Best Big Data Analytics Tools
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PRODUCTS
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BUYER'S GUIDE
Buyer's Guide
By Ritinder Kaur, Market Analyst at SelectHub
Every organization worth its money invests in big data analytics to monetize its digital assets. With massive volumes of business information lying unused in silos, businesses are aware that they lose out on opportunities to the competition. If this is you, you should look for the best big data tools in the market.
This buyer’s guide provides the information to assist you in your software search, with handy resources and tips to ask the right questions before you decide.
Executive Summary
- Big data analytics software has the edge over traditional systems in processing large volumes of information, including unstructured digital assets.
- Creating a requirements checklist by identifying your implementation goals assists in software selection.
- Separating requirements into must-have and nice-to-have features helps stay on track with budget and business needs.
- Data sharing and IoT analytics are some current big data analytics trends.
- Ask the right questions within your organization and vendors for the tool comparison.
- What Is Big Data Analytics Software?
- Deployment Methods
- Primary Benefits
- Implementation Goals
- Key Features & Functionality
- Advanced Features to Consider
- Current Trends
- How to Begin a Software Comparison
- Cost & Pricing Considerations
- The Best Big Data Analytics Software
- Questions to Ask Yourself
- Questions to Ask Vendors
- In Conclusion
- Additional Resources
What Is Big Data Analytics Software?
Big data analytics solutions are software that enables analyzing complex and varied information to gain meaningful insight that’ll benefit your organization. High volume, velocity and variety of information overwhelm traditional processing and analytics systems, creating the demand for modern big data software.
These tools have an advantage over traditional software by integrating and analyzing unstructured information. In an economy ruled by big data and business analytics, a good piece of software that can process vast amounts of information is priceless. Organizations can't do without big data tools with sources as disparate as social media comments, images, and audio and video feeds.
Mobile insight, connected IoT devices, cloud computing traffic and technologies like artificial intelligence (AI) contribute to the big data boom.
Fortune Business Insights predicts the big data analytics market will grow to $655.53 billion by 2029.
Big data analytics is a discipline that falls under the umbrella term business intelligence, with business analytics, embedded analytics and enterprise reporting. Descriptive, predictive and prescriptive analytics are different ways of looking at information for business analytics.
Deployment Methods
Would you prefer to deploy on-premise or move to the cloud? Or would a hybrid solution work better, considering your legacy infrastructure?
On-premise
Installing on local infrastructure means you pay once for the cost of ownership. Plus, internet connectivity isn’t a deal-breaker when the software is in-house. You have complete control with full autonomy to plan maintenance, downtimes and upgrades. You can buy more storage when needed.
But setup and maintenance don’t come cheap. Scaling can be a pain, and the cost of technical resources for upgrades and patches adds to your overhead.
Cloud-based
Cloud software vendors offer flexible subscription plans with the option to pick and choose features, like the number of licenses and storage capacity. Managed analysis tools make you worry-free as the vendor handles upgrades and fixes.
Information security is a primary enterprise concern when considering cloud-based software tools. But not to worry, all leading vendors provide encryption and authorization protocols.
But, slow internet can cause latency issues and performance lag. Besides, a periodic subscription might be less than the cost of owning the software, but costs can pile on fast when opting for additional licenses, add-ons and upgrades.
Hybrid
A hybrid solution combines the benefits of information security with the perks of the cloud. Hybrid analytics solutions maintain consistent performance, scaling with larger workloads.
Pay-as-you-go plans are available, though at slightly higher prices than the cost of a public cloud. They prove viable for companies with fluctuating workloads.
Whatever your preferred deployment method, whether the software integrates with your existing software and hardware systems can be a deal-breaker.
So, which model fits you best?
A SaaS analytics solution might be a good fit for small businesses with limited infrastructure and IT resources. Larger enterprises can afford greater control over their information, so they might find on-premise or hybrid solutions appealing.
However, these aren’t set-in-stone rules, so consider your business’ unique situation when choosing a deployment method.
