Row-level security gives you precise control over which rows each user can access. This guide walks through advanced row-level security features and practical examples: passing multiple values, numeric and date ranges, handling multiple security groups and datasets in a single dashboard, plus nested Boolean logic. Follow these patterns to implement flexible, secure row filtering in your dashboards.
This video describes how to customize the appearance of Qrvey Composer as well as specific applications to match your organization's branding and styling needs. It demonstrates the Customization features provided in Qrvey Admin Center, which you can use to customize your instance of Qrvey Composer. It also demonstrates the Settings feature and Style Themes feature available in Qrvey Composer, which you can use to customize the appearance of applications. Finally, this video provides an overview of the Themes API.
This guide explores optimization and configuration of the Qrvey platform, covering integrations with Amazon Redshift, Snowflake, Databricks, and Elasticsearch. It provides best practices for each, addressing both Live Connect datasets (real-time queries against your source) and Managed datasets (data ingested into Qrvey’s internal Elasticsearch engine) to help architect optimal solutions. The focus is on conceptual and architectural considerations, performance, and cost trade-offs.
Overview
This video provides an in-depth overview of Column-Level Security and how to configure it. It demonstrates how to create a Role in Qrvey Admin Center, how to use the Get Dataset, Update Dataset, and Apply Dataset Changes APIs to enable Column-Level Security for a dataset, and how to include the Role in a JSON Web Token (JWT).
A Connection is a link established between Qrvey and an external data source.
This video provides an in-depth overview of the Content Deployment feature in the Qrvey Admin Center. It provides an overview of each component of Content Deployment (Servers, Packages, Deployment Definitions and Deployment Jobs), and it demonstrates how to use these components to deploy content to a Qrvey instance. It also describes how to update existing content using the Content Deployment feature.
Content Deployment enables you to copy the contents of a source Qrvey application, then pass it into a new target application.
Content Deployment enables you to copy the contents of a source Qrvey application, then pass it into a new target application.
In Qrvey, Content Deployment refers to the process of copying content from one application to another, in the same environment or to a different one. In a multi-tenant deployment, use Content Deployment to manage the data, content, and generated Qrvey objects.
This video describes how to publish the dashboards that you create in Qrvey Composer so that they are available to end users. It covers both embedded and non-embedded scenarios. Features discussed include Publish, Unpublish, Embed, and Live Preview. This video also describes how to use the Navigation feature in Dashboards, as well as an overview of the appid and pageid parameters.
By customizing the appearance of a Qrvey widgets in your application, you can provide a more native presentation within your product.
Well-designed dataset transformations prevent fragmented insights and keep dashboards truthful. By applying the following techniques to ensure that your charts reflect reality, not accidental string differences.
As a data engineer, you use Qrvey to design, implement, and optimize data solutions tailored to your organization’s business objectives. Data engineers design scalable data pipelines that can integrate different data sources. When deployed, the Qrvey platform helps your teams to manage data efficiently and create advanced analytics.
A Deployment Definition is a set of instructions for a deployment job. It defines the package version to use, which content from the package to include, and whether any of the content has to be modified during deployment. You can configure the deployment definition to create a new application or to update an existing application.
A deployment job executes the type of deployment specified by one or more deployment definitions against one or more target servers. Deployment jobs consist of blocks of instructions, with each block configuring the deployment of one deployment definition to any number of users on one destination server. That enables you to use one deployment job to deploy multiple applications to as many users as necessary, on multiple environments. Deployment jobs require at least one block.
After you've installed the Qrvey Platform, you will want to architect your data pipeline. This article highlights best practices regarding how your data gets imported into Qrvey, optimized for visualizations, and refreshed over time.
This feature is disabled by default and must be enabled by your team admin. For details, see Customize Qrvey Composer.
Introduction
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As an administrator, use the Admin Center's Entity Management tab to manage datasets and data loads.
This video provides an in-depth overview of using Filters in the Qrvey platform, including how to build Filters, the types of Filters available, and how Filters can be configured for a cascading hierarchy.
This video provides an in-depth overview of using Formulas and Buckets in Chart Builder to prepare data for analytics. Formulas enable you to compute values in real-time, and Buckets enable you to create partitions or groupings of data that do not already exist in the database.
This article contains frequently asked questions organized by user persona.
