Best Big Data Analytics Tools

SelectHub is real people helping you find the top big data analytics tools for your unique business needs while recognizing the true software leaders who help make your decisions possible.

Big Data Analytics Tools Buyer's Guide

Last updated on October 19th, 2020

Owning Big Data Is All About Using The Best Big Data Tools

The truth is that in today’s business landscape, solid ideas and hard work aren’t going to save your business from failure. In an economy ruled by rapid change and trends, the winner is often the one with the best tools. Big data tools are no different in this aspect — they are the line between the data-rich and the data-deprived. With software handling literally 2.5 quintillion bytes of data a day, your business can’t afford to avoid diving into the realm of big data. If you’re looking for more information or already have your toes dipped in, then you’ve come to the right place.

Executive Summary

  • Big data analytics tools can run the gamut from simple(r) and user-friendly to requiring a data scientist to interpret.
  • A strong list of essential core-features you need versus the features you want will help make your decision process even easier.
  • Can significantly augment your business needs, show you where to improve upon, where you’re excelling and what to do next.
  • Big data analytics software can provide a robust set of advanced features and installation types.

Benefits of Big Data Software and What It Does

Improve the Decision-Making Process

Big data analytics software is designed to ingest thousands of gigabytes — all kinds of it. From customer interaction metrics to web hits to sales figures and more. All the buzzwords and hype you’ve heard around the words “big data”? This is where it all happens.

Digest Large Amounts of Data

Naturally, the benefits for big data software are numerous, but none are as important as the actual processing of large batches of data. In an economy now ruled by business analytics and big data, the value of a good piece of software that can process in bulk cannot be understated.

Players like Oracle deliver on simplifying user interaction and crunching what could be mountains of data. Like most other systems, they also provide a visual interface to Hadoop.

Hadoop Visualization

Hadoop is an open source software library that “allows for the distributed processing of large data sets across clusters of computers using simple programming models.” Hadoop is considered necessary for mining helpful insights out of large sets of data, so buyers would do well to make sure their select software has it. And while Hadoop might be an essential feature that Oracle, IBM and SAP build their product around, it’s not nearly as important as reporting features.

Contextualize

There’s a common piece of knowledge in the world of big data: Without context, it's meaningless. And it’s true. Without a meaningful way to communicate what you’ve gathered, you’re simply wasting time and money throwing numbers and figures at people. With the revenue from big data set to grow to $260 billion in 2022, according to the International Data Corporation, not properly contextualizing information reporting features is a critical mistake that could cost your business.

So make sure that sophisticated, Hadoop enabled big data analysis tools is going to let you visually see and drill-down into your insights, and then share those insights in a meaningful way with other key players.

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Implementation Goals

Goal 1

Improve Business Performance With Greater Insight

Big data analytics offers a unique chance to get an objective look at your company’s performance. By seeing your key performance indicators (KPIs) laid out in front of you with hard data to back them up, you’re afforded a chance to optimize your business. See where you’re underperforming and then find out why. See where you’re excelling and then double-down on your efforts.

Among other advantages, you can assess supply and demand concerns, and the correlate concerns with internal friction. For example, if you’re not meeting your key performance indicators and you notice your data is showing key supply shortages, you can adjust accordingly and squash errors.

Goal 2

Better Understand and Serve Customers

As data becomes more descriptive and robust, big data analytics software is giving people better and better ways to understand and serve their customers. Imagine being able to not just see how your customers are interacting with your business, but be able to solve their problems before they happen. And then imagine taking that insight and turning it into a reason that customers become repeat customers.

With big data analytics solutions, it’s possible to get the intel you need to quickly identify problems with the customer experience. Then you can focus on delighting your buyers by catering to their wants and their needs. The best big data software builds descriptive customer models for you. They provide insight into behavior, patterns wants and needs. Sophisticated customer profiles can augment different components of your business, such as marketing or sales. Arming employees with knowledge will make them more precise in their work.

