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What’s New

Qrvey 9.1
Qrvey Version 9.1 is now available! This release introduces numerous features, enhancements, bug fixes, and performance improvements.
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Qrvey 9.0
Qrvey Version 9.0 is now available! This release introduces multi-platform hosting (Azure & AWS), a redesigned Dashboard, extensive widget customizations, and numerous features, enhancements, bug fixes, and performance improvements.
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Qrvey 8.8
Qrvey Version 8.8 (LTS) is now available to customers! This version supports FIPS for GovCloud and includes tons of bug fixes and performance improvements.
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Qrvey 8.7
Version 8.7 of the Qrvey platform is now available to customers! This version includes new features including area charts, the ability to pivot and export data, as well as numerous bug fixes and performance improvements.
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Qrvey 8.6
Version 8.6 of the Qrvey platform is now available to customers. This version includes several new feature enhancements and performance improvements.
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Required Update for 8.5.1
Attention 8.5.1 customers: for any 8.5.1 instance deployed prior to 08/05/2024, an update is required to ensure you are running the latest images.
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Qrvey 8.5
Version 8.5 (LTS) of the Qrvey platform is now available to customers. This version includes several new features and performance improvements.
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End-of-life Schedule
We've added a new article that lists the features and endpoints that have been scheduled for deprecation. All features and endpoints will be supported for (1) year after the release date of the LTS version that contains the alternative.
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Version: 9.1

Intro to Datasets

A Qrvey Dataset is a collection of structured data used to create dashboards and visualizations within the platform.

Datasets

Types of Datasets

There are four types of datasets, each with a different use-case.

  • Managed Datasets: Store data inside Qrvey’s Elasticsearch-based repository.

    • Data is imported and indexed within Qrvey, making it available even if the original connection is offline.
    • Supports data transformations, scheduled imports, and full indexing.
    • Enables fast performance for dashboards, filters, aggregations, and large-scale analytics.
    • Great for any size of dataset, that does not require real-time updates.
    • Common use cases: reports, analytics, etc.
  • Dataset Views: A materialized view of a managed dataset.

    • Points to a Managed Dataset (can be in another application), without duplicating data.
    • Cannot alter or add to the source data (cannot be transformed, no joins/unions, etc.)
    • Can superficially hide/redact data (e.g., hide columns), set RLS & CLS differently, etc.
    • Common use case: Simplifies dataset design in commingled datasets.
      • Inject tenant-level filtering / security without duplicating data.
      • Maintain one managed dataset, then create dataset views with custom-tailored columns & permissions for different applications/users.
      • When you synchronize the main dataset, all the dataset views are synced as well.
  • Base Datasets: Similar to managed datasets, but data is stored as a file in S3. Only used as a data source within other Managed datasets.

    • Used to speed up joins in managed datasets: joining base datasets is faster than managed datasets.
    • Offers fewer features than managed datasets, due to limitations that arise from being stored in S3 files.
    • Not recommended to create base datasets from existing managed datasets, should be sourced from a Connection.
    • Can be joined with any other dataset type.
    • Common use cases: Cannot be used directly for visualizations; intended for use as join sources within managed datasets.
  • Live Connect Datasets: Provide direct, real-time access to data at its source (e.g., SQL, REST API).

    • Qrvey runs live queries at runtime; no data is stored or indexed in Qrvey.
    • Ideal for real-time or near real-time data needs.
    • Performance depends on the source system and may be slower for complex queries.
    • Joins, Unions, and Formulas are not supported. These must be performed in the source.
    • Limited support for advanced analytics on large tables.
    • Works with: PostgresQL, Amazon Redshift, Snowflake, and Databricks.
    • Performance depends on the source database design, including: partitioning, indexes and keys, monitoring source for concurrency, auto-scaling, etc.
    • Common use cases: real-time dashboards, always-fresh operational data.

Create a Dataset

  1. Navigate to the Data Module and make sure you are in the Datasets tab.
  2. Click Create New Dataset. A dropdown will appear. Choose to create a:
    • New Managed Dataset
    • New Live Connect Dataset
    • New Base Dataset
  3. Configure as Desired.

