Top 10 Open Source Big Data Tools in 2019

Today, big data tools has an extensive usage in almost every organization, and the big data tools have flooded the market. Big data not only brings efficiency in the cost but also conducts a better time management into the data analytical tasks. With this in mind, open source big data tools for big data processing and analysis are the most useful choices of organizations, considering the cost and other benefits.

There are multiple aspects which are considered on the big data concern. Like we say, what size do the data sets have, what analysis is to be done on the data sets, what about the expected outcome etc. Hence, the big data open source tools list could be categorized on the following basis: data stores like development platforms, development tools, integration tools, for analytics and reporting tools.

As we move closer to the big data open source tools list, it can be bewildering. As there is a deep requirement of having all relevant data secured at one place without any loss in the previously stored data, the organizations are rapidly developing new solutions to achieve the competitive advantage in the big data market. It would be useful to focus more on open source big data tools which are driving the big data industry. We take a look at top 10 Open Source Big Data Tools in the World

1. Apache Spark

Apache Spark would be the big boom in the industry, among the big data tools. The basic concern of this open source big data tool is that, it is focusing on filling the space of Apache Hadoop, keeping in mind about the data processing. Batch data and real-time data, both can be handled by Spark well. As Spark processes the in-memory data, the data is processed much faster than the traditional disk processing. Indeed, this is a positive point for the data analysts handling certain data types for achieving the faster outcome.

Apache Spark is flexible to work with HDFS and the other data stores as well, like Apache Cassandra, OpenStack Swift. Spark could also run on a single local system to make development and testing easier.

Features –

  • Speed of the tool: Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing the number of reading/write operations to disk. It stores the intermediate processing data in memory.
  • Supporter of Multiple languages: Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
  • Advanced Analytics: Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph Algorithms.

2. Hadoop

The Apache Hadoop Software Library is among the most prominent and used tool in the big data industry. Distributed Processing of large data sets enormously, across clusters of computers has been the main concern of Hadoop. This open source framework is designed to run on commodity hardware in an existing data center. Moreover, it can also run on a cloud infrastructure. Hadoop has an ability to scale up from single servers to thousands of machines. It offers robust ecosystem that is well suited to meet the analytical needs of developer.

Features – 

  • Write efficient Distributed Systems: Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
  • High-quality service without harware: Hadoop does not rely on hardware to provide fault-tolerance and high availability
  • Operates without Interruption: You can add or remove the cluster dynamically and Hadoop continues to operate without interruption
  • Comfortable with all platforms: Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms

3. Cassandra

Apache Cassandra is a distributed type database, which is widely used to manage a large set of data across the servers. Cassandra focuses on processing the structured data sets. It provides lower latency to users and replicates the data to multiple nodes for fault-tolerance. Highly available service is offered by Cassandra, as it ensures about not a single point of failure. Additionally, it has certain capabilities which no other relational database and any NoSQL database can provide. Numerous concurrent users across data centers could be handled by Cassandra. With a linear scalable performance, Cassandra enables easy data distribution across the data centers.

Features –

  • Elastic Scalability: Cassandra is highly scalable; it allows to add more hardware to accommodate more customers and more data as per requirement
  • Always on Architecture: Cassandra has no single point of failure and it is continuously available for business-critical applications that cannot afford a failure
  • Fast linear-scale Performance: Cassandra is linearly scalable, i.e., it increases your throughput as you increase the number of nodes in the cluster. Therefore it maintains a quick response time
  • Flexible Data Storage: Cassandra accommodates all possible data formats including: structured, semi-structured, and unstructured. It can dynamically accommodate changes to your data structures according to your need
  • Easy Data Distribution: Cassandra provides the flexibility to distribute data where you need by replicating data across multiple data centers
  • Transaction Support: Cassandra supports properties like Atomicity, Consistency, Isolation, and Durability (ACID)
  • Fast Writes: Cassandra was designed to run on cheap commodity hardware. It performs blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the read efficiency

4. Storm

Apache Storm is a free, open source distributed real-time framework, offering fault-tolerant processing system for the unbounded data stream. This computation system supports multiple programming language. Parallel Calculations are used by Storm, which run across the cluster of machines. It has fail fast, auto-restart, approach, in case a node dies. Storm supports the Direct Acrylic Graph Topology. It is surely an easiest tool for Bigdata Analysis, once deployed. It can interoperate with Hadoop’s HDFS through adapters if required, which is another positive point to make it a very useful open source big data tool.

Features –

  • Massive scalability: Apache storm works with massive scalability
  • Fault-tolerance: It offers fault-tolerant processing system for unbounded data stream
  • Fail fast, auto restart approach: Apache storm offers fail fast and auto restart approach to its users
  • Written in Clojure: The language of the Apache storm is written on Clojure
  • Runs on the JVM: Apache Storm has an ability to run on the JVM
  • Direct acrylic graph(DAG) topology: Storm is a supporter of DAG topology
  • Multiple languages: Apache Storm supports multiple languages
  • Supports protocols like JSON

5. Rapid Miner

Rapid Miner is an open source software platform for data science activities, providing an integrated environment for data preparation, machine learning, text mining, visualization, predictive analysis, application development, prototyping, model validation, statistical modelling, evaluation, deployment, etc. Rapid Miner offers a suite of products to develop a new data mining processes. This open source big data tool follows a client/server model, where the server could be located on-premise, or in a cloud infrastructure. It is written in Java and provides a Graphical User Interface or batch processing to design and execute workflows.