Goal 3

Become More Certain in Your Decision Making

Big data analytics is as much marketing software as it is an internal administrative tool. When you need to decide where and how to spend a budget, by utilizing your gathered intel, you can augment the decision-making process with objective data. Same goes for ordering inventory, making new hires or cutting staff.

Naturally, this prompts the need for even greater accuracy.

Big data analytics lives and dies by the accuracy of the systems. They integrate countless quality management features, models and algorithms to clean and prepare data so that it’s both accurate and understandable. If your decision makers are basing their decisions off of poor data, big data might do more harm to the company than good.

Goal 4

Empower Employees

Modern workers are technology-natives. They’ve been raised around technology and see it as an essential part of their daily lives, especially in their work-lives. There are very few places and employee levels that don’t benefit from having access to big data. Business leaders get a high-level overview of their business, mid-level managers get to see and understand the key intel on their specific niche and lower level employees are going to have more specific data to empower their productivity and decisions. When evaluating software, keeping implementation goals in mind helps users decide which features are required to accomplish those goals. Constructing a requirements list will provide a framework for the kind of BI software your business needs.

 

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Basic Features & Functionality

Vendors are always scrambling to include the latest and greatest software features that can set them (and you) apart from the competition. And while having the newest features can be exciting and perhaps even beneficial to your business, consider focusing on these core features first before you start looking for shiny bells and whistles:

Robust Reporting

Reporting is the bridge between the abstract world of data and actionable insights. Good reporting features when dealing with large subsets of data are essential. They visually show us the relationships between abstract figures and aspects of a business, either in the past, present or future.

Big data products with powerful reporting functions also empower users with sharing abilities, so no one person is the sole messenger in any given situation. With good reporting software, you can generate beautiful visual reports on the fly and instantly ship them off company-wide.

Data Warehousing, Storage and Retrieval

We produce 2.5 quintillion bytes of data a day, and it’s all got to go somewhere. Warehousing is the process of storing data in large quantities and processing it. Big data analytics solutions are going to be centered around their warehousing abilities, whether they be cloud-based or on-premise software. Both have a number of trade-offs and advantages, chiefly that with a cloud-based big data warehouse, you don’t need to manage it all yourself. It’s stored on someone else’s system, as opposed to on-premise.

Support For Different Types of Analytics

Contrary to popular assumptions, there are multiple types of analytics that are applicable. You’ve got business intelligence which is any kind of knowledge that’s applicable to the operation of a business. There’s also predictive analytics, which takes data from the past and present, runs it through sophisticated data models, and attempts to make a prediction based on what it knows.

Alongside predictive analytics is prescriptive analytics — commonly referred to as “the final frontier." Unlike predictive analytics, prescriptive analytics attempts to make decisions based on predictions, much in the same way an artificial intelligence (AI) might make a decision. Real-time analytics isn’t as future-oriented. Real-time is focused on what’s happening in the now and shows intel as it's happening combined with what has already happened.

Finally, there’s machine learning. Big data software is becoming increasingly advanced — so advanced that it’s learning on its own based off prior knowledge. Machine learning analytics is capable of learning on its own by feeding off your data, making predictions based on that information, and notifying users of suggestions or decisions it made. It’s not full auto-pilot yet, but it’s a strong step forward.

Support The Full Spectrum of Data Types, Protocols and Integrations

A large amount of a data scientist’s time is spent cleaning, preparing and organizing data for different applications and APIs. When you’re picking your big data tool, make sure it supports the full spectrum of data types, analytics types and integrates well into other applications. A solid reporting API is helpful to have for your more tech-inclined users.

Version Control

In a large scale operation, several individuals may be apart of analyzing or experimenting with data. There’s a lot of tiny moving parts, and someone can change a data set on the fly. Version control offers users the chance to revert their old set back to a certain point, or even keep separate versions to experiment on, without polluting or diluting the primary set.

Data Governance

Data governance is an essential tool for any big data software. It gives you more granular control and allows you to closely follow privacy regulations like the GDPR. Some big data systems even allow you to pseudonymize right in the software, making your agency more privacy-friendly and regulation compliant.

