.

R can run on Windows and Linux servers as well as SQL servers. It also supports Hadoop and Spark. Using the R tool, one can work on discrete data and try out a new analytical algorithm for the analysis.

It is a portable programming language. Therefore, a model built with R and tested on a local data source can be easily implemented in other servers or even on Hadoop data.

Benefits:

  • The biggest advantage of R is the sheer size of the package ecosystem.
  • A large variety of graphics.

Disadvantages:

  • Its shortcomings include memory management, speed, and security.

11. Neo4j

Neo4j

Hadoop may not be the right choice for all big data issues. For example, when you need to deal with a large volume of network data or a graph related issue like social media or demographic model, a graph oriented database can be a perfect choice.

Neo4j is one of the big data tools with a graph-oriented database widely used in the big data industry.

The database follows the structure of interconnected nodes of data. It maintains a key-value model in data storage.

Benefits:

  • It supports ACID transaction
  • High availability
  • Expandable and reliable
  • Flexible because it does not need a schema or data type to store the data
  • It can integrate with other databases
  • Supports the query language for graphs, commonly known as Cypher.

Disadvantages:

  • If you are faced with a high volume of writes, this can quickly become a bottleneck, as only one node will be able to handle these requests.
  • No sophisticated indexing mechanism is supported – search is not efficient in Neo4J

12. Apache SAMOA

Apache SAMOA

Apache SAMOA is one of the well-known big data tools used for streaming algorithms for big data mining.

Not only for data mining, Apache SAMOA is also used for other machine learning tasks such as: classification, clustering, regression …

It runs on top of Distributed Stream Processing Engines (DSPEs). Apache Samoa is a pluggable architecture and allows it to run on multiple DSPEs which include Apache Storm, Apache S4, Apache Samza, Apache Flink.

Benefits:

  • Easy to use.
  • Fast and scalable.
  • True real-time streaming.

 

 

 

Related Posts

Leave a Comment

Your email address will not be published. Required fields are marked with *