Configure a Dataset

To manage a dataset, follow these steps:

  1. Navigate to Data > Datasets.
  2. Click on the desired Dataset.
  3. Configure and Manage the Dataset as needed.

You'll find configuration options under three main tabs: Design, Analyze, and Activity Log.

Design

Once you've created a dataset, you can customize it as desired under the Dataset > Design tab.

Dataset Design

Analyze

The Analyze tab provides a suite of tools for analyzing datasets. These visualizations may be added into Dashboards and embedded as widgets.

Summary View

1_summary-view

Every dataset connected to Qrvey generates a Summary View, offering a quick overview of your data. Access it by selecting a dataset and clicking the Analyze tab.

Each column/field is displayed with relevant summary statistics. Numeric fields are visualized using bar charts, while other field types may have different visual representations.

Options in the panel's top-right menu: Download, Apply Filters, Formulas, and Buckets.

Tabular View

1_tabular-view

The Tabular View presents data in a familiar spreadsheet format.

Options in the panel's top-right menu: apply Filters, Formulas, and Buckets.

More Options:

  • Filter, Sort, and Show/Hide columns
  • Drag to reorder or resize columns
  • Apply aggregate functions (via the three-dot menu in column headers)
  • Numeric fields offer multiple aggregation options, with results displayed in the column footer.

Custom View

2_custom-view

Custom View treats the Analyze area similar to a Dashboard to create custom visualizations.

Steps:

  1. Navigate to the Analyze tab and select Custom View.
  2. Click Add Chart to open the Chart Builder.
  3. Click on the chart's 3-dot menu and use the "Size" option to adjust its size.

Options in the panel's top-right menu: Edit, Download, Duplicate, or Delete.

Metric View

5_metric-view

The Metric View highlights key performance indicators using various visual formats.

To create a metric:

  1. Navigate to the Analyze tab and select Metric View. The [Chart Builder](../07-Charts/the-chart-builder.md will open.
  2. Choose a metric type: Indicator, Bullet, or Gauge.
  3. Drag data into the fields shelf or canvas and style as needed.

Activity Log

Each Dataset gets its own Activity Log.

Activity Log

Browse Datasets

  1. Navigate to Data > Datasets to browse all Datasets within the application.
    Each dataset will be displayed as a card, displaying the following details:
    • Name — The name assigned to the Dataset.
    • Type — Notes if it a Managed or a Live Connect Dataset.
    • Statusactive (ready to create visualizations), draft (dataset is still loading), or failed (dataset is not properly loaded).
    • Last Loaded — The date that the data was loaded.
    • Data Sync — Notes if Data Sync is On or Off.
    • Data Source — The Connection used by this Dataset.
    • Records — Number of records within the dataset.
    • Columns — Number of columns within the dataset.

Mark Dataset as Favorite

To mark a Dataset as a favorite, follow these steps.

  1. Navigate to Data > Datasets.
  2. Find the desired dataset card.
  3. Click the star in the upper-right corner.

Rename a Dataset

  1. Navigate to the desired dataset card.
  2. Click on its name.
  3. Type to change it as desired.

Rename a Dataset

Share and Un-Share Datasets

By default, datasets are only accessible in the application in which they are created.

  1. Navigate to Data > Datasets.
  2. Find the desired Dataset and click its three-dot menu. A dropdown will appear.
  3. Click either Share Data with my Organization or Unshare with my Organization. A modal will appear.
  4. Click to confirm your choice in the modal and you will return to Data > Datasets.

Note: If unshared, any existing datasets and/or dataset views created from this dataset will no longer be available to users building charts and metrics. Any existing charts and metrics that have a dependency on this dataset will no longer show any data.

Delete a Dataset

  1. Navigate to Data > Datasets.
  2. Find the desired Dataset and click its three-dot menu. A dropdown menu will appear.
  3. Click Delete. A modal will appear.
  4. Click Delete to confirm your choice in the modal and you will return to Data > Datasets.