R Integration

R is the programming language of statisticians and data scientists. It allows capable users to run complex and original algorithms. By having R in your big data software, you give yourself greater flexibility and potential when handling intel. In addition to being able to perform more complex functions, you also make scaling up your business and software much easier.

Ease of Use

Not everyone is a data scientist, and hiring one isn’t cheap. If a piece of software toes the line between complex functionality and ease-of-use, it’s a winner. This will help empower your employees, save you money, and increase and quality of your reports. It also gives you the flexibility you need to go from simple to complex analysis.

 

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Additional Features to Enhance Your Strategy

At some point, the core features mentioned above won’t be enough. Eventually, your business will outgrow its basic software needs and will require something a lot more specialized. This is where you should start considering additional/advanced features.

Cloud-Based

Sounds like every buzzword ever, right? Well, not entirely. Cloud analytics is an up-and-coming hosting solution. It offers maintenance-free storage, algorithms and application processing, while usually being cheaper than paying thousands to own and manage on-premise software. The downside is that they’re reliant on a good internet connection, less customizable and ultimately you’re dependent on the developers to provide the features that you might need.

CRM Integration

Enrich your CRM data. Software like Alteryx can combine your customer relationship management data with your big data analytics to produce clearer customer insights and profiles.

Augmented Analytics

Imagine automating your insights using machine learning and natural language generation. Imagine automated warnings and responses when there are notable changes, all without ever having to tell the software what to do. That’s what augmented analytics is. It can be a serious boon to your enterprise, but it might not be something you need or even want, depending on your agency's size and needs.

 

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Software Tools Comparison

Your software search is going to take you all over the place, from vendor to software to vendor again. You’re going to have to sift through a veritable pile of software and features in order to find the right one for your business. There’s a lot of tiny moving parts involved, and each business has its own wants and needs that will steer it towards one piece of software or another.

To simplify this process, come into the search with your business plan set up and in place, as well as having answered questions like, “How can we use big data analytics in our business specifically?” Our in-depth requirements guide can help you decide what the most meaningful requirements are to your business, and can make this process a lot easier. And then when you’ve got your requirements, check out our software comparison to compare vendors and different big data tools.

To find vendors in your pricing range, give us a call, or check out our pricing guide.

Questions to Ask a Vendor

Selecting a big data solution is a unique process for each organization, and what works best for one business may be totally wrong for another. To make matters worse, there are countless big data vendors on the market, each offering different software. Fortunately, SelectHub is here to help. To get a big data tools comparison in the context of your organization, ask yourself the questions below:

Can this software be implemented successfully and within budget?

When shopping for big data software, interface directly with the vendor and ask for a quote. Account for additional costs on top of the initial quote.

Do you recommend cloud-based software for our business or something hosted on-premise? Or a mix of the two?

There are two roughly defined camps: Cloud-based and on-premise (hybrid being a mix of the two). Both have their tradeoffs, which we covered here.

What internal IT resources will we have to commit to this software (roles, responsibilities, infrastructure, etc.)?

Depending on your hosting solution (which we covered above), you may need to commit some IT resources to manage your big data analytics solution. On-premise software typically requires more in-house resources.

Is this system going to allow us to be GDPR compliant?

With Google recently being fined $57 million by the European Union for privacy violations, your business can’t afford (literally) to not be compliant with regulations like the GDPR.

Will this system help us meet our metrics and KPIs?

Your key performance indicators are what define the success or failure of your implementation. Though you might not know for sure until you have the software in your hands, based on your feature needs and requests, you can get a decent picture of how the software is going to meet your success metrics.

Is training available to any level employee, or is it specific to IT professionals or business analysts?

Sometimes you get more than you paid for — and not in a good way. If you’re opting for a complex piece of software, then you should ask about training.

What support and training will we receive after the installation?

Most vendors offer six months to a year’s worth of support after installation. Find out what your preferred vendor offers and any additional fees you may have to pay as a result.

