Alteryx vs SAP HANA

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Our analysts compared Alteryx vs SAP HANA based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

SAP HANA Software Tool

Product Basics

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.
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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.
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$99/User, Monthly
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$972/Capacity Unit, Usage-Based
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Product Assistance

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Product Insights

  • Coding Flexibility: Design workflows in a flexible code-free or code-based interface, depending on individual abilities, needs and programming knowledge. Optionally, create code with C++, Python or R. 
  • In-House Model Library: Save on time and resources during app development; lean partially on the platform’s extensive customer base for the know-how. Access, run and modify any of hundreds of analytics applications in the Analytics Gallery created by the vendor’s community. 
  • Thorough End-to-End Analytics: Perform end-to-end analytics with products each specifically developed for a certain step of the analytical process. Collect, organize and prioritize data with Alteryx Connect and Dataset, execute it with Alteryx Designer and streamline operationalizing models with Promote. 
  • Spatial Analytics: Make location-based calculations — i.e. trade areas, drive time and more — using geospatial data and street map or satellite imagery integration. 
  • ClearStory Data: Perform continuous, automated analytics on complex and unstructured data at the enterprise level through ClearStory Data, acquired by Alteryx in 2019. 
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  • Database Management: Reduces operational complexity through the use of a single database that allows data to be stored without a predefined structure. Provides data structure flexibility to application developers. Joins this data with many other data types with full interoperability. 
  • Build Business Solutions: The Business Function Library delivers pre-built functions that developers link at the database kernel level to build powerful business solutions. 
  • Geo-spatial Analysis: Stores spatial data and enables geographical data processing to drive location-specific business application development. 
  • Deploy Anywhere: Deploy on-premise, multi-cloud or go hybrid. Set up on traditional servers, pre-configured appliances, the HANA Enterprise Cloud or partner clouds including AWS and Microsoft Azure. Extend on-premise solutions to the cloud smoothly during any phase of the project. 
  • Predictive Analytics and Machine Learning: Supports transactional processing through machine learning and data analysis in real time. Take action before or as events happen to improve results and boost productivity. 
  • Smart Data Access: Connect virtually to remote, externally supported databases. Stores only the metadata of the database objects as a virtual table in the local database schema. Access data from the remote database in real time, irrespective of its location. 
  • Reduced Cost of Ownership: Mitigates hardware costs through a reduced data footprint made possible by a compression algorithm and lean data structure. 
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  • Internal Data Visualization: Display data insights at each stage of ETL, enabling validation and verification at every step of analysis through its in-platform data visualization solution, Visualytics. 
  • Data Visualization Export: Export to data visualizers like Qlikview and Tableau in several formats seamlessly, if the platform’s in-house visualization capabilities don’t satisfy the business’s needs. 
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  • Data Integration: Captures any type of data — structured or unstructured — from database transactions, applications and streaming data sources. Ingests part of a data set or the complete data set into its native architecture for ready access. 
  • Capture and Replay: Record complex database transactions and then replay them on another device. Test on a non-production system while using production transactions, on a hosted instance, or in the cloud. 
  • Graph Data Processing: Combines its built-in graph engine with the in-memory relational database. Makes graph processing of relational tables easy to learn and use. 
  • SAP HANA Cockpit: Configure and manage HANA instances and applications through a single console interface. Easily schedule all backup jobs and monitor the system for immediate visibility of potential blockers. Integrate with other applications for workload management and security. 
  • Flexible Querying: Choose from a variety of semantics structures to query data in the database memory through a flexible algorithm. 
  • In-memory Architecture: Analyze insights in real time to monitor business KPIs and generate forecast trends. Access data quicker than with conventional databases via its in-memory database. 
  • Data Compression: Compresses data by up to 11 times and stores it in columnar structures for high-performance read and write operations. Saves storage space and provides faster data access for search and complex calculations. 
  • Parallel Processing: Performs basic calculations, such as joins, scans and aggregations in parallel, leveraging column-based data structures. Processes data quicker for distributed systems. 
  • Real-time Analysis: Queries transactional data directly as it is added in real-time. Leverages its inbuilt data processing algorithm to read and write data to a column storage table at high speeds. Acquire total visibility over information while it is being analyzed and make on-the-spot, incisive decisions. 
  • Role-based Permissions: Maintain data integrity across the organization — assign data access based on each team member’s role. 
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Product Ranking

#8

among all
Big Data Analytics Tools

#13

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Big Data Analytics Tools

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Analyst Rating Summary

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Analyst Ratings for Functional Requirements Customize This Data Customize This Data

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Augmented Analytics Computer Vision And Internet Of Things (IoT) Dashboarding And Data Visualization Data Management Data Preparation Geospatial Visualizations And Analysis Machine Learning Mobile Capabilities Platform Capabilities Reporting 69 54 89 100 96 93 0 86 0 25 50 75 100
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

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User Sentiment Summary

Excellent User Sentiment 496 reviews
Great User Sentiment 1173 reviews
90%
of users recommend this product

Alteryx has a 'excellent' User Satisfaction Rating of 90% when considering 496 user reviews from 4 recognized software review sites.

86%
of users recommend this product

SAP HANA has a 'great' User Satisfaction Rating of 86% when considering 1173 user reviews from 4 recognized software review sites.

4.5 (158)
4.2 (345)
4.7 (74)
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4.6 (19)
4.4 (56)
4.3 (328)
4.5 (208)
4.3 (481)

Awards

SelectHub research analysts have evaluated Alteryx and concluded it earns best-in-class honors for Integrations and Extensibility. Alteryx stands above the rest by achieving an ‘Excellent’ rating as a User Favorite.

