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.
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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.
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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.
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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 (Page 2)
Azure Big Data
Azure Big Data from Microsoft provides robust analytics and AI services with cloud computing. Build, test, deploy and manage services and applications via public and private cloud data centers managed by Microsoft. It runs both on-premise and through the cloud, using powerful modules and frameworks to create scalable business options. It provides a range of services which include IaaS, PaaS and SaaS.
Ayasdi
Owned by the SymphonyAI Group since 2019, Ayasdi is a machine intelligence platform that leverages statistical calculations and mathematical algorithms to deliver analytics to enterprises. It helps businesses connect the dots by segmenting complex, related datasets into groups through topological data analysis (TDA). Organizations can detect financial risk areas and fraud through automated machine learning workflows, and uncover previously undiscovered patterns and risks.Its sequential data processing and augmented analytics inclusions enable healthcare providers to create consistent patient care strategies. Running on Linux and Hadoop, it can be deployed on premises or in the cloud as a Software-as-a-Service (SaaS).
Azure Synapse Analytics
Microsoft Azure Synapse Analytics is a comprehensive cloud-based analytics service designed to empower organizations with robust data warehousing and data analytics capabilities. It seamlessly integrates data warehousing and big data analytics, enabling users to ingest, prepare, manage, and serve data for immediate business insights. Key features include scalable resources, real-time analytics, machine learning integration, and a collaborative workspace for data professionals. Azure Synapse Analytics offers benefits such as improved scalability, enhanced data security, and simplified data integration. It's particularly valuable for organizations looking to harness the power of data to make informed decisions and gain a competitive edge in today's data-driven landscape.
Actian
Actian is a cloud data management platform that enables data integration with fully managed warehousing, transformation and analytics for enterprises. It integrates with various cloud-based and on-premise technologies and services. With massively parallel processing and data compression on the back end, its embedded analytical engine works in tandem with a robust RDBMS, complementing its computing with built-in user-defined functions to process online transactions at scale.Part of the Cloud Security Alliance, the vendor consistently updates their best practices to ensure the security of its cloud services. It offers a 30-day free trial. Pricing is available on request.
1010data
1010data is a market intelligence and enterprise analytics solution that helps track consumer insights and market trends. In addition to vendor-critical insights, it provides brand performance metrics to buy-side entities. Seamlessly embeddable, it can also function as a standalone private-label option. Data scientists and statisticians leverage its integration with R to view and query data tables.It enables analytics development through its QuickApps framework. By tracking consumer spending trends and brand performance, it enables businesses to better position their products in the marketplace.
Pentaho Data Integration
Pentaho Data Integration helps organizations access, prepare and analyze all of their data from one place. It connects quickly and easily to any data source, whether it's structured or unstructured. It uses an extract, transform and load (ETL) approach to data management.Organizations can govern their data, ensure data quality and accelerate time to insight. Available on-premise and in the cloud, it’s easy to use and offers a wide range of features.
Apache Arrow
Apache Arrow is an open-source software development framework for building high-performing data analytics applications that process large, complex data sets. With an in-memory columnar format for data storage, it provides a query execution language that helps define the framework for analytics libraries. Language-agnostic due to easy translation, it acts as an interface between a wide range of programming languages and technologies.With a modern CPU architecture based on the single instruction, multiple data (SIMD) taxonomy, it processes data at lightning speeds. Its zero-copy design makes moving data round faster and more efficient.
WebFOCUS
WebFOCUS is a data analytics solution from Information Builders that helps businesses transform their data into actionable insights. Its intuitive interface makes self-service intelligence accessible to users of all skill levels. Its capabilities include dashboards, scorecards, mobile access, guided ad hoc reporting, data integrations, dynamic report distribution and much more. As of 2020, the vendor has announced that it will be acquired by TIBCO.