Expert recommendations and analysis on the top software
Primary Benefits
With big data tools, you can pivot with fluctuating market trends by realigning strategies and redesigning campaigns. In the process, you can look closely at your business’ performance to identify inefficiencies and successes.
Let’s see how.
Maximize Your ROI
Customer feedback and operational performance metrics help downstream inventory, supply chain and workforce management processes. You come closer to identifying what customers want and can realign internal processes accordingly. It saves a lot of heartburn later trying to catch up with the competition.
You can boost revenue by consistently delivering to buyers, which helps establish a reputation as a serious player in the market. Analytics tools enable you to base your business strategy on hardcore metrics, irrespective of their volume and type.
Innovate
Big data analytics drives innovation by providing easy access to hidden insight through self-service methods like natural language processing. Data democratization increases the chances of finding previously undiscovered correlations between metrics.
Big data means more comprehensive information — and harnessing it gives a clearer picture of what’s working and not. This insight into the business’s strengths and weaknesses can help you decide which new products to launch and whether it’s the right time to diversify.
Improve Customer Experience
Customer service calls, chats, surveys, and social media comments and impressions drive the consumer experience. Big data software can capture this information to provide clear and current market insight.
Was the setup process intuitive, and did the customers find the help documentation useful? Were they able to generate the desired reports? For consumer products, the feedback could range from a star-based rating to text-based comments about whether the buyer would recommend the product to others.
Customer feedback is the linchpin around which you arrange your service or product design. It's how you build and retain a customer base and acquire new buyers.
Manage Risk
All business decision-making involves risk, especially when diversifying or growing your business. Risk management identifies and assesses these potential blockers and reduces their adverse impact through mitigation and monitoring. Big data combined with analytics systems can highlight hurdles in your business journey, reducing decision-making uncertainty.
Its applications include vendor risk management, money laundering, fraud prevention, credit risk assessment and identifying customer churn.
Expert recommendations and analysis on the top software
Implementation Goals
Listing what you want the product to achieve will give you the clarity to shortlist suitable software. Here’s a list of common implementation goals to add to yours.
Goal 1 Stay Ahead in the Market |
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Goal 2 Improve Business Performance |
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Goal 3 Increase Customer Engagement |
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Goal 4 Manage Digital Assets | Poor information management practices cost enterprises thousands of dollars every year.
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Goal 5 Secure Business Information |
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Key Features & Functionality
Vendors advertise solutions with many shiny, new features, but do you need them? If your stakeholders approve, sure, go for them. But, if you’re working within strict budget constraints, it helps to focus on the basic features.
Digital Asset Integration | Connect to web and local files, and information warehouses and lakes in the cloud and on-premise. You should have access to near real-time information when you need it. |
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Dashboarding and Visualization | Create visualizations and personalize them with custom themes, formatting, styles, colors and fonts. Insights are helpful when consumers can understand them, irrespective of their skill levels. |
Report Sharing | Share information with your teams and clients with big data reporting tools. Does the tool provide report bursting? Automated refreshes and interactive controls give you a clear view of critical metrics. |
Security and Information Governance | Check if the software complies with PCI DSS, HIPAA and ISO 27001. Restrict access by assigning permissions at the column and row levels. Audit usage and access with activity logs and versioning. |
Embeddability | Add analytics to user consoles by embedding the software tool into business applications. Personalize the dashboards and reports to give users a familiar interface and establish your brand identity with company logos, themes and colors. |
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Advanced Features & Functionality
At some point, the core features mentioned above won’t be enough. Eventually, your business will outgrow its basic software needs and require something more specialized. Or you might want to get advanced features at the onset to prepare for the future.
Even if you’re not planning for them now, it helps to know about the advanced features these tools offer.
