What Makes Us Different
Software companies who are part of the AWS ecosystem have undoubtedly heard about Quicksight, Amazon’s in-house, SaaS-based analytics service. But when choosing an analytics solution for embedded, distributed or software development integration, it’s important to understand Quicksight’s strengths and weaknesses and which use cases it is best suited for.
Quicksight is marketed as a analytics service that allows individuals to create charts, reports and dashboards and perform basic self-service analytics. In these areas, Quicksight is a solid contender, on par with the rest of the industry. In recent years, Amazon has added some developer-friendly functionality, including a basic API to embed charts into other applications. These features have allowed Quicksight to appear more suitable for some enterprise use cases, but the service still has many critical architectural shortcomings surrounding embedded, distributed and software development applications.
Embedded use cases are complex and require analytics solutions that are built with their specific needs in mind. They require solutions that are flexible, scaleable and powerful enough to meet their needs today but also to meet the needs they might have tomorrow. Without the right architecture, SaaS offerings will never meet all of these needs. Embedded and distributed analytics also require a business and licensing model that understands the unique needs of embedded use cases. Even the right set of features can be all wrong if they come with restrictive licensing terms or onerous per-user costs.
for Software Companies
As a SaaS product, Quicksight is not SDLC compliant and offers only a single production environment for all users. It does not have the ability to create staging or development environments, and instead forces everyone to build, update, test and deploy their analytics from the production environment.
Qrvey is SLDC compliant, with the ability to create separate development, testing and staging environments as well as multi-tenant environments as needed.
All Data Accepted
Quicksight has limitations on the overall size and types of data in your deployments. It is limited to 1,000 fields per table and 100 million records per dataset. Additionally, Quicksight only works with structured data sources and cannot process semi- or unstructured datasets. Most important however is that Quicksight does not currently offer the ability for incremental data loads. All of your data must be reloaded, in its entirety, with every update.
Qrvey is an enterprise-grade data platform that has adopted an “all data accepted” philosophy. With Qrvey, there are no limitations on your data because we deploy directly into your existing AWS account where your data already resides. Qrvey has the ability to collect, transform and analyze all types of data, including semi- and unstructured data sources with the performance and robustness you expect from an enterprise data solution.
Quicksight is merely a data visualization tool that only offers users the ability to build charts, reports and dashboards.
Qrvey, however, is an all-in-one analytics platform that includes data collection, transformation, analysis, automation and actions. Qrvey allows users to build robust analytics applications and complete data pipelines for individual, self-service and embedded use cases.
Built For Embedded
Quicksight is a SaaS application that was not built for embedded uses. It has no whitelabeling capabilities and is licensed on a per-user basis.
Qrvey was built from the ground up with a B2B2B mindset. It includes robust APIs and pre-built UI widgets for white-labeling and embedded use cases. Qrvey deploys into your existing AWS account, ensuring data privacy, security and compliance with your existing rules and requirements. And Qrvey is licensed so that it will grow with your business.
Quicksight vs. Qrvey Comparison
|Architecture||Managed SaaS architecture running on AWS||Cloud-native microservices deployed to your AWS|
|Data Sources||AWS-data sources including RDS, Redshift, S3 and standard databases. Limitations on data size and no incremental data loading||All data accepted including unstructured, semi-structured, nested, hierarchical and databases|
|Use Cases||Visual analysis, data science exploration, dashboards||All-in-one platform for custom analytics, self-service dashboards and embedded data automation|
|Data Collection / |
|Data connectors with limited data manipulation during loading plus||Built-in data collection, transformation and loading capabilities in our all-in-one platform|
|Performance and Scale||SaaS model, internal SPICE data engine that is in-memory processing of data||Fully Elastic and cloud-scale that works with multi-TB data sources with sub-second response times and auto-scales to any number of users|
|Dashboards||Support for standard charts, metrics, and data visuals||Fully embeddable and actionable charts, metrics, and visuals|
|Mobile / Responsive||Mobile apps available for download in iOS and Android stores for viewing outputs||Designed to be 100% responsive with outputs running automatically on mobile and touch devices|
|Embedding & White Label||Basic embedding of outputs, no whitelabel of the build experience||Fully embeddable with full support for white-labeling|
|AWS Cloud||SaaS application running on AWS available through AWS services||100% AWS cloud-native with microservices, deployed into your AWS account|
|AI & ML||Works with AWS Sagemaker created models||Platform includes AWS AI and ML services directly with no coding required|
|Automation and Workflow||Limited to emailing report snapshots on day, week or month schedules||Full self-service data-driven automation with conditional logic, actions and notifications that can be embedded or stand-alone|
|License Model||Pay per session, User-based licensing for authors and readers. Not licensed for embedded use cases||Unlimited users to encourage user adoption with licensing based on data size. Perfect for embedded uses cases|