TARGIT
Targit is built from the ground up for reporting and business metrics, getting actionable insight from trillions of rows of data in seconds. It surfaces complex events and provides key information to make business decisions. It merges internal and external data and presents it in customizable dashboards and storyboards. Businesses can figure out how to grow in target markets by accurately analyzing data. Deployment options vary from stand-alone to embedded, with web browser compatibility built-in.
Arcadia Data
Arcadia Data is a cloud-native visual analytics platform that provides data visualization and analysis to enterprises. Its in-data-lake architecture enables accelerated insights in real time through integrations with Confluent Cloud, Google BigQuery, Amazon Redshift and other cloud data warehouses. Developers leverage its intuitive interface and no-code approach to build analytics applications and embed them into websites. Its ability to process batch and streaming data in a single, unified interface empowers enterprises to harness real-time data insights for enhanced decision-making.Owned by Cloudera since 2019, it serves small and medium-sized businesses in the U.S. and the U.K. In addition to its free version, Arcadia Instant, the vendor offers an Enterprise version.
Apache Pig
Apache Pig is an open-source platform ideal for analyzing large datasets and representing them through data flows. Leveraging its high-level language, Pig Latin, users can develop functions and write scripts of their own to process data. Using a component known as Pig Engine, these scripts are further transformed into MapReduce tasks. With the help of a multi-query approach, users can easily reduce the code’s length, thus, decreasing development time. It’s commonly used with Hadoop to perform all aspects of data manipulation.
Logi Symphony
Logi Symphony (formerly Logi Analytics) provides businesses with interactive data analytics and visualizations, offering advanced reporting and visualization features in a minimal-code environment. It offers self-service data analysis and data discovery; then connects and blends operational intelligence to prepare it; and finally lets users turn it into dashboards, reports and other visualizations.
Striim
Striim is a data integration platform that facilitates ingestion, processing and delivery of real-time data across diverse cloud or on-premise environments. It ingests high-volume and high-velocity data from enterprise databases, log files, messaging systems, cloud applications and more. It performs on-flight data processing, including filtering, aggregations, transformations, masking and data enrichment. It offers built-in pipeline monitoring and validation capabilities. Its distributed clustered architecture enables users to scale horizontally to cater to data processing needs.
SAS Viya
SAS Viya is a cloud-based in-memory analytics engine that provides data visualization, reporting and analytics to businesses for actionable data insights. Powered by AI, it brings together visual analytics, visual statistics and data science for enterprises to achieve end-to-end self-service analytics. It uses a standardized code base with support for programming in R, Python, SAS, Java and Lua. Deployable in the cloud, on-premises and hybrid environments, it integrates with a wide range of business applications through an agile, scalable architecture. The vendor offers an introductory 30-day free trial.
IBM Watson Studio
IBM Watson Studio is a powerful platform designed to empower organizations in their data science and machine learning endeavors. It serves as a comprehensive hub for data analysis, model development, and collaboration among teams. Key features include advanced analytics tools, AutoAI for automating machine learning tasks, and a collaborative workspace for seamless teamwork. Users benefit from the ability to create, train, and deploy machine learning models within the platform, simplifying the transition to production environments. Watson Studio also offers data visualization tools for effective communication of insights. Its strengths lie in its versatility, collaboration capabilities, and automation, making it a valuable asset for organizations seeking to harness the potential of data-driven decision-making.
Displayr
Displayr is a noteworthy solution that ranks 52 among all Big Data Analytics Tools according to our research analysts and 1 crowd-sourced review from 1 source. Starting from $2499, Displayr is priced on the costly side, offers a free trial on occasion and is most advisable for companies large and small. Displayr can be deployed online and is accessible from a limited number of platforms including Windows devices.
SAP Analytics Cloud
SAP Analytics Cloud is an augmented analytics solution that uses machine learning technology to generate data visualizations and simulate predictive scenarios. It provides every member of an organization with access to automatic, interactive insights via natural language processing. Powered by artificial intelligence, it helps businesses make decisions with confidence by exploring what-if possibilities based on historical data. There are multiple plans available with different functionality levels, so businesses can choose the subscription that provides the best fit for their needs.