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Questions About Vendor Considerations

The prestige, experience and viability of a vendor should play a major role in your decision-making process, alongside the capabilities of their software. Find out:

What sort of track record does this vendor have with other customers and strategic partners? Are any of them willing to speak on behalf of the vendor as references?

A vendor can have the best marketing in the world, but if every one of their clients walks away dissatisfied, you should probably look elsewhere. Do some scanning through social media tags mentioning the vendor, read reviews (such as the ones on our vendors page) and ask your business partners what they’d recommend.

How active is the vendor in developing their product or fixing bugs?

Having a good track record for fixing bugs and implementing new features should be a definite positive in your list of features. Developer support is a real and valid need in an ever-growing market such as this one.

What implementation methodology does the vendor use and how successful is it in delivering the software on time and within the set budget?

The first part of this question (implementation methodology) is primarily the concern of on-premise software. Cloud-based software will ordinarily have little-to-no installation. But with both of them, you should consider your own budget and what the product can offer. Seldom is it a good idea to go over budget for software — unless you’re absolutely certain about its usefulness.

Does the software integrate data from multiple departments?

When you can combine information from different areas of the business, you can analyze factors like how the latest marketing campaign affected sales and whether you had enough inventory to support that growth. This can also help you forecast growth and tailor protocol to the way you predict the company to operate in the future.

How can the software forecast growth?

Intuitive software help companies make business decisions such as new software implementation timelines or office expansion. Amazon uses technology like this to recommend additional products to its customers. If you can predict that customers who purchase toaster ovens will also be in the market for a stove within the next year, you can target your marketing campaigns accordingly.

Will it grow with my business?

If you’re investing in a big data solution, you don’t want it to be obsolete in a year. Plan to implement software that will last at least ten years. BI should be scalable. When ranking your big data tools, find out what the limitations are on data capacity so you can make an adequate decision about whether the software is right for you.

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MATLAB

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MATLAB is a numerical computing and programming platform that enables users to develop and implement mathematical algorithms, create models and analyze data. Designed for engineers and scientists, it can be used for a range of purposes, including deep learning and machine learning, computational finance, image processing, predictive maintenance, IoT analytics and more. Built around its matrix-based programming language, it can help users run analyses on large data sets as well as design and rigorously test models.It is available through on-premise installation on Windows and Mac. For eligible licensees, there is also a SaaS version accessible through a web browser. Users can purchase it under a perpetual or annual license, with discounts for academic institutions. For individuals not associated with government agencies, private companies or other organizations, there is a less expensive home license for personal use. Students can purchase a student license for a version designed for coursework and academic research. Early-stage technology startups can apply for startup-friendly pricing and opportunities.

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Hadoop

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Apache Hadoop is an open source framework for dealing with large quantities of data. It’s considered a landmark group of products in the business intelligence and data analytics space, and is comprised of several different components. It functions on basic analytics principles like distributed computing, large data processing, machine learning and more. Hadoop is part of a growing family of free, open source software (FOSS) projects from the Apache Foundation, and works well in conjunction with other third-party products.

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Spark

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Apache Spark is an open source unified analytics software for distributed, rapid processing. It distributes data across clusters in real time to produce market-leading speeds. It is rising in popularity in the space, catching up to its sister-offering, Hadoop, because of its quicker speeds and specific focus on optimizing processing performance and ability to stream data. It supports several coding languages, including Python, R, Scala, SQL and Java. It can function stand-alone, or be integrated into broader workflows easily.

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Tableau Big Data Analytics

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Tableau Big Data Analytics is an analysis and reporting tool from Tableau. With this system, users can experience platform variety and utilize popular frameworks such as Apache Hadoop, Spark and NoSQL. Viewing and sorting is made simple as information is presented in one easy-to-use and digestible dashboard. Decision makers can also take advantage of the ability to see data from all sources, enriching their views.

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Board

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BOARD is a robust business intelligence solution that offers users analytical insights, business analytics and enterprise performance management all under the same hood. It helps key players of a company improve the effectiveness of their decision making. Its customizable and interactive dashboards give users the ability to see a high-level overview of their business, as well as drill down into their KPIs to assess business performance goals. It serves mid to large companies across various industries, and its programming-free toolkit empowers users of all skill levels to analyze and plan with a tailored, efficient approach.