Forecasting | Any big data analytics tool worth its name will provide forecasting features. Predictive analytics lets you plan for upcoming opportunities through modeling and regression techniques. Ask the vendor if the solution supports machine learning and automated recommendations for guided analysis. |
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Scalability | Your analytics tool should seamlessly connect to a greater number and variety of sources and integrate with newer add-ons. It should scale to take regular vendor updates with minimal downtime and zero impact on end users. |
Collaboration | The best big data software should let you collaborate with others on specific dashboards and visualizations through comments and mentions. It’ll help avoid switching between different apps to get the desired metrics. How much work you can get done as a team within your dashboards will determine the tool’s efficacy. It’ll reduce the need for many run-of-the-mill reports, saving your team’s time. |
Mobility | The best big data analytics software vendors offer mobile insight. Check which specific features they offer, like interactivity, sharing, in-app alerts and push notifications. |
Augmented Analytics | Many enterprises want to reduce the time to insight by delegating analytics tasks to respective teams. It reduces the burden on managers and top stakeholders and encourages shared ownership of project goals. If this is you, augmented analytics can help analyze metrics using machine learning and natural language. These modules might be á la carte and so cost extra. Do check with vendors before you decide. |
Expert recommendations and analysis on the top software
Current Trends
Knowing the latest software trends helps fine-tune your requirements checklist and compare products.
Industry-driven bots capture user information and execute tasks, generating job reports and driving processing pipelines. Virtual assistants like Siri and Google Assistant, and social media platforms capture and generate information by the second, producing huge repositories to fuel the analytics market.
IoT Will Raise the Bar for Data Management
Due to the growth in machine learning, AI and IoT analytics, there is an upsurge/increase in the number of connected devices like machine sensors. Statista predicts that IoT-connected devices will grow to 29 billion by 2030.
IoT is in use in all industries like gas, steam, water supply and waste management utility companies, among others. According to Statista (linked above), consumer companies like retail, manufacturing, real estate and healthcare accounted for 60 percent of all IoT-connected devices in 2020.
IoT big data differs from traditional IoT as it needs more agile, scalable platforms, flexible storage and robust analytical tools than conventional infrastructure. Managing this information securely and providing transparency to users and regulatory bodies will be challenging for enterprises going ahead.
Edge Computing and AI Will Drive Security Standards
Edge computing refers to information processing at or near the source, which lightens the workload burden on servers and networks. Edge devices like smartphones, autonomous vehicles and smart grids add to the already ballooning information online.
Edge computing raises logistical challenges like migrating network security protocols and complying with privacy and governance regulations. The secure access service edge (SASE) framework is a network protection protocol for information processed in edge devices. But it's new and needs to become mainstream to have a large-scale impact on information security.
IoT and machine learning are a formidable combination. With edge devices added to the mix, secure data access and governance compliance will be the next challenge for businesses.
Information Sharing Will Fuel Industry Verticals and Consumer Markets
Previous privacy protocols restricted sharing of consumer information with third parties. But, CEOs realize that siloed information benefits only a select few, while shared information helps everyone. Decrypting shared information isn’t even an issue now that processing is possible on encrypted sets.
Data marketplaces monetize information sharing with subscription-based licensing, offering connectivity through the data fabric. They build separate databases with on-demand connectivity for entities willing to store and share their digital assets.
With data sharing promising to be a trend next year and beyond, big data in verticals is a commodity that adds to enterprises’ bottom line. Big data analytics tools with open architectures will be in demand.
Augmented Insights Will Be a Primary Business Requirement
Verified Market Research predicts the augmented analytics industry will be worth $66.61 billion by 2030. The increase in information volumes and complexity drives the need for self-service BI and analytics, which drives augmented tech.
The software sector is the largest consumer of artificial intelligence and machine learning technologies. Modern big data analytics software with machine learning speeds up information discovery through model building, clustering and regression techniques. Automation support is a primary advantage of augmented technologies.
Augmented analytics is a primary requirement for many enterprises when buying software. It’ll be interesting to see how it evolves in tandem with regulatory and data quality concerns.
How To Perform a Software Comparison
Comparing software solutions can be difficult without the right tools and resources. We are here to help.
Start your software selection process with our requirements template, or create your own checklist listing your business needs. Compare vendors and big data analytics solutions with our software comparison report.
Cost & Pricing Considerations
The cost of a software solution can make or break your buying decision. While building your product list, check the deployment cost, especially whether implementation support is available. If not, you should pencil in the cost of technical resources.
Ask about necessary and optional add-ons and which ones are free. Verify whether support is free with the product and what it entails, and check into paid support plans.
