Top 9 Big Data Tools In 2019

Introduction

The IT industry is now all about big data. The datasets are of a wide variety of types, size, and complexity. In order to analyze this data, the usage of storage and analysis tools is necessary. This makes the process more affordable and fast. Effective analysis and usage of big data give businesses a competitive edge in the market.

Today, big data management and analysis tools are available in abundance. However, one must understand the aspects of the data one wishes to work on such as data types, sizes, and complexities in order to choose a tool effectively. Here are some of the most popular big data storage, analysis, and management tools.

Apache Hadoop

Apache Hadoop is the best and the most popular big data processing software and has held the spot for a long time. It is based on the Java framework. The most effective of its features is perhaps its scalability. It can scale from single servers to networks efficiently. This gives it capabilities to process an enormous amount of data easily. It runs in parallel over the cluster, which facilitates the optimum distribution of data during the processing. It can run on physical datacentres as well as cloud platforms.

Features and advantages:

• Hadoop Distributed File System: The HDFS is a large bandwidth file distribution system, which distributes the data files across the nodes. It can duplicate the files, ensuring continuous availability.
• YARN: It is a resource management platform, which ensures the effective management of the resources by Hadoop
• It includes programming software models and libraries to facilitate the processing of big data.
• The HDFS can hold any type of file.
• It is compatible with the POSIX type file attributes.

Apache Storm

Apache Storm is made for processing data in a real time frame. This means that it can process nearly boundless amounts of data. It supports all programming languages. The Storm file distribution across nodes depends on the topology configurations. Storm can work in conjunction with Hadoop. It is also considered to be quite fault-tolerant to other tools.

Features and Advantages:

• Automatically restarts in case of a crash. Automatically redirects the user to another node in case a node fails.
• Its data processing scalability is massive. It can process a million 100 byte messages per second per node.
• It supports protocols like JSON. It also supports multiple programming languages.
• It works on the Direct Acrylic Graph topology.
• It is perhaps the easiest tool to manage once deployed.

Apache Cassandra

It is perhaps the most effective tool to process structured datasets. It incorporates failures without any hampering of services. It can be deployed easily over a large number of servers. It is quite widely used, most notably and effectively by Facebook. It’s scalability and processing capacities are enormous.

Features and Advantages:

• The tool can cover a vast number of servers, which minimizes latency and maximizes the processing efficiency.
• Automatic replication and backup of data ensure comprehensive processing. It also means that the data is available in spite of technical failures.
• The tool can be easily operated and provides simple management options for nodes. You can easily add or delete nodes as required.
• It can handle numerous users across nodes due to its unique architecture.
• It features significant horizontal scalability and failure tolerance.

Rapidminer

This software is primarily used for analysis. It provides predictive analysis, machine learning models, deep learning and graphic UI workflows. It is used for advanced big data analytics. It uses a core java framework. It can work across on-premise as well as cloud-based servers.

Features and Advantages:

• It features an easy to use graphic interface. This makes the programming models much easier to design.
• It can manipulate data, create predictive models, machine learning and statistical models and generate reports
• This software can be used for drawing inferences from the analysis. It is therefore useful in planning strategies as well.
• It integrates seamlessly with the Cloud and API ecosystems and thus has many areas of applications.
• It has exceptional customer service.

Apache CouchDB

It is primarily a data storage tool. It stores files in the JSON format which are easy to translate across different platforms as necessary. It is written in Erlang, a language oriented towards concurrency. It runs a single node database across multiple servers.

Features and Advantages:

• It is easy to manipulate data on CouchDB. The deletion, addition, and updation of the database are simple.
• It can be accessed by multiple servers, which improves collaboration.
• It is relatively fault-tolerant.

HPCC

This big data solution is the chief competitor to Hadoop. It delivers on a single platform only, which makes it easy to manage. It is written in C++ but also uses ECL. It supports Thor and Roxie architectures for batch and real-time analyses respectively.

Features and Advantages:

• It provides parallel processing over systems, pipelines, and data that ensures high performance.
• It is very efficient and highly scalable.
• The graphical UI simplifies the development and testing processes.
• It ensures continuous availability of data.
• It can include and operate all C++ libraries.

Apache SAMOA

SAMOA stands for Scalable Advanced Massive Online Analysis. It allows distributed streaming of algorithms for data mining and streaming purposes. It also facilitates the creation of statistical and machine learning models. It runs over multiple distributed stream processing engines or DSPEs.

Features and Advantages:

• It features a Write Once Run Anywhere (WORA) architecture. This means that you need to program it only once and it can be plugged wherever needed.
• It is extremely efficient and scalable.
• The architecture does not necessitate downtimes, backups, or cycles. This makes this tool efficient and affordable.

Neo4J

This big data analysis tool covers what Hadoop lacks: graphic analyses of data. The data is stored in the key-value pattern, necessary for graph analysis. This tool is mostly used by digital marketing firms and social media marketers. It uses interconnected nodal relations to structure its graph database.

Features and Advantages:

• It uses a language known as Cypher for graph queries.
• It can integrate with any database as it needs no specific data type or schemas.
• It supports ACID transactions.
• It ensures data availability.
• It maintains scalability of the data.

QuBole

The QuBole platform is an automated, self-optimizing tool that learns according to usage. This means the resources expended in managing the platform can be saved giving huge advantages to businesses.

Features and Advantages:

• It is a single platform and is easy to operate.
• It provides in-depth reports, suggestions, and optimizations to the current processes.
• Automatically revises and updates protocols to minimize manual operations.
• It can be used to perform advanced analysis of big data.

Conclusion

There are a large number of big data storage and analysis tools available in the market. However. One must carefully analyze the requirements before selecting one. Although Hadoop introduces the systems to most of the services, it is not the only option available. This take on the available tools will surely help you finalize one for your requirements.