User Favorite Award
Integrations and Extensibility Award

we're gathering data

Synopsis of User Ratings and Reviews

Data Analysis: All users who reviewed analytics said that the platform adds value to data through features such as statistical modeling and predictive analysis.
Data Processing: Around 86% of the users who mentioned data processing said that, with a lightweight ETL tool, the solution excels at data wrangling for further analysis.
Data Integration: Citing strong integration with multiple data sources and tools, around 84% of the users said that it works well with big data.
Ease of Use: Approximately 83% of the users who mentioned ease of use said that the platform’s low-code approach, with drag-and-drop functionality, makes the interface user-friendly.
Online Community: The online community is responsive and helpful, according to around 74% of users who discussed support for the platform.
Functionality: With fuzzy matching and join capabilities, the platform is feature-rich and versatile, said approximately 63% of users who discussed functionality.
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Data Analysis: Around 92% of users who reviewed data analysis said that the tool analyzes and displays insights and trend forecasts of transactional data in real time to enable timely decision-making.
Data Processing: Approximately 91% of the users who discussed data processing said that the tool can query large amounts of data due to its in-memory architecture and data compression algorithm.
Data Integration: Around 87% of the users said that the solution migrates data efficiently from a wide range of SAP and non-SAP systems.
Support: Approximately 87% of the users who discussed support said that they are responsive, and online user communities and knowledge bases assist in faster resolution of issues.
Speed and Performance: Citing the tool’s fast runtime, around 76% of the users said that they can perform on-the-fly calculations at very high speeds.
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Cost: In addition to the high cost of licenses, the price of add-ons is limiting, said around 89% of the users who reviewed pricing.
Data Visualization: Around 75% of users who reviewed its presentation capabilities said that with outdated graphics, the platform lags behind other solutions in data visualization.
Performance: The solution is prone to infrequent crashes, especially when processing large amounts of data, as said by 65% of users who discussed performance.
Training: Approximately 54% of the users who reviewed learning said that with the documentation not being up to date with latest features, there is a steep learning curve and training is required.
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Pricing: Approximately 80% of the users who mentioned pricing said that the solution’s in-memory architecture demands large amounts of RAM, which adds to the cost.
Functionality: According to around 53% of the users who reviewed functionality, the solution needs to be more flexible and agile to perform complex calculations on large datasets.
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Alteryx is a data science solution that leverages the power of AI and ML to blend, parse, transform and visualize big business data to promote self-serve analysis of business metrics. Many users who reviewed data analysis said that the tool performs statistical, spatial and predictive analysis in the same workflow. Most of the users who reviewed data processing said that, with a lightweight ETL tool, the platform has strong data manipulation and modeling efficiencies, though some users said that it can be tricky to use SQL queries. Citing integration with Power BI, Tableau and Python, most of the users said that the tool connects seamlessly to data from databases and files, apps, and third-party data sources, among others, to expand the reach of search-based and AI-driven analytics. Most of the users who discussed ease of use said that the tool is intuitive with drag-and-drop functionality and a well-designed interface, though some users said error handling can be challenging for automated workflows. Most of the users who reviewed support said that online communities are helpful in providing answers to queries. Citing automated workflows, many users said that the tool helps save time, though some users said that these can be overly complex and need improvement. On the flip side, many users who reviewed pricing said that its expensive licenses and add-ons are cost-prohibitive, and cost per core is high for enterprises looking to scale. A majority of users who reviewed its visualization capabilities said that they need to export data to visually stronger applications, such as Tableau or Power BI, to make the reports presentation-worthy. Citing slow runtimes when executing complex workflows, especially with large datasets, many users said that performance-wise, the solution is prone to infrequent crashes. Most of the users who discussed learning said that with documentation not being in sync with latest releases, training is a must to optimally use the tool. Overall, Alteryx is a data science tool that, with its low-code approach and strong data wrangling capabilities, makes the journey from data acquisition to data insights seamless and promotes data literacy across organizations, though it might be better suited for medium- to large-sized organizations.

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SAP HANA is a multi-model database and analytics platform that combines real-time transactional data with predictive analytics and machine learning capabilities to drive business decisions quicker. Most of the users who mentioned analytics said that, with its Online Analytical Processing(OLAP) and Online Transactional Processing(OLTP) capabilities, the tool analyzes data faster with predictive modeling and machine learning. Many users who reviewed data processing said that the tool has a lean data model due to its in-memory architecture and columnar storage capabilities, and, paired with its compression algorithm, can perform calculations on-the-fly on huge volumes of data. In reference to data integration, many users said that the platform connects seamlessly with both SAP and non-SAP systems, such as mapping tools like ArcGIS, to migrate data to a consolidated repository, though quite a few users said that integration with media files and Google APIs is tedious. Most of the users who reviewed support said that they are responsive, and online user communities and documentation help in resolving issues, whereas some users said that the support reps had limited knowledge. A majority of the users who reviewed its speed said that the platform has a fast runtime, though some users said that it requires high-performing hardware infrastructure to do so and that memory management might be tricky with large datasets. The software does have its limitations though. Being in-memory, the tool is RAM-intensive, which can add to the cost of ownership, though some users said that data compression reduces the database size and saves on hardware cost. A majority of the users who reviewed its functionality said that it needs to be more mature in terms of flexibility and agility, though some users said that with easy updates and maintenance, it is a robust solution and increases efficiency and productivity. In summary, SAP HANA serves as a single source of truth for analysis of large volumes of data and uncovering consumer insights through planning, forecasting and drill-down reporting. However, it seems more suited for large organizations with complex data types and analytics workflows because of its costly pricing plans.

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