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Domo

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Domo is a cloud-based business management suite that accelerates digital transformation for businesses of all sizes. It performs both micro and macro-level analysis to provide teams with in-depth insight into their business metrics as well as solve problems smarter and faster. It presents these analyses in interactive visualizations to make patterns obvious to users, facilitating the discovery of actionable insights. Through shared key performance indicators, users can overcome team silos and work together across departments.

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Cloudera

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Cloudera is a multi-environment analytics platform powered by integrated open source technologies that help users glean actionable business insights from their data, wherever it lives. With an enterprise data cloud, it puts data management at analysts’ fingertips, with the scalability and elasticity to manage any workload. It offers users transparency into the whole data lifecycle and the flexibility of customization through its open architecture. It is available on an annual subscription basis with three offerings: CDP Data Center, Enterprise Data Hub and HDP Enterprise Plus. Each edition offers different components and pricing varies based on computing power, storage space and number of nodes. The company merged with Hortonworks in 2019 to provide a comprehensive, end-to-end hybrid and multi-cloud offering.

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Alteryx

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The Alteryx platform is a suite of five products offering self-service statistical, predictive and spatial data analytics to achieve enterprise, financial and industrial intelligence. It allows users to create repeatable extract-transform-load workflows, with or without a programming language. Its scalable performance and deployment options enable analysis from the enterprise to big data levels.A drag-and-drop interface is the user’s gateway to high-speed analytics and modeling, supported by a community of model developers in the vendor’s customer base. Depending on which products a user opts for, it can perform end-to-end BI, from data harvesting from deep data pools to automated operationalizing.

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Oracle Big Data

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Interact with discovery and advanced analytics to explore and uncover hidden patterns and insights in big data with rich visualization at the speed of thought. Experiment, decide, and act with more accuracy and precision to improve decision making and business outcomes.

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Hortonworks

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Hortonworks Data Platform is an open-source data analysis and collection product from Hortonworks. It is designed to meet the needs of small, medium and large enterprises that are trying to take advantage of big data. The company was acquired by Cloudera in 2019 for $5.2 billion.HDP has a number of features that help it process large enterprise-level volumes, including multi-workload processing, batch processing, real-time processing, governance and more.

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SAP Big Data

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Deliver 360° insight across your enterprise with big data analytics from SAP. Allows you to analyze massive volumes of data at lightning speed – for the real-time business intelligence (BI) you need to make smarter, more profitable decisions.

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Ezoic

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Ezoic is an analytics-based advertisement testing and web optimization platform. It utilizes the concepts of big data and machine learning to learn how users engage with content and how to improve revenue.With deep revenue breakdowns, ad and layout testing, Google AMP converting and speed acceleration, it has several avenues for optimizing a site’s web presence and value. It links with more than 10,000 advertisement networks and is a partner with Google to maximize advertising options.

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Panoply

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Panoply is a fully-integrated data management platform that syncs, stores, organizes and analyzes data from many sources. It lets users use search query language to explore data, then analyze and visualize it through its robust integration capabilities.It combines data warehousing, AI-powered data processing and a variety of integrations to provide user-friendly data analysis infrastructure. Users can access it anywhere via the cloud.

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QlikView

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QlikView is a data discovery and customer insight software tool from Qlik, a leader in the insight and intelligence space. It’s built to offer self-service data that can help drive decisions and generate significant ROI for users of any technical skill level. It offers several features, including its “pre-canned” dashboards — dashboards that are preconfigured for data analysis and interpretation. It has been built from the ground up to be affordable, scalable and adaptable. It can ingest data from diverse sources like big data streams, file-based data, on-premise or cloud data. It is well-known for its data associations and relationship functionality, keeping data in-context automatically. It delivers results quickly, via its patented in-memory data processing feature, allowing data to be processed down to as little as 10% of its original size.

