Smart Insights has put together a list of free big data analytics tools. The list includes analytic software that provides best in class performance for handling large amounts of data, in the form of structured, semi-structured or unstructured. We selected our top ten big data tools based on functionality, usability, performance, accessibility and cost.
Here are the best!
Microsoft Azure
Microsoft Azure, formerly known as Windows Azure, is a public cloud computing platform handled by Microsoft. It provides a range of services that include computing, analytics, storage, and networking.
Windows Azure provides big data cloud offerings in two categories, Standard and Premium. It provides an enterprise-scale cluster for the organization so that they can run their big data workloads.
Microsoft Azure offers reliable analytics with an industry-leading SLA, and enterprise-grade security and monitoring. It is also considered a high-productivity platform for developers and data scientists.
The platform aims to offer information in real-time in a way that is easy to manage even when used on the most advanced applications.
There’s no necessity of creating and allocating fresh IT infrastructure or virtual servers for the processing. Rather, generally adopted SQL queries can be used for extracting basic information, while other programming languages like JavaScript and C# can be added for more complex operations.
Zoho Analytics
Zoho Analytics is a BI and Data analytics software platform that helps its users to visually analyze data, create visualizations, and get a better and in-depth understanding of raw data.
It allows its users to integrate multiple data sources that may include business applications, databases, cloud drives, and more. It helps users generate dynamic, highly customizable, and actionable reports.
Zoho Analytics is a user-friendly platform that makes it easy to upload and control data. Also, it enables the easy creation of multifaceted and custom dashboards. The software platform is easy to deploy and implement.
The platform of Zoho Analytics can be accessed widely, be it via the data pros in the C suite to the sales reps that require data analytics trend lines for their operations.
Zoho Analytics also enables the users to generate a comment threat in the app, for facilitating collaboration between staffers and teams. The platform is an effective choice for businesses that are required to offer convenient, accessible data analytics insight to staffers at every level.
R-Programming
R-Programming is a domain-specific programming language specifically designed for statistical analysis, scientific computing, and data visualization using R Programming. Ross Ihaka and Robert Gentleman developed it in 1993.
It is among the top big data analytics tools because R-Programming software helps data scientists to create statistics engines that can provide better and precise insights due to relevant and accurate data collection.
The tools exhibit some features that are:
- Effective data handling and storage facility
- It provides tenacious and integrated tools for data analysis
- Allows you to create statistic engines rather than opting for a pre-made approach
- R integrated with its sister language Python gives faster, up-to-date, and accurate analytics
- R produces plots and graphics that are ready for publication
Altamira LUMIFY
Lumify is a big data fusion, analysis, and visualization platform. Like all big data analytics tools, it too enables you to understand connections and explore the relationship between your data.
Lumify is considered as a good big data analytics tool because it facilitates its users to get a set of analytics options that include graph visualizations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces that can be shared in real-time.
Lumify offers both 2D and 3D graph visualizations with automatic layouts. It also provides a plethora of options to analyze the links between different entities in a graph.
Lumify comes with specific ingest processing and interface elements for textual content, images, and videos. The platform allows you to organize your work in different workspaces.
The platform is built on proven,scalable big data technologies. It is secure, scalable, and backed by a motivated full-time development team.
Apache Hadoop
Apache Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Doug Cutting and Mike Cafarella worked together to come up with Hadoop in 2005. It was originally designed to distribute for the Nutch search engine project which was an open-source web crawler created in 2002.
Apache Hadoop is a framework that consists of a software ecosystem. Hadoop Distributed File System or HDFS and MapReduce are the two primary components of Hadoop.
The software produces a distributed storage framework and uses the MapReduce programming model for the processing of big data.
Hadoop possesses a great ability to store and distribute big data sets across hundreds of inexpensive servers and hence is considered as a top big data analytics tool. Its users can even the size of the cluster by adding new nodes as per their requirements too without any downtime.
MongoDB
MongoDB is a document-oriented NoSQL database used to store high volumes of data. MongoDB is well-known for its robustness and this makes MongoDB different from Hadoop.
Unlike traditional rotational databases, MongoDB makes use of collections and documents rather than using rows and columns. These documents consist of key-value pairs which are considered as the basic unit of data in MongoDB.
Xplenty
Xplenty is a cloud-based ETL solution that provides simple visualized data pipelines. These pipelines allow data to flow automatically across sources and destinations.
Xplenty has powerful on-platform transformation tools that allow you to clean, normalize, and transform data whilst adhering to compliance best practices.
The platform exhibits some features that make it a user-friendly platform:
- Easy Data transformations
- Simple workflow creation to define dependencies between tasks
- REST API for connecting to any data source
- Salesforce to Salesforce integrations
- Cutting-edge data security and compliances
- Diverse data source and data destination options.
