Big Data Analytics Tools and Technologies with key Features

With the rise in the volume of Big Data and tremendous growth in cloud computing, the cutting edge Big Data Analytics Tools have become the key to achieve a meaningful analysis of data. In this article, we shall discuss the top Big Data Analytics tools and their key features.

• Apache Storm
• Talend
• CouchDB
• Apache Spark
• Splice Machine
• Plotly
• Azure HDInsight
• R
• Skytree
• Lumify
• Apache Hadoop
• Qubole

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Big Data Analytics Tools List

Apache Storm: Apache Storm is an open-source and free big data computation system. Apache Storm also an Apache product with a real-time framework for data stream processing for the supports any programming language. It offers distributed real-time, fault-tolerant processing system. With real-time computation capabilities. Storm scheduler manages workload with multiple nodes with reference to topology configuration and works well with The Hadoop Distributed File System (HDFS).

Features:

• It is benchmarked as processing one million 100 byte messages per second per node
• Storm assure for unit of data will be processed at minimum once.
• Great horizontal scalability
• Built-in fault-tolerance
• Auto-restart on crashes
• Clojure-written
• Works with Direct Acyclic Graph(DAG) topology
• Output files are in JSON format
• It has multiple use cases – real-time analytics, log processing, ETL, continuous computation, distributed RPC, machine learning.

Talend: Talend is a big data tool that simplifies and automates big data integration. Its graphical wizard generates native code. It also allows big data integration, master data management and checks data quality.

Features:

• Streamlines ETL and ELT for Big data.
• Accomplish the speed and scale of spark.
• Accelerates your move to real-time.
• Handles multiple data sources.
• Provides numerous connectors under one roof, which in turn will allow you to customize the solution as per your need.
• Talend Big Data Platform simplifies using MapReduce and Spark by generating native code
• Smarter data quality with machine learning and natural language processing
• Agile DevOps to speed up big data projects
• Streamline all the DevOps processes

Apache CouchDB: It is an open-source, cross-platform, document-oriented NoSQL database that aims at ease of use and holding a scalable architecture. It is written in concurrency-oriented language Erlang. Couch DB stores data in JSON documents that can be accessed web or query using JavaScript. It offers distributed scaling with fault-tolerant storage. It allows accessing data by defining the Couch Replication Protocol.

Features:

• CouchDB is a single-node database that works like any other database
• It allows running a single logical database server on any number of servers
• It makes use of the ubiquitous HTTP protocol and JSON data format
• document insertion, updates, retrieval, and deletion is quite easy
• JavaScript Object Notation (JSON) format can be translatable across different languages

Apache Spark: Spark is also a very popular and open-source big data Software tool. Spark has over 80 high-level operators for making easy build parallel apps. It is used at a wide range of organizations to process large datasets.

Features:

• It helps to run an application in Hadoop cluster, up to 100 times faster in memory, and ten times faster on disk
• It offers lighting Fast Processing
• Support for Sophisticated Analytics
• Ability to Integrate with Hadoop and existing Hadoop Data
• It provides built-in APIs in Java, Scala, or Python
• Spark provides the in-memory data processing capabilities, which is way faster than disk processing leveraged by MapReduce. 
• In addition, Spark works with HDFS, OpenStack and Apache Cassandra, both in the cloud and on-prem, adding another layer of versatility to big data operations for your business.

Splice Machine: It is a big data analytics tool. Their architecture is portable across public clouds such as AWS, Azure, and Google.

Features:

• It can dynamically scale from a few to thousands of nodes to enable applications at every scale
• The Splice Machine optimizer automatically evaluates every query to the distributed HBase regions
• Reduce management, deploy faster, and reduce risk
• Consume fast streaming data, develop, test and deploy machine learning models

Plotly: Plotly is an analytics tool that lets users create charts and dashboards to share online.

Features:

• Easily turn any data into eye-catching and informative graphics
• It provides audited industries with fine-grained information on data provenance
• Plotly offers unlimited public file hosting through its free community plan

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Azure HDInsight: It is a Spark and Hadoop service in the cloud. It provides big data cloud offerings in two categories: Standard and Premium. It provides an enterprise-scale cluster for the organization to run their big data workloads. You can get a better understanding of the Azure Data Engineer certification.