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RapidMiner

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The RapidMiner platform is a cloud-based series of data intelligence offerings, capable of all layers of a big data ecosystem. It can work with structured and unstructured data alike, preparing, blending, analyzing and visualizing it. It utilizes a code-free interface for designing big data workflows and integrations, capable of the complete data science life cycle. It can achieve top-level analytics like machine learning and predictive modeling. Its cloud deployment comes in managed or on-demand options. It has open-source and commercial versions.

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Spotfire

User Sentiment:
87%

TIBCO Spotfire is a complete business intelligence and data discovery platform that can perform various functions, including in-depth analysis and robust visual reporting, all powered by artificial intelligence. It offers data streaming technology, which can support insights with AI, big data integration, integration with the Internet of things (IoT) and more.

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Call SelectHub for a free 15-minute selection analysis: 1-855-850-3850

SAP HANA

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SAP HANA Platform's in-memory database technology, is a computer appliance which can operate as a platform for big data analytics and is considered by many to be a premier technology for high speed computing. It helps accelerate processing across flexible management options to support hot, warm, and cold data.

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MicroStrategy Analytics Express

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Microstrategy Analytics Express is an intelligence product that best serves small and medium-sized companies. Due to its ease of use, scalability and sophisticated analytics functions, the product is one of the more popular data solutions. The software brings a bevy of features that help it stand out from its competitors, including social intelligence, app integration, data discovery, mobile productivity and real-time telemetry. Users can easily explore with the software’s interactive reporting tools.

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BigQuery

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Google BigQuery handles large volumes of data and applies standard and sophisticated techniques to deliver actionable insights to users. It comes with a number of standard and unique features to help technical and non-technical users perform analysis, deliver reports, create dashboards and generate insights.

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Vertica

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HP Vertica is an analytics and data exploration platform that is designed to ingest massive quantities of data; parse it; and then return insights, reports, and interactive graphics.It is built to be deployed in the cloud or on-premise, and takes advantage of a number of unique features that help it stand out from its competitors. A serverless setup and advanced data trawling techniques help users store and access their data with ease.

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Kofax Insight

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Kapow Katalyst provides an agile and practical way to deliver big data integration and process automation. Catering to the needs of both IT and the line-of-business (LOB), Katalyst is a secure platform that enables you to extract web data, integrate applications or automate web-based manual business processes in just a few days - if not a few hours. Katalyst enables you to publish a Synthetic API to any web application or web portal, even when no API exists.

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BIRT

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This open-source reporting tool from OpenText offers data exploration, visualization and collaboration. It can embed into a range of both web and client applications, and it works especially well with Java and Java EE-based solutions.It aims to meet users’ business intelligence needs by facilitating report creation. The charting engine lets users embed fully-integrated reports and charts into the report designer or into other applications.

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KNIME

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The KNIME Analytics Platform is an open-source end-to-end data science solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the learning curve of data analytics, with more than 1,800 prebuilt default workflows for streamlined setup. It allows for data ingestion, preparing, cleansing, analyzing and visualizing. It can be scaled for deeper analytics through integrations, with sophisticated data modeling capabilities. It can be hosted on-premise or in the cloud through Microsoft Azure.

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GoodData

User Sentiment:
83%

This powerful analytics software is a SaaS platform that combines, analyzes and visualizes the internal and/or external data of an organization in order to monetize. It aims to help businesses change the way they make decisions with a focus on data-driven best practices. Provides an embeddable, customizable business analytics and intelligence that lets users process data, analyze trends and create visualizations that present information in an easily-digestible format. Users can interpret these visualizations to draw insights and make intelligent business decisions.

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Zoomdata

User Sentiment:
90%

Zoomdata is an analytics and reporting tool that allows users to explore and analyze large, complex datasets. It provides a simple, modern interface that makes data literacy attainable for users of all technical levels.It is designed to be scalable and embeddable through white labeling architecture. It is built on HTML5 and JavaScript, making it fully customizable. It aims to expedite the processes of data exploration, visualization and analysis to help users make data-driven decisions.