Get the pricing details from the vendor’s website or ask for a quote. Our pricing guide helps you determine the best big data software per your budget.
Expert recommendations and analysis on the top software
The Best Big Data Analytics Software
Check out the big data tools list on our analytics leaderboard, curated by the SelectHub analyst team. It ranks the software solutions based on how well they deliver on essential requirements.
Oracle Analytics Cloud (OAC)
OAC is a reporting and analytics tool that drives business decisions by integrating and analyzing big data from disparate sources. Personalized searches and automatic recommendations help generate interactive, up-to-date visualizations. Dashboards and reports are shareable and collaborative, and natural language explanations make them understandable for all.
The vendor offers a native mobile app.
Big data analytics with Oracle Data Integrator.
IBM Watson Analytics
Watson Analytics is a big data analytics solution with machine learning capabilities for data exploration and forecasting. The vendor offers customizable modules for lifecycle management, applications, APIs and industry-specific specializations. AI and pattern identification help remedy potential issues in workflows.
Role-based permissions and single sign-on (SSO) protocols secure data through restricted information access. You can train the solution to preempt information breaches before they impact performance.
An IBM Watson dashboard shows multiple visualizations with performance and risk metrics.
SAP HANA
SAP HANA is the in-memory database for SAP’s Business Technology platform, supporting businesses with robust processing and analytics. The platform’s lean model lets you perform advanced analytics on real-time information at the speed of thought.
Developers can build robust solutions through its library of pre-built functions. The platform allows you to incorporate location coordinates into business intelligence.
A performance analytics dashboard in SAP.
BIRT
BIRT from OpenText is an open-source big data analysis software for information exploration through visual analysis. The platform blends information from various sources, including Plain Old Java Objects (POJOs), Java Data Objects (JDO) datastores, SQL databases, JFire scripting objects, XML and web services.
Its charting engine enables embedding reports and charts into business systems. A single presentation can incorporate multiple reports in one document.
Viewing business insight in BIRT Report Viewer.
Qlik Sense
Qlik Sense is an AI analytics platform that processes big data for enterprises. It’s deployable on-premise, in private and public clouds, or as a hybrid solution. Qlik Sense is flexibly scalable with an enterprise-class, modular architecture.
The vendor offers conversational and location-based analytics and indexing through Qlik NPrinting, Qlik Insight Bot, Qlik DataMarket and Qlik GeoAnalytics.
On-the-go big data analytics with Qlik Sense.
Expert recommendations and analysis on the top software
Questions to Ask Yourself
Even with a requirements checklist, weighing the various big data analytics solutions’ pros and cons can be daunting. Having internal conversations and asking questions to identify stakeholder expectations can help.
Use these questions as a starting point:
- Are you using a big data analytics solution? What are the pain points?
- What’s the company budget? Is the current software cost-effective?
- What are your company's present and future goals? How does the analytics tool fit in with them?
- Who are the end users, and what gaps do you hope this platform fills?
- What databases should it integrate with, e.g., SQL Server, MySQL, MS Access, etc.?
- Which deployment method works best for your company?
- Is self-service a must-have feature, or do you want a system that performs the analysis and offers you recommendations for further action?
- How important is scalability?
- What databases should it integrate with, e.g., SQL Server, MySQL, MS-Access, etc.?
- Will you need a mobile application?
- Is technical expertise available to deploy and manage the software?
Questions to Ask Vendors
Use these questions as a starting point for conversations with vendors:
About the Software
- Does the solution provide visualization, data management, reporting and collaboration?
- Is it scalable? Can it integrate with add-ons and other applications?
- Does it need customization before deployment?
- Which mobile functionalities will you get?
- Can you track KPIs? Is this feature available on mobile?
- Is the software user-friendly?
About the Vendor
- How often does the vendor issue updates?
- Is training included in the purchase plan?
- Which features cost extra?
- Do they provide implementation support?
- Do they offer phone, email and chat support? Is it free or paid?
- How can you submit a support request? What is the average response time?