Features:

• Reliable analytics with an industry-leading SLA
• It offers enterprise-grade security and monitoring
• Protect data assets and extend on-premises security and governance controls to the cloud
• A high-productivity platform for developers and scientists
• Integration with leading productivity applications
• Deploy Hadoop in the cloud without purchasing new hardware or paying other up-front costs

R: R is a programming language with free software and it’s Compute statistical and graphics. The R language is popular among statisticians and data miners for developing statistical software and data analysis. R Language provides a large number of statistical tests.

Features:

• R is mostly used along with JupyteR stack (Julia, Python, R) for enabling wide-scale statistical analysis and data visualization. Among the 4 widely used Big Data visualization tools, JupyteR is one of them, 9,000 plus CRAN (Comprehensive R Archive Network) algorithms and modules allow composing any analytical model running it in a convenient environment, adjusting it on the go and inspecting the analysis results at once. R language is having as following:
  • R can run inside the SQL server
  • R runs on both Windows and Linux servers
  • R supports Apache Hadoop and Spark
  • R is highly portable
  • R easily scales from a single test machine to vast Hadoop data lakes
• Effective data handling and storage facility,
• It provides a suite of operators for calculations on arrays, in particular, matrices, 
• It provides a coherent, integrated collection of big data tools for data analysis
• It provides graphical facilities for data analysis which display either on-screen or on hardcopy

Skytree: Skytree is a Big data tool that empowers data scientists to build more accurate models faster. It offers accurate predictive machine learning models that are easy to use.

Features:

• Highly Scalable Algorithms
• Artificial Intelligence for Data Scientists
• It allows data scientists to visualize and understand the logic behind ML decisions
• The easy to adopt GUI or programmatically in Java via. Skytree 
• Model Interpretability
• It is designed to solve robust predictive problems with data preparation capabilities
• Programmatic and GUI Access

Lumify: Lumify is considered a Visualization platform, big data fusion and Analysis tool. It helps users to discover connections and explore relationships in their data via a suite of analytic options.

Features:

• It provides both 2D and 3D graph visualizations with a variety of automatic layouts
• Link analysis between graph entities, integration with mapping systems, geospatial analysis, multimedia analysis, real-time collaboration through a set of projects or workspaces.
• It comes with specific ingest processing and interface elements for textual content, images, and videos
• It spaces feature allows you to organize work into a set of projects, or workspaces
• It is built on proven, scalable big data technologies
• Supports the cloud-based environment. Works well with Amazon’s AWS.

Hadoop: The long-standing champion in the field of Big Data processing, well-known for its capabilities for huge-scale data processing. It has low hardware requirement due to open-source Big Data framework can run on-prem or in the cloud. The main Hadoop benefits and features are as follows:

• Hadoop Distributed File System, oriented at working with huge-scale bandwidth – (HDFS) 
• A highly configurable model for Big Data processing – (MapReduce)
• A resource scheduler for Hadoop resource management – (YARN)
• The needed glue for enabling third-party modules to work with Hadoop – (Hadoop Libraries)

It is designed to scale up from Apache Hadoop is a software framework employed for clustered file system and handling of big data. It processes datasets of big data utilizing the MapReduce programming model. Hadoop is an open-source framework that is written in Java and it provides cross-platform support. No doubt, this is the topmost big data tool. Over half of the Fortune 50 companies use Hadoop. Some of the Big names include Amazon Web services, Hortonworks, IBM, Intel, Microsoft, Facebook, etc. single servers to thousands of machines. You can get a better understanding with the Data Engineering Course in India.

Features:.

• Authentication improvements when using HTTP proxy server
• Specification for Hadoop Compatible File system effort
• Support for POSIX-style file system extended attributes
• It offers a robust ecosystem that is well suited to meet the analytical needs of a developer
• It brings Flexibility In Data Processing
• It allows for faster data Processing

Qubole: Qubole data service is an independent and all-inclusive big data platform that manages, learns and optimizes on its own from your usage. This lets the data team concentrate on business outcomes instead of managing the platform. Out of the many, few famous names that use Qubole include Warner music group, Adobe, and Gannett. The closest competitor to Qubole is Revulytics.

With this, we come to an end of this article. I hope I have thrown some light on to your knowledge on Big Data tools and Technologies.

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