Oracle Analytics Cloud
Oracle Analytics Cloud is an AI-powered solution that provides robust reporting and analytics features to businesses of all sizes. It offers a strong selection of reporting and analytics features from the convenience of the cloud. It prepares and analyzes data for trends, then turns that data into an intuitive visualization for users to explore and share. This helps users get a deeper understanding of a range of business questions as well as make accurate predictions and forecasts.
DBSync
DBSync is an integration Platform-as-a-Service (iPaaS) that provides bi-directional data synchronization, migration and replication to enterprises. It enables businesses to connect to any combination of SaaS, cloud and on-premise applications and databases. It empowers companies to build warehouses for reporting and data mining. Its powerful integration engine offers scheduling, logging and transformation to streamline data management.Easily accessible from any device, it can be deployed in the cloud, on-premises or as a hybrid. In addition to its free Standard version, the vendor also offers paid pricing plans.
Apache Phoenix
Apache Phoenix is an open-source and relational database engine that uses SQL interface over HBase. With the standard JDBC APIs, users can easily create, delete, and alter SQL sequences, as well as delete or insert rows for tables in a single format or bulk. Via the HBase API, custom filters and coprocessors, it can load and query tens of thousands and even billions of records.
GridGain
Gridgain speeds up and scales data-demanding applications and provides real-time data processing through its distributed in-memory computing architecture. It is built on Apache Ignite to enable developers to modify its functionality without altering source codes. It is deployed securely across numerous global data servers to power existing or new data-critical projects with zero downtime. Customers can utilize robust integrations with relational operational databases (RDBMS), NoSQL databases and data lakes. In addition, it offers data monitoring, visualizations, off-heap memory support, load-balancing, fault tolerance and remote connectivity.The vendor offers three packages including an open-source community edition along with enterprise and ultimate packages with varied modules and cloud, on-premises and hybrid deployment options. Companies use it to meet the real-time big data demands of a variety of use cases such as ACID transactions, hybrid transactional/analytical processing (HTAP), ANSI-99 SQL, accelerated query processing, streaming analytics and machine learning frameworks.
InPhase
InPhase is an end-to-end scalable platform that enables organizations to make the most out of business data, improve decision-making and troubleshoot issues. It generates and delivers easy-to-interpret data insights to relevant stakeholders faster through dashboards and automated routing. Organizations can avert crises, anticipate outcomes and prevent wasted efforts using robust KPI monitoring and predictive analytics. Easy to customize, use and scale, it offers optional ready-to-use packages to facilitate various business processes. It also provides data consistency, mobility, communication and team planning modules to enable remote collaboration across devices. The vendor provides onboarding in days and offers extensibility through development to tech-savvy users.
SlamData
SlamData is an open source tool based on SQL that aims to make it easy for developers and non-developers to access MongoDB. This software is one of the fastest growing products in the NoSQL database space. This program brings high-quality source integration to the table, meaning that users can import from any source; no coding skills needed. This data can then be streamed to a variety of sources.
H2O.ai
H2O.ai is a cutting-edge machine learning platform known for its advanced automation and scalability. It empowers organizations to harness the power of artificial intelligence and predictive analytics. Key features include AutoML, which streamlines the machine learning process, and a rich library of advanced algorithms. H2O.ai offers benefits such as enhanced predictive accuracy, model interpretability, and flexible deployment options, catering to a diverse range of industries. Its open-source foundation fosters collaboration, while its comprehensive ecosystem allows users to customize and extend its capabilities. While it's praised for its efficiency and support for large datasets, users may encounter a learning curve when navigating its sophisticated features.
Dundas BI
Dundas BI is a browser-based analytics platform built to embed from the ground up. The vendor provides single and multi-tenant deployments with access to shared, reusable content. HTML5 enables customizable visualizations and user-friendly dashboards to build reports with interactivity options to drill down into data on demand. It allows secure write-backs to the database, capturing input through custom forms, stored procedures and REST APIs. It gives businesses complete control over the data stored on their IT infrastructure. The vendor offers a 25-day free trial.