In Conclusion
Selecting a big data analytics tool is a task that needs careful thought and lots of research. Do your due diligence in evaluating business requirements and list them by holding in-depth discussions with stakeholders. It should tell you what you want in a big data analytics tool.
Asking the right questions internally and to potential vendors is an excellent way to fine-tune your requirements and compare software products. It will help you narrow down your product shortlist to present to your top-level management for the final decision.
Expert recommendations and analysis on the top software
Top 10 Big Data Analytics Tools Leaders by Analyst Rating (of 96 products)
(of 96 products) GET THE IN-DEPTH REPORTProducts found for Big Data Analytics Tools
Hadoop
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.
Tableau Big Data
Tableau is a data visualization platform that can perform big data analytics. Users can leverage well-known frameworks such as Apache Hadoop, Spark and NoSQL databases to meet their data needs. It simplifies the management, sorting and analysis of information through a single, digestible dashboard. Businesses can incorporate data from all sources and visualize it in a myriad of ways to acquire insights. The vendor offers three versions — Tableau Online, Tableau Desktop and Tableau Server.
Board
Board is a robust solution that offers 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 enterprises 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-sized companies across various industries, and its programming-free toolkit helps businesses analyze and plan with a tailored, efficient approach, irrespective of technical skill levels.
Domo
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.
Tableau
Tableau is a data visualization and analytics solution that assists enterprises in making data-driven business decisions. It blends information from a wide range of sources to deliver actionable, real-time insights. It allows exploration of data via intuitive means such as drag-and-drop filtering and natural language queries, irrespective of skill levels. With ample customization and security options, it offers control over data visualization, enabling creation of dashboards and stories that effectively convey business narratives. It can be purchased as part of the Tableau Creator package, which includes the desktop version, Prep and a Creator license of the server or online version.
BIRT
It’s an open-source project on Eclipse and is an acronym for Business Intelligence and Reporting Tools. It lets organizations extract and transform data for business analysis. Its Report Designer enables visual report-building within interactive dashboards. The runtime component executes the reports once ready. Embedded into a range of business interfaces, it enables custom design layout, data access and scripting to present report output over the web. It supports charts, crosstabs, using multiple data sources within the same report, re-using queries within reports and addition of custom code.
Zoomdata
Zoomdata (now discontinued) was an analytics and reporting tool that allowed users to explore and analyze large, complex datasets. It provided a simple, modern interface that maked data literacy attainable for users of all technical levels.It was designed to be scalable and embeddable through white labeling architecture. It was built on HTML5 and JavaScript, making it fully customizable. It aimed to expedite the processes of data exploration, visualization and analysis to help users make data-driven decisions.
Alteryx
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 enables high-speed analytics and modeling, supported by a community of model developers in the vendor’s customer base. Depending on the products selected from the suite, it can perform end-to-end BI, from data harvesting from deep data pools to automated operationalizing.
Spotfire
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.
BigQuery
Google BigQuery is a serverless solution that can handle large volumes of data and apply standard and sophisticated analytics techniques to deliver actionable insights to users. It comes with a number of standard and unique inclusions to help technical and non-technical users perform analysis, deliver reports, create dashboards and generate insights.
MATLAB
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.
Ezoic
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.
SAP HANA
SAP HANA is the in-memory database for SAP’s Business Technology platform with strong data processing and analytics capabilities that reduce data redundancy and data footprint, while optimizing hardware and IT operational needs to support business in real time. Available on-premise, in the cloud and as a hybrid solution, it performs advanced analytics on live transactional data to display actionable information. With an in-memory architecture and lean data model that helps businesses access data at the speed of thought, it serves as a single source of all relevant data. It integrates with a multitude of systems and databases, including geo-spatial mapping tools, to give businesses the insights to make KPI-focused decisions.
Panoply
Panoply is a fully-integrated data management platform that syncs, stores, organizes and analyzes data from many sources. It enables the use of search query language to explore data, then analyze and visualize it through its robust integration capabilities. Accessible anywhere via the cloud, it combines data warehousing, AI-powered data processing and a variety of integrations to provide a user-friendly data analysis infrastructure.