Key takeaways
As a CloudOps engineer, you can set up Google as the OpenID provider usedd for logging into Qrvey.
While you can view general information about your ElasticSearch cluster on the Account Info tab, use the Infrastructure page of the Qrvey Admin Center to monitor and manage its configuration.
Introduction
Qrvey platform supports internationalization and localization for end users in a comprehensive manner. Multi-lingual support is offered for both “static” and “dynamic” text, as well as formatting of numbers and dates based on regions and cultures.
SaaS organizations that want to embed self-service analytics within their core applications to support a multi-tenant deployment should think about the following considerations:
The Qrvey platform is an all-in-one analytics solution that enables SaaS providers to quickly put analytics in the hands of their users.
Metric Comparison Tutorial
This video discusses multi-tenancy architecture of the Qrvey platform for SaaS organizations. Includes a demonstration of a parent application that embeds a Qrvey application in a multi-tenant environment. Topics covered include:
The first step with any self-service embedded analytics implementation is to design, develop and then populate the datasets that will become the sources for all charts, metrics and visualizations created in the platform. SaaS organizations building datasets for multi-tenant deployments of embedded analytics must think about the following topics with respect to the data architecture:
Qrvey’s approach to multi-tenant security architecture is to use the assert model for both authentication and authorization. The assert model effectively allows you to maintain your existing user accounts, roles/groups and permissions, and then declare a user’s identity and permissions to the Qrvey platform dynamically at runtime. The ability to assert/declare a user’s identity and access permissions is favorable when embedding third party platforms in your core application for a multi-tenancy deployment, because it means that you will not need to redefine and replicate your existing security schema.
Before Creators can use the OpenID provider to log in, you must set up your OpenID provider account to integrate with Qrvey.
Within an application's package (objects housed within the source application), you can create a version (snapshot) that contains all of the application content and dependencies. As the source application changes over time, you can use the same package to create subsequent versions.
Whether you're getting started with Qrvey Pro or Qrvey Ultra, use this quickstart guide to go from zero to embedded!
Use the following step-by-step video tutorials to help you get started.
- If you plan to upgrade to a newer version of Qrvey, review the upgrade and release notes for all previous versions, which can contain platform changes that must be accounted for in the development process.
This video provides an in-depth overview of Row-Level Security, which is especially important for multi-tenant environments. It demonstrates how to enable Row-Level Security for the columns in a dataset, and how to configure a widget in code to enforce Row-Level Security. It also describes how to enable Row-Level Security in the Qrvey Admin Center.
What is RLS and When is it Useful?
Use Row-level security to protect and filter rows of data so users only see the records they are allowed to see. This control can help you to manage multi-tenant or role-sensitive applications.
This video describes how to use Security Groups to manage user accounts. Topics discussed include the difference between the two primary users of Qrvey dashboards, Composers and Consumers, and how they relate to a Security Group Model. This video then demonstrates how to create Composers using Qrvey Admin Center and how to create Consumers using Qrvey Composer. It also describes how to use the Qrvey API to programmatically create users and groups.
This video provides an overview of the Security features in the Qrvey platform. Topics discussed include authentication versus authorization, configuring user roles and groups, and configuring role-level security and column-level security.
Within the Content Deployment module, each instance of the Qrvey platform is considered a server. This term is synonymous with “environment”.
Set Dashboard Widget Defaults Tutorial
Single Panel Widget Tutorial
Data Sync pulls new and updated records from your sources, keeping your base and managed datasets current without unnecessary reloads.
Tabular Comparison Tutorial
Tabular Report Tutorial
The Qrvey platform enables end users to display dates and times in their local time zone. The Qrvey platform stores data in UTC/GMT+0 and adjusts the time displayed to the end user based on their preferred settings. The default time zone setting is specified in Qrvey Admin Center, but it can be overridden programmatically. The possible settings are:
Toggle Dashboard Widget Features Tutorial
Embedding Qrvey widgets speeds up delivery and keeps your product focused on core value while offering rich analytics, reporting, and automation capabilities. By means of a small HTML element, a JSON config, and a launcher script, you can add dashboards, analysis tools, scheduled exports, and more, with configurable security and UI controls tailored to your users.
To encrypt and protect sensitive embed configuration and render a Qrvey dashboard in a client environment, generate and deploy a secure token.
This video demonstrates how to use the Automation feature in Qrvey Composer to create a workflow.