Azure Data Lake Analytics – Best pay-per-job big data solution
Azure Data Lake Analytics is an on-demand analytics job service that prices per-job, ensuring that you only pay for the processing as you use it. This tool can process petabytes of data for business intelligence (BI) as well as sentiment analysis. You’re left with high-impact visualizations of your relational source data, such as Azure SQL Database and Azure Synapse Analytics.
Azure Data Lake Analytics costs from $1 per 1,000 runs or $0.25/DIU-hour and scales according to your use case.
Pros:
- Simple solution for batch workloads
- Complimentary storage of relational database and NoSQL
- Works well with power BI services for reporting
- Only pay for consumed ADLUs
Cons:
- Lack of streaming option and event processing
- May be confusing for users coming from a primarily MSBI background
- Lacks resources for end-user training
IBM Cloud Pak for Data – Best for reducing ETL requests
IBM Cloud Pak for Data is a fully-integrated, cloud native, data and AI platform designed for sophisticated DataOps and business analytics solutions. IBM boasts a potential for a 25-65% reduction in extract, transform, load (ETL) requests by eliminating the complexities of data integration of different data types and structures using Cloud Pak for Data. You can customize your workflow using their flexible API and complimentary proprietary and third-party services.
IBM Cloud Pak for Data costs from $800/month and offers a 7-day free trial.
Pros:
- Award-winning data security solutions
- Good for optimizing storage and other maintenance of preexisting data
- Tailored solutions for lessening your ETL request load
Cons:
- Could use more options to better migrate data from other cloud providers to IBM
- May be cost prohibitive for smaller enterprises
- Their Db2 database has a bit of a clunky, old-fashioned feel
Splunk – Best for user behavior analytics
Splunk is currently used by 91 of the Fortune 100 companies, including Intel, Comcast, and Coca-Cola. Splunk offers machine learning-centric visibility and detection of entity profiling and scoring, risk behavior detection, anomaly observation, and high fidelity behavior-based alerts. You can access a free cloud-based sandbox trial of Splunk UBA to check it out before committing. They offer dedicated solutions to DevOps, Security, IT, and big data.
Splunk costs from $2000/year for 1 GB/day and offers a free plan that allows you to index only 500 MB/day.
Pros:
- Flexible data and report sharing using URL links
- Quick log queries across different types of infrastructure
- Search queries can be saved for repeat use or converted into apps
- Can set up detailed, specific alerts for various KPIs
Cons:
- Infrastructure maintenance requires more manpower than some competitors
- Query builder may be prohibitive for non-technical users
- Steep learning curve compared to others
SAS Visual Analytics – Best big data analytics tool with smart visualizations
With SAS Visual Analytics, users are able to easily import data from databases, Hadoop, Excel spreadsheets, and social media. They offer a huge variety of interactive visualizations, including bar and pie charts, heat maps, animated bubble charts, vector maps, numeric series, tree maps, network diagrams, correlation matrix, forecasting, decision trees, and more. Plus they have ease-of-use options like one-click filtering and automated content linking.
SAS Visual Analytics costs from $8000/year and offers a free 14-day trial.
Pros:
- Flexible drag-and-drop analytics elements
- Well-suited to support high volume of simultaneous users
- Works with tens of millions of records without lagging
- Quality BI dashboards can be accessed across many devices
Cons:
- Low number of connection options with third-party apps
- Could use better HTML5 support
- May be price prohibitive compared to others on this list
Tableau – Best big data analytics tool for ease of use
Tableau is a user-friendly, intuitive visual analytics platform with built-in best practices for data exploration and informational storytelling. Users can access their full suite of self-service prep and analytics tools with a minimal learning curve, leveraging drag-and-drop visualizations and easy point-and-click AI-driven statistical modeling. Most users should be able to assemble data to their liking without advanced programming or special commands.
Tableau costs from $70/user/month and offers a free 14-day trial.
Pros:
- Good native integration with Salesforce CRM
- Comes with robust mobile app for iOS and Android
- Offers a hearty variety of chart types (Sankey, Doughnut, Maps)
- Easy to use with self-learning module available
Cons:
- Some data manipulation required in order to successfully match queries
- Limited room for columns when assembling worksheets
- Frequently requires saved database connections to be re-authenticated
Conclusion
Analytics software is a small part of big data analytics tools. Analytics helps in collecting, analyzing and interpreting the available data. Today, a majority of businesses use different forms of analytics. Recently, there has been a surge in the number of tools being used for data analysis. From enterprise-level solutions to open source tools, these have been built with various platforms and languages.