GoodData
GoodData is a powerful, embeddable, customizable SaaS solution that combines, analyzes and visualizes the internal and external data of an organization to help businesses change the way they make decisions, with a focus on data-driven best practices. It 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.
RapidMiner
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.
Spark
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.
Cloudera
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.
Hortonworks
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.
MicroStrategy
MicroStrategy is a data analytics platform that delivers actionable intelligence to organizations of all sizes. It allows users to customize data visualizations and build personalized real-time dashboards. It leverages data connectivity, machine learning and mobile access to offer users comprehensive control over their insights. Due to its ease of use and scalability, it stands out as a leader in the enterprise analytics field. Users can choose between cloud, on-premise or hybrid deployment according to their needs.
Confluent
Confluent is a cloud-native data streaming platform for data storage and management. It integrates Apache Kafka with other systems and offers pre-built connectors for other sources. Users can get the most out of Kafka with real-time data flows and processing. Enterprise-grade security protects data, and automated monitoring detects potential problems. Processing is continuous, so data moves in real time and reaches the users who need it. Pipelines can be built and managed in a simple graphical interface with multiple programming languages supported.
QlikView
QlikView is a data discovery and customer insight platform from Qlik, a leader in the insight and intelligence space. However, it is not available for purchase any longer. Qlik Sense, Qlik’s next-generation offering, is available for new customers. It offers self-service data that can help drive decisions and generate significant ROI for technical skill level users. It’s 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, and 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 module, processing data down to as little as 10% of its original size.
Infor Birst
Infor Birst is a cloud-based analytics software tool that aims to help users discover insights without the need for analyst input. It unifies IT-managed enterprise data with user-owned data, supporting the blending of both in a top-down and bottom-up manner. It uses consistent business metrics to structure raw data into organized sets and visualizations. It helps users identify patterns and better understand their organization’s KPIs. It offers a seamless, integrated UI that allows users to perform every step of the data analysis process in a single interface, enabling a smooth experience. It can be deployed either from the cloud or self-hosted on-premise. Users can purchase it in three available formats: per-user fee, by department or business unit or by end-customer in embedded scenarios.
KNIME
KNIME is an open-source end-to-end data analytics solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the data analytics learning curve data, 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.
Talend
Talend is an open-source data integration and management platform that enables big data ingestion, transformation and mapping at the enterprise level. The vendor provides cross-network connectivity, data quality and master data management in a single, unified hub – the Data Fabric. Based on industry standards like Eclipse, Java and SQL, it helps businesses create reusable pipelines – build once and use anywhere, with no proprietary lock-in.The open-source version is free, with the cloud data integration module available for a monthly and annual fee. The price of Data Fabric is available on request.
Airflow
Airflow is an open-source Python framework that allows authoring, scheduling and monitoring of complex data sourcing tasks for big data pipelines. Aligned with the DevOps mantra of “Configuration as Code,” it allows developers to orchestrate workflows and programmatically handle execution dependencies such as job retries and alerting. Through the use of Directed Acyclic Graphs (DAGs), developers can customize pipeline processes as needed by using multi-step workflows. They can run part of the workflow at any time, even when tasks are being updated in real time. Besides out-of-the-box integrations with MySQL, Microsoft Server and SaaS platforms, it also provides custom connections to plugins. Robust and flexible, it is free to download for all users.
Qlik Sense
Qlik Sense is a self-service data analytics software that enhances human intuition with the power of artificial intelligence to enable better data-driven business decisions. It allows organizations to explore their data and create intuitive and compelling visualizations from data 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 businesses modernize their approach to intelligence. It has two editions: Business and Enterprise, offered on a per account annual subscription. Enterprises can choose between a hosted SaaS public cloud or multi-cloud, on-premise or private cloud deployment. Qlik Sense Business comes with a free 30-day trial. Its desktop version is available for free for personal use.
