These top 10 big data analytics tools promote efficiency during tough working conditions

Presently with the rising pace of technology, the demand to track data is increasing rapidly. As we are aware, data is everything in today's IT world and this data keeps multiplying by manifolds each day. Previously, we were talking about kilobytes and megabytes. But nowadays, we are talking about terabytes. Nowadays, almost 2.5quintillion bytes of data are generated globally and it is of no use until that data is segregated adequately. It has become critical for businesses to maintain consistency in the business by collecting useful and meaningful data from the market today and for that, all it requires is the selection of the right big data analytic tool and a professional data analyst to segregate a large amount of raw data using which a company can take the right approach. In remote working environments, it fuels the capacity of employees in the process of collecting, examining, and analyzing large amounts of data to discover market trends. Big data analytics comprises huge amounts of structured and unstructured data, which can offer important insights when analytics are applied. Here the article lists the top 10 big data analytics tools to enhance efficiency in remote working environments.

Dataddo: Dataddo is a no-coding, cloud-based data analytics tool that offers flexibility first – with a large range of connectors and the facility to choose your metrics and attributes. It makes creating stable data pipelines simple and fast.

Pros:

• Favourable for non-technical users with an uncomplicated user interface
• Can deploy data pipelines within minutes of account creation
• Flexible plugs into users' existing data stack
• New connectors can be added within 10 days from the request

Apache Hadoop: Apache Hadoop is a software framework process for clustered file systems and operating big data. It operates datasets of big data through the MapReduce programming model. It is an open-source framework that is written in Java and it offers cross-platform support. Apache Hadoop is the topmost used big data tool. Over half of the Fortune 50 companies use Hadoop. Some of the Big tech names include Amazon Web services, Hortonworks, IBM, Intel, Microsoft, Facebook, etc.

Pros:

• The key strength of Hadoop is its HDFS (Hadoop Distributed File System) which is capable to hold all types of data – video, images, JSON, XML, and plain text over the same file system
• Highly recommended for R&D purposes
• Offers fast access to data
• Highly scalable

CDH (Cloudera Distribution for Hadoop): CDH is one of the most important big data analytics tools which focuses on enterprise-class deployments of the technology. It is open source and has a free platform distribution that encompasses Apache Hadoop, Apache Spark, Apache Impala, and many more. It permits you to collect, process, administer, manage, discover, model, and distribute unlimited data.

Pros:

• It has a comprehensive distribution
• Cloudera Manager administers the Hadoop cluster very well.
• It has easy implementation.
• High security and governance

Cassandra: Apache Cassandra is a free and open-source distributed NoSQL DBMS developed to manage large volumes of data spread across numerous commodity servers, delivering high availability. It engages CQL (Cassandra Query Language) to interact with the database. Many high-profile companies like Accenture, American Express, Facebook, General Electric, Honeywell, Yahoo, etc., use Cassandra.

Pros:

• No single point of failure
• Capacity to handle massive data very quickly
• Log-structured storage
• Automated replication
• Linear scalability

Knime: KNIME stands for Konstanz Information Miner and it is an open-source data analytics tool that is applied for Enterprise reporting, integration, research, CRM, data mining, data analytics, text mining, and business intelligence. It supports Linux, OS X, and Windows operating systems. It is considered a good alternative to SAS. Many top companies using Knime include Comcast, Johnson & Johnson, Canadian Tire, etc.

Pros:

• Simple ETL operations
• Integrates very well with other technologies and languages
• Rich algorithm set
• Automates a lot of manual work
• No stability issues
• Easy to set up

Datawrapper: Datawrapper is also an open-source big data analytics tool for data visualization that helps its users to generate simple, precise, and embeddable charts quickly. Its major customers are newsrooms that are spread all over the world. Some of the names include The Times, Fortune, Mother Jones, Bloomberg, Twitter, etc.

Pros:

• Device friendly. Works very well on all types of devices – mobile, tablet, or desktop
• Fully responsive
• Fast
• Interactive
• Brings all the charts to one place
• Great customization and export options
• Requires zero coding

MongoDB: MongoDB is a NoSQL, document-oriented database written in C, C++, and JavaScript. It is a free open-source tool that employs multiple operating systems which includes Windows Vista (and later versions), OS X (10.7 and later versions), Linux, Solaris, and FreeBSD. Its primary features are Aggregation, Adhoc-queries, Uses BSON format, Sharding, Indexing, Replication, Server-side execution of JavaScript, Schemaless, Capped collection, MongoDB management service (MMS), load balancing, and file storage. Customers using the MongoDB platform include Facebook, eBay, MetLife, Google, etc.

Pros:

• Easy to learn
• Provides support for multiple technologies and platforms
• No hiccups in installation and maintenance
• Reliable and low cost

Lumify: Lumify is a free-of-cost open-source tool for big data fusion/integration, analytics, and visualization. Its main features are full-text search, 2D and 3D graph visualizations, automatic layouts, link analysis between graph entities, integration with mapping systems, geospatial analysis, multimedia analysis, and real-time collaboration through a set of projects or workspaces.

Pros:

• Scalable
• Secure
• Supported by a dedicated full-time development team
• Supports the cloud-based environment. Works well with Amazon's AWS

Storm: Apache Storm is a free and open-source distributed real-time computation system. Apache Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. The developers of the storm aid Backtype and Twitter. It is written in Clojure and Java. Among many, Groupon, Yahoo, Alibaba, and The Weather Channel are some famous companies that use Apache Storm.

Pros:

• Reliable at scale
• Very fast and fault-tolerant
• Guarantees the processing of data
• It has multiple use cases – real-time analytics, log processing, ETL (Extract-Transform-Load), continuous computation, distributed RPC, and machine learning

Rapidminer: Rapidminer is a cross-platform tool that allows an integrated environment for data science, machine learning, and predictive analytics. It comes under various licenses that provide small, medium, and large proprietary editions as well as a free edition that allows for 1 logical processor and up to 10,000 data rows. Companies like Hitachi, BMW, Samsung, Airbus, etc have been using RapidMiner.

Pros:

• Open-source Java core
• The convenience of front-line data science tools and algorithms
• The facility of code-optional GUI
• Integrates well with APIs and cloud
• Superb customer service and technical support