Prisync
Prisync is a unique software product that ranks 22 among all Pricing Software according to our research analysts and 122 crowd-sourced reviews from 1 source. Starting from $99, Prisync is priced below average, has offered a free trial in the past and is most applicable for any business. Prisync can be deployed in the cloud.
Upsolver
Upsolver is a big data solution for ingesting and maintaining data at scale. It delivers exactly-once, strongly-ordered data in real time. It can ingest data from a variety of database types and formats. It identifies and addresses data quality issues during the ingestion process to avoid downstream errors and can automatically adjust for upstream schema changes. It uses cloud infrastructure and can deploy to AWS on a virtual private cloud. Processes can be configured through either SQL or an integrated development environment.
Microsoft Machine Learning Server
Microsoft Machine Learning Server is an AI-enabled enterprise intelligence solution with big data capabilities. With full R and Python support, it produces predictive and retrospective analytics, with the ability to score structured and unstructured data. It is fully machine learning enabled, with options to train or use prebuilt trained models. It integrates with major open-source resources and other Microsoft entities like Azure. Its web service publishing and operationalization features streamline putting insights to work.
Logz.io
Logz.io, a comprehensive observability platform, caters to diverse industries, providing scalable and efficient log management and analytics. Suited for enterprises seeking streamlined operations, Logz.io boasts key features such as real-time monitoring, log parsing, and advanced visualization tools. Users commend its "intuitive interface" and "quick setup," emphasizing its role in "simplifying log analysis." Some note pricing considerations, with one user stating, "cost-effective for the value." Users appreciate its performance, citing its ability to "handle large volumes of data." Compared to alternatives, Logz.io stands out for its "seamless integration" and "scalability," making it a favored choice in the competitive observability landscape. Pros Real-time monitoring Efficient log parsing Advanced visualization tools Intuitive interface Quick setup Cons Pricing considerations Learning curve for some Integration complexities Varied performance feedback Resource-intensive in some cases
Putler.com
Putler.com is an up-and-coming application that ranks 66 among all Big Data Analytics Tools according to our research analysts and 26 crowd-sourced reviews from 1 source. Starting from $29, Putler.com is priced moderately, offers a free trial on occasion and is most advisable for midsize companies. Businesses who have used Putler.com include Arcane Creatives, Convert Rocket and The Living Tree. Putler.com can be deployed online and on-premise and is accessible from a handful of platforms including Windows and Linux devices.
CXAIR
CXAIR is a rising software tool that ranks 67 among all Big Data Analytics Tools according to our research analysts and 10 crowd-sourced reviews from 1 source. Starting from $6000, CXAIR is priced fairly, doesn't typically offer a free trial and is most suitable for any sized company. Corporations who have used CXAIR include J.P. Morgan, ResMed and Wembley.
AnswerMiner
AnswerMiner is a rising software solution that ranks 68 among all Big Data Analytics Tools according to our research analysts. Starting from $39, AnswerMiner is priced fairly, doesn't usually offer a free trial and is most suitable for smaller businesses of 100 employees or less. Corporations who have used AnswerMiner include Motorola, PepsiCo and Rolls Royce.
MLens
MLens is an up-and-coming solution that ranks 69 among all Big Data Analytics Tools according to our research analysts. MLens is most suitable for medium-to-large-sized businesses. Enterprises who have used MLens include Assam Oil, Coca-Cola and VISA. MLens can be deployed in the cloud.
Exasol
Data Sandbox
limestats
Limestats is an up-and-coming platform not yet ranked among all Big Data Analytics Tools. Starting from $29.99, limestats is priced fairly, commonly offers a free trial and is most suitable for companies on the small side. Limestats can be deployed in the cloud and is accessible from a limited set of platforms including Windows devices.
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