Every large organization operates with big data. It solidifies their position at the frontlines in their industries. Big data enable an organization to save costs, reduce decision-making time, understand market conditions faster, control their online reputation, and boost customer acquisition and customer retention. But without effective tools to process and analyze big data, it’s as good as nothing. That’s why every organization must utilize the best big data platform to achieve speed and maintain a competitive advantage over competitors.
In this article, we’re going to explore the top ten open source data platforms out there for your big data collection and analysis. Our list didn’t follow any form of pattern.so, you can consider each one and select the best that matches your business needs.
Here is one big data tool that is making waves in the industry in 2020. This tool covers the gap which Hadoop created relative to data processing. One of the high points of Apache Spark is that it handles real-time data and batch data. It also does what we know as “in-memory” processing, which is a much faster way of data processing. So, any analyst working on specific types of data can leverage Spark to achieve a quicker outcome.
Spark works with HDFS due to its flexible nature. It also works with other stores such as Cassandra and OpenStack. The best part is that you can run Spark very smoothly on one local system, which in turn facilitates development and testing.
Features of Spark
• Spark is very fast and can run an application in the Hadoop cluster 100 times faster when running in-memory and ten times more quickly when it runs on disk.
• Apache Spark supports many languages, such as Java, Python, or Scala. Users can write an application in any language they want, especially those supported by Spark.
• This big data tool offers advanced analytics, such as Graph Algorithms, SQL queries, Machine learning, etc.
Apache Storm is an open-source real-time framework suitable for an unbounded stream of data. Many data analyst commend this tool because of its simplicity and support for all programming languages. This system uses parallel calculation, and it features fail fast and auto-restart approach in an event where a node dies. Apache Storm can interoperate with Hadoop’s HDFS via an adapter and offers multiple user benefits.
Features of Storm
• Fault tolerance
• Fail fast, auto-restart approach
• Supports many programming languages
• Supports JSON protocol
This big data tool is top-rated amongst prominent data analysts because it supports distributed data processing on clusters of computers. It runs on commodity hardware and also runs on a cloud infrastructure seamlessly. It scales up easily from single servers to thousands of machines. Hadoop has a robust ecosystem and facilitates the analytics of big data for developers.
Features of Apache Hadoop.
• The file system is compatible with high scale bandwidth
• It features MapReduce which facilitates big data processing
• Hadoop integrates YARN for managing & scheduling resources
• Some libraries enable other modules to work with the tool.
This big data tool is also among the top players in the industry. It is suitable for managing large data sets across many serves and processes sets of structured data. Cassandra handles many concurrent users across many data centers. It also offers lower latency and replicates data to various nodes to ensure fault-tolerance.
Features of Cassandra
• Massive scalability
• Quick response time
• Zero-point of failure
• Flexible storage
• Seamless data distribution
• Transaction Support
• Fast writes
This big data tool offers an integrated platform where users can carry out processes such as data preparation, text mining, predictive analysis, machine learning, evaluation, statistical modeling, deployment, etc. RM follows a client/server model and offers multiple products for developing mining processes. It also provides a GUI or batch processing where you can design & execute workflows.
Features of Rapid Miner
• Graphical User Interface/Batch Processing.
• Features interactive and shareable dashboards
• Enables predictive analytics on big data
• Allows for data management
• Enables remote analysis processing
Mongo DB is another big data tool that enables a user to store any type of data. It has impressive built-in features and serves multiple users seamlessly. You can use it on the MEAN software stack, Java platform, or NET applications. If your business requires real-time data to make meaningful decisions, Mongo DB is your best option. Its infrastructure is flexible and also based on the cloud.
Features of Mongo DB
• Stores various data types
• Saves cost
• Offer real-time data
• It features a cloud-based, flexible infrastructure.
If you have a graph database, this open-source data tool is for you. It follows an interconnected node relationship of data and supports ACID transactions. Being a schema-less tool, usage is flexible, and it also supports Cypher-a query language used for graphs.
Features of Neo4j
• Supports ACID transaction
• Supports Cypher
• Integrates various databases.
SAMOA is suitable for distributed streaming algorithms used in data mining. It can be programmed everywhere and doesn’t need complex backup or difficult update process. Its infrastructure can be reused, and it handles multiple ML tasks such as regression, programming, etc.
Features of SAMOA
• No need for complex backup
• The program runs anywhere
• Apache SAMOA doesn’t experience downtime
• Infrastructure is reusable.
High Performance Computing Cluster
HPCC is a tool that runs under Apache 2.0 license, and LexisNexis Risk Solution developed it. It is suitable for complicated data processing operations and also works on the Thor cluster. HPCC features binary packages for Linux distribution. Also, it runs on commodity hardware.
Features of HPCC
• Open-source data
• Binary Packages
• Data Processing
• Commodity Hardware
• Shared nothing architecture
• End-to-end management
R Computing Tool
This tool focuses on data modeling and statistics. It comes with a unique library CRAN, which contains 9000 algorithms and modules for data analysis. R computing tool is written in 3 programming languages, which include Fortran, R, and C. this tool has an impressive storage facility and runs seamlessly on Linux, SQL Server, and Windows.
Features of R Computing Tool
• Supports statistical data analysis
• Excellent data storage facility
• Offers graphical facilities
• Aids Calculations
• Easy-to-read programming language.
Companies will continuously generate and use large volumes of data for business decisions. That’s why there is an unprecedented demand for data analysts. Every data analyst can perform faster and efficiently by leveraging any of the big data tools in this article. We recommend applying for training in Hadoop as it also works with other tools here.
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