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Qlik Sense

User Sentiment:
83%

Qlik Sense is a self-service data analytics software tool that enhances human intuition with the power of artificial intelligence to help users make better data-driven business decisions. User-friendly in its approach, it allows users to explore their data and create visually appealing visualizations from their insights with drag-and-drop simplicity. As the next-generation advancement of QlikView, released in 2014, it expands analytical possibilities to support the entire insights life-cycle and helps users modernize their approach to intelligence. It has two editions: Business and Enterprise, offered on a per-user annual subscription basis. Users can choose between a hosted SaaS public cloud or multi-cloud, on-premise or private cloud deployment.

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Actian

User Sentiment:
n/a

The Actian Analytics Platform accelerates the entire analytics value chain from connecting to massive amounts of raw big data all the way to delivering actionable business value. The platform addresses business opportunities with sophisticated, high performance analytics at low latency and excels in multiple dimensions.

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1010data

User Sentiment:
n/a

By unifying data and analytics on its platform, 1010data completely changes the analytic process – now even business users can perform analysis on data in the same place as it is stored. Use more information to make better business decisions.

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Pentaho Data Integration

User Sentiment:
n/a

Pentaho Data Integration (PDI), also known as “Kettle,” is part of the larger Pentaho Open Source Suite. PDI has numerous features, but topping the list is its approach to data integration, which allows users to define integration jobs and alternative transformations. Kettle takes a code-free approach and instead opts for visual tools and is known for its easy integration.

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WebFOCUS

User Sentiment:
89%

The WebFOCUS BI suite is a strategic intelligence product from Information Builders, built to help companies use their data more strategically. The tool is effective for medium to large-sized enterprises.Dashboards, scorecards, mobile business intelligence, guided ad hoc reporting, deep integrations with third-party programs, dynamic report distribution and much more, are included in the software. The system can be deployed in the cloud, or on-premise to meet user’s need.

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TARGIT

User Sentiment:
85%

TARGIT Decision Suite is a self-service, end-to-end BI tool with a focus on creating unique and actionable information for each department in an enterprise. It merges internal and external data and presents it in customizable dashboards and storyboards for each division of a business. Its proprietary in-memory technology makes it flexible and accessible. With variable deployment and stand-alone or embedded delivery options, it makes data accessible and actionable from most devices with web-browser capability.

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SAS Viya

User Sentiment:
n/a

Quickly and confidently seize new opportunities. Make better choices ahead of the competition. And create new value from big data. High-performance analytics from SAS can handle your most difficult challenges and generate high-impact insights.

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Dundas BI

User Sentiment:
86%

Dundas BI is a powerful browser-based analytics solution that enables users to generate interactive dashboards, customize their visualizations, build reports and drill down into data. It is flexible and scalable, with a responsive design that lets users integrate data from any kind of source in real time.Accessible from any device, it functions on its own or users can embed it into other kinds of software.

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Birst Big Data

User Sentiment:
n/a

Combine large volumes of unstructured data with disparate sources of structured data to visualize data in its full context. Use Birst’s Automated Cloud Analytics Engine and User-Ready Data Store to transform complex data sets into intuitive visualizations, reports and dashboards – so non-technical end-users can leverage insights in their daily work lives.

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Essbase

User Sentiment:
n/a

Oracle Essbase, formerly known as Oracle Hyperion Essbase, is an Online Analytical Processing provider for businesses to develop complex models of their activities that result in actionable insight. It can scale from simple ad-hoc queries to extensive, repeated multi-dimensional aggregations, and present the results in a usable form.Through both retrospective and predictive analysis, business owners can maximize efficiency and profitability by turning data sources throughout the enterprise into usable information. It is configurable to an organization’s ongoing data needs.

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Azure Big Data

User Sentiment:
n/a

Microsoft is taking Big Data to a billion people by providing easy access, big or small, and enabling end users to analyze with familiar tools like Excel. We also give IT a complete platform to scale insights across their organizations with confidence

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