Looker Studio
Google Looker Studio is a robust data analytics and business intelligence platform designed to help organizations extract valuable insights from their data. It offers a user-friendly interface for data exploration, powerful visualization tools, and real-time data monitoring. Key features include customizable dashboards, SQL-based queries, and seamless data integration from various sources. Users can collaborate within the platform, fostering a data-driven culture. Looker Studio empowers users to make data-informed decisions, with the ability to create tailored reports and leverage machine learning for predictive analytics. It suits businesses of all sizes and industries, facilitating scalable analytics and promoting data-driven decision-making.
Dataiku
Dataiku is a powerful data analytics platform designed to empower organizations with data-driven insights and machine learning capabilities. It offers a comprehensive suite of features, including data integration, preparation, and advanced machine learning, all within a user-friendly interface. Dataiku facilitates collaboration among data professionals and business users, streamlining the data analytics process. Its AutoML capabilities simplify machine learning model development, making it accessible to users with varying levels of expertise. Real-time insights and scalability are key benefits, allowing organizations to make timely decisions and adapt to changing data requirements. Despite some learning curve challenges, Dataiku remains a favored choice for medium and large businesses seeking robust data analytics solutions.
SageMaker
Amazon SageMaker is a comprehensive machine learning platform by Amazon Web Services (AWS) designed to simplify the entire machine learning lifecycle. It empowers businesses to build, train, deploy, and manage machine learning models efficiently. Key features include robust data preprocessing tools, a wide selection of machine learning algorithms, and automated hyperparameter tuning. SageMaker's scalability ensures it's suitable for both small experiments and large-scale production deployments. It offers cost-efficiency with a pay-as-you-go pricing model and facilitates model management and monitoring. The platform integrates seamlessly with the AWS ecosystem, providing security and compliance features. SageMaker's AutoML capabilities make machine learning accessible to users of varying expertise. Overall, it streamlines the machine learning process, enabling organizations to harness the power of AI for improved decision-making and innovation.
Mashvisor
Mashvisor is a well-known application that ranks 31 among all Big Data Analytics Tools according to our research analysts. Starting from $17.99, Mashvisor is priced a notch under most others, commonly offers a free trial and is most advisable for companies on the small or medium side. Enterprises who have used Mashvisor include Assam Oil, Coca-Cola and VISA. Mashvisor can be deployed online and on-premise and is accessible from a limited set of platforms including Windows and Linux devices.
Vertica
Vertica is an analytics and data exploration platform designed to ingest massive quantities of data, parse it, and then return business insights as reports and interactive graphics. Elastically scalable, it provides batch as well as streaming analytics with massively parallel processing, ANSI-compliant SQL querying and ACID transactions. Deployable in the cloud, on-premise, on Apache Hadoop and as a hybrid model, its resource manager enables concurrent job runs with reduced CPU and memory usage and data compression for storage optimization. A serverless setup and advanced data trawling techniques help users store and access their data with ease.
IBM Watson Analytics
IBM Watson is an AI-augmented data science solution that enables employees to harness the power of proprietary data, unlock its potential and apply insights gained from it in new ways. It offers a wide variety of customizable modules for lifecycle management, data applications, APIs and industry-focused specializations.
Qubole
Qubole is a cloud-based data lake management solution that enables fast data lake adoption for businesses. It allows continuous collaboration by ingesting and processing continuously generated data. Connect to a variety of structured and unstructured data sources and perform ad hoc and streaming analytics, build and test machine learning models and explore data.Explore, build, orchestrate and deliver data pipelines with ease while minimizing cost and maximizing performance. Users can choose a data format best suited to their workflow. It includes a centralized workspace, development tools, an inbuilt notebook environment and extensive integrations to provide end-to-end service with near-zero maintenance.
Azure Databricks
Azure Databricks is a unified big data analytics platform that provides data management, machine learning and data science to businesses through integration with Apache Spark. Integrating with a host of data sources, it pulls data from a wide variety of sources, transforms and then analyzes it through visualizations. In addition to setting up ETL flows, it empowers enterprises to create data models for predictive analysis, forecasting and future planning. The vendor offers three workloads based on the stages of analytics workflows — Jobs Compute and Jobs Light Compute for data engineers, and the All-Purpose Compute workload for data scientists.
Essbase
Oracle 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 multidimensional 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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