In the world of IT, data is everything. To help in analyzing and reporting the data, companies use Big Data tools to determine the behavior on the large scale and further in making efficient decisions. Today, the market is flooded with a wide array of Big Data tools, but choosing the right one is daunting. Wondering, how to choose a reliable tool that can help in determining the data and reducing the overall operational costs.
Here is the list curated by the top Big Data tools that can help in Data Analysis. Let’s dig into the details.
Cassandra
It is an open-source and free tool that helps in the efficient management of large amounts of data, thanks to NoSQL DBMS.
Features
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The data can automatically be replicated to the multiple nodes for fault-tolerance.
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It is considered one of the best big data tools that are widely used to secure massive amounts of data.
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Users get support services from third parties using the tool.
Pros
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There is log-structured storage of data.
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Effortlessly handles massive amounts of data.
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Linear Scalability
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Simple architecture
Cons
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There is no row-locking feature in the tool.
Price: Free
Apache Hadoop
It is a software framework that is used for clustered file systems and managing big data. The tool is designed to scale up from single servers to thousands of machines.
Features
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Supports POSIX-style filesystem.
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Authentication improvements while using the HTTP proxy server.
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Assures fast data processing.
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Flexibility in data processing.
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It uses big data technologies that can offer a robust ecosystem to meet the developers’ needs.
Pros
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Quick access to data.
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Highly scalable.
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Core strength lies in its HDFS (Hadoop Distributed File System) that can hold all types of data including – XML, videos, and images.
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Service resting available.
Cons
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Users sometimes face disk space issues.
Price: It is free under Apache License.
Cloudera
Cloudera is considered to be the easiest, fastest, and highly securable. This open-source tool allows users to collect, process, manage, administer, and distribute unlimited data. This means that anyone can get the data using a single scalable platform.
Features
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There is a provision for multi-cloud.
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Easily spin and terminate the clusters.
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Conducting accurate model scoring.
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Deploy and manage Microsoft Azure, Cloudera Enterprise across AWS, and Google Cloud Platform.
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Delivers real-time insights into detection.
Pros
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Easy to implement.
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Comprehensive distribution
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High security
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Efficiency administers the Hadoop cluster.
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The administration is less complex
Cons
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Complicated UI features like – charts on CM service.
Price: Free
Storm
It is a cross-platform open-source computation system. It is written in Java and Clojure. The architecture of Storm is based on the customized bolts and spouts to process massive data.
Companies like – Yahoo, Groupon, and Alibaba are using a storm.
Features
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Big Data tools use parallel calculations.
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Automatically restarts when a node dies.
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Once deployed successfully, it is possibly the easiest tool for Big Data analysis.
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The tool assures to process each unit of data once.
Pros
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Guarantee the processing of data.
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Reliable and scalable.
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Multiple uses like – log processing, real-time analytics, continuous computation, machine learning, and more.
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Fault-tolerant and fast.
Cons
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You may find difficulties in debugging.
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A bit difficult to learn and use.
Price: Free
Conclusion
All in all, there are ample tools available to support big data operations. But, before choosing any you must consider your project needs and then finalize.
Tip: Always check for reviews or free trial versions before choosing any tool.
Good Luck!