Nearly every organization today makes extensive use of big data to achieve the competitive edge in the market. With this in mind, Open Source Big Data Tools for Big Data Processing and Analysis are the organizations’ most useful choice, considering the cost and other benefits. Hadoop is the industry’s leading open source project, and the big data bandwagon roller.
Today, while we are talking about big data devices, there are many facets to the picture about it. For example, how big the data sets are, what kind of analysis we will do on the data sets, what the expected output is, etc. Hence, broadly speaking, we can categorize the list of big data open source tools into the following categories: data storage based, as platforms for development, as development tools, integration tools, analytics and reporting tools.
1. Apache Hadoop
With its immense capacity to process data on a wide scale, Apache Hadoop is the most popular and used platform in big data industry. This is a 100% open source platform, which runs in an existing data center on commodity hardware. It can also run on a Cloud infrastructure. Hadoop is composed of four parts.
- Hadoop Distributed File System commonly known as HDFS, it is a distributed compatible file system with very high bandwidth scale.
- MapReduce: A Big Data Processing programming model.
- YARN: It is a platform used to manage and Big data scheduling Hadoop’s infrastructure resources within Hadoop.
- Libraries: Using Hadoop to help other modules work.
2. Apache spark
Apache Spark is the next big-data sensation in the industry. The key point of this open source big data tool is that it fills the data processing gaps in Apache Hadoop. Interestingly, Spark is capable of handling both batch data and data in real time. Since Spark processes data in memory, it processes data much more quickly than traditional disk processing. This is indeed a plus point for data analysts to handle certain data types to achieve the quicker outcome.
Apache Spark is flexible to work with both HDFS and other data stores, such as OpenStack Swift or Apache Cassandra for example. To make development and testing easier it is also quite easy to run Spark on a single local system.
Spark Core is at the heart of the project and it makes a lot of things like
- Transmission of delegated tasks
- Timeline
- I / O Features
Spark is an alternative to a MapReduce from Hadoop. Spark can execute jobs 100 times faster than the MapReduce from Hadoop.
3. Apache Tempest
Apache Storm is a distributed real-time framework designed to process the unbounded data stream reliably. Any programming language is supported by the framework. Apache Storm is unique in its features.
- Enormous scalability
- Tolerance to failure
- “Fail quick, reboot auto” approach
- Process guaranteed for every tuple
- Authored in Clojure
- Goes on the JVM
- Supports topology with Direct Acrylic Graph(DAG)
- Does multiple languages support
- Supports JSON-like Protocols
Topologies of storms may be considered similar to MapReduce job. However, in the case of Storm, it is the processing of real-time stream data rather than batch data. Storm scheduler distributes the workloads to nodes, based on the topology configuration. If required, Storm can interoperate with Hadoop’s HDFS via adapters which is another point making it useful as an open source big data tool.
4. Cassandra
Apache Cassandra is a distributed database of sorts for the management of a wide collection of data across servers. This is one of the best big data tools which processes structured data sets primarily. It provides highly available service without a single fault point. Furthermore, it has certain capabilities that no other relational database or NoSQL database can provide. Those skills are as follows.
- Continuous accessibility as source of data
- Scalable Linear Output
- Simple moves
- Easy distribution of data across the data centre
- Cloud Accessibility Pointg
- Tailoring
- Achievement
The architecture of Apache Cassandra does not follow master-slave architecture, and all nodes play equal roles. It can handle numerous users at the same time across data centres. Therefore, adding a new node in the existing cluster doesn’t matter even at the time it is up.
5. Rapid Miner
Rapid Miner is a data science software platform, and provides an integrated environment for as follows.
- Data Preparation
- Learning the Machine
- Mining text
- Forecast analytics
- Learning Deeply
- Production of applications
- Prototyping
This is one of the useful big data tools which support various machine learning measures, such as follows.
- Preparing data
- Visualising
- Forecast analytics
- Validation Model
- Optimizing the
- Modeling figures
- Assessment
- Upgrade
Rapid Miner follows a client or server model where it is possible to locate the application on-site or in a cloud network. It is written in Java, and provides a GUI for workflow design and execution. This can have an integrated analytical solution of 99 percent.
6. Mongo DB
Mongo DB is a C, C++, and JavaScript-written NoSQL, document-oriented database. It is free to use, and it is an open source tool that supports multiple operating systems including Windows Vista (and later), OS X (10.7 and later), Linux , Solaris, and FreeBSD.
Its key features include
- aggregation,
- adhoc-
- queries,
- uses of BSON format,
- sharding,
- indexing,
- replication,
- server-side
- javascript execution,
- schemaless,
- caped array,
- MongoDB management service (MMS), load balancing, and file storage.
Some of MongoDB’s big clients include Facebook, eBay, MetLife, Google etc. SMB and company versions of MongoDB are paid, and pricing is available on request.
7. Neo4j
Hadoop may not be a wise choice for all problems relating to big data. For example, a graph database may be a perfect choice when dealing with large volumes of network data or graph related issues such as social networking or demographic patterning.
Neo4j is one of the big data tools in the big data industry which is widely used graph database. It follows the basic graph database structure, which is the interconnected node-relationship of data. It maintains a pattern of key-value when storing data.
Neo4j’s Notable features are as follows.
- It supports transaction to ACID
- High Velocity data
- Scalable, reliable and
- Flexible since it does not need a schema or type of data to store data
- Could be integrated with other databases
- Supports query language for graphs commonly referred to as the Cypher.
8. Lumify
Lumify is a free , open source tool for fusion / integration, analytics, and visualization of big data.
Its primary features include the following.
- full-text search,
- 2D and 3D graph visualizations,
- automated templates,
- analysis of linkages between graph entities,
- integration with mapping systems,
- geospatial analysis,
- multimedia analysis,
- real-time collaboration across a collection of projects or workspaces.
9. HPCC
HPCC stands for High-Performance Cluster computing. This is a complete big data solution over a supercomputing platform which is highly scalable. HPCC is also known as DAS (Supercomputer for Data Analytics). LexisNexis Risk Solutions developed the tool. This tool is written in C++ and a programming language known as ECL (Enterprise Control Language), which is data centered. The features of HPPC are as follows.
- Helps to process data in parallel
- Open Source distributed data platform
- Followers didn’t share anything about architecture
- Runs on commodity equipment
- Comes with supporting binary packages for Linux distributions
- Supports end-to — end management of big data workflows
10. SAMOA on Apache
SAMOA stands for Online Scalable Advanced Massive Analytics. It’s an open source platform for the mining and machine learning of big data streams.
It enables you to create algorithms for distributed streaming machine learning ( ML) and run them on multiple DSPEs distributed stream processing engines. The closest alternative to Apache SAMOA is tool Big ML. The features of Samoa are as follows.
- You can program once and run it wherever you want
- Its existing infrastructure can be used for reuse. Therefore, you can avoid cycles to deploy.
- No downtime on the machine
- No need for complex process backups or updates
Conclusion
I hope you reach a conclusion about 10 big data tools in Hadoop in 2020. You can learn more through big data online training