Features –

  • Multiple data management: Rapid Miner allows multiple data management to its users
  • GUI or batch processing: Rapid Miner uses both, GUI or batch processing
  • Integrates with in-house databases: This big data tool has an ability to integrate with in-house databases
  • Interactive, shareable dashboards: The dashboards in Raid Miner are interactive they could be shared
  • Big Data predictive analytics: Rapid Miner allows Big Data Predictive analytics to its users
  • Remote analysis processing: It consists of remote analysis processing 

6. Apache SAMOA

Apache SAMOA has been a big name in the Open source big data tools. SAMOA is used for distributed streaming algorithms for big data mining. It has got immense importance among the industry, as it could be programmed and run everywhere. It does not require any complex backup or update process. SAMOA’s existing infrastructure is reusable, and the deploying cycles could be avoided. Not only for data mining, but it is also used for other machine learning tasks like clustering, regression, classification, programming abstractions for new algorithms, etc.

Features –

  • Runs Everywhere: The Program can be run everywhere
  • Reusable Infrastructure: Its existing infrastructure is reusable. Hence, deploying cycles can be avoided
  • System downtime: There is no system downtime in Apache SAMOA
  • Complex Backup: There is no need for complex backup or update process in this big data tool

7. Mongo DB

MongoDB is an open source NoSQL database, and is cross-platform compatible with many built-in features. Any data type could be stored in Mongo DB like integer, array, object, string, Boolean, date, etc. Mongo DB is ideal for the users who want data-driven experiences. It performs on Java Platform, NET applications and MEAN software stack. It can work really well for the business that needs fast and real-time data for instant decisions. It provides flexible cloud-based infrastructure. It can easily partition the data across the servers in a cloud structure.

Features –

  • Data Storage: It can store any type of data like integer, string, array, object, boolean, date etc
  • Flexible Cloud Infrastructure: It provides flexibility in cloud-based infrastructure
  • Data Partition: It is flexible and easily partitions data across the servers in a cloud structure
  • Cost-saving tool: MongoDB uses dynamic schemas. Hence, you can prepare data on the fly and quickly. This is another way of cost saving

8. HPCC

High Performance Computing Cluster (HPCC) is another big data tool, developed by LexisNexis Risk Solution. It runs under the Apache 2.0 license. HPCC offers high redundancy and availability. It could be used for both, Thor cluster and complex data processing. It supports end-to-end big data workflow management. HPCC maintains code and data encapsulation. It compiles into C++ and native machine code. It comes with binary packages supported for Linux distributions and runs on commodity hardware. It can build graphical execution plans.

Features – 

  • Data Processing: HPCC helps in parallel data processing to its users
  • Open-source data: HPCC allows open-source distributed data computing platform
  • Shared nothing architecture: HPCC follows the shared nothing architecture
  • Commodity Hardware: HPCC runs on commodity hardware
  • Binary Packages: HPCC comes with binary packages supported for Linux distributions
  • End-to-end management: HPCC supports end-to-end big data workflow management

9. R Computing Tool

R Computing Tool is a widely used open source big data tool, mainly focusing on data modelling and statistics. It has its own public library CRAN (Comprehensive R Archive Network), consisting of more than 9000 modules and algorithms for statistical analysis of data. It is written in three different programming languages – C, Fortran and R. R performs effortless data handling with excellent storage facility. It offers a coherent collection of various big data tools that can be used for data analysis. It can run on Windows and Linux as well inside SQL server.

Features –

  • Data handling and storing: R Computing Tool has effective data handling and storage facility
  • Calculations: It is a suite of operators for calculations on arrays, in particular matrices
  • Data Analysis: Large, coherent, integrated collection of intermediate tools for data analysis
  • Graphical Facilities: It has graphical facilities for data analysis and display either on-screen or on hard-copy
  • Simple Programming Language: This big data tool has well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities

10. Neo4j 

Neo4j is an open source big data tool, which is widely used graph database in big data industry. It follows the fundamental structure of graph database, which is interconnected node-relationship of data. It supports the ACID transaction. It provides highly scalable and reliable performance. It does not need a schema or data type to store data, hence making it flexible. Neo4j can be integrated with other databases. It maintains a key-value pattern in data storing. It supports query language for graphs which is known as Cypher.

Features –

  • ACID Transaction: Neo4j is a big data tool that supports ACID transaction
  • High availability: It offers high availability to ts users
  • Scalable and reliable: The users can be work on Neo4j platform as it is highly scalable and offers reliable performance
  • Flexibility: It is as flexible as it does not need a schema or data type to store data
  • Integrity: It can integrate with other databases
  • Language: Supports query language for graphs which is commonly known as Cypher

The entire spectrum of the big data tools above, are rolling out to be the most specific cogs in the big data clock-house to carry out a compelling range of functionalities that develop companies more nimble, more efficient and more welcoming to the changing market forces. As the market only assures to produce more and more data for every facet of any business, the reason is big data, as it holds the promise of helping a business unit out there, to make sense of the ever growing oceans of data.

*This list of the top 10 Open Source Big Data Tools, is a result of the editorial decision of ELE Times.

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