Release Notes

Query Timeout helps you abort any query that exceeds your timeout limit and takes too much time and resources.

However, previously in Holistics, the Query Timeout setting was only available for Postgres, Redshift, and MySQL databases. Understanding the importance of resource optimization - especially in our current economic climate - we’ve gradually expanded our support for Query Timeout in most SQL databases - making it also available for BigQuery, Snowflake, Microsoft SQL Server, ClickHouse, and AWS Athena.

Learn more here.

If you have any feedback for us, we're all ears.

We totally get it - feels like time's standing still when you wait for a dashboard to load.

We hate the loading screen as much as you do so we’ve been rolling out a new API - Dashboard Preload API - to help you preload the cache of your most frequently used or operationally critical views of your Dashboards so that they can be loaded in an instant.

This means no waiting time - not even enough for a quick coffee break :) Learn more here.

What do you think? Feel free to share your feedback and suggestions here, we'd really appreciate it.

Period Comparison was a hit with a lot of our users as it allows anyone to get insights into how data fluctuate over time. However, previously in Holistics, Period Comparison could only be set up by data analysts. Viewers don’t have the option to turn it on/off freely.

Recognizing the inconvenience and the growing demands for a more unified experience, we took a step forward: Period Comparison is now available at the dashboard level.

Dashboard explorers can now choose to apply the same comparison to numerous widgets within a dashboard and get a bird-eye view into how multiple metrics are changing over time.

What do you think? Feel free to share your feedback and suggestions here, we'd really appreciate it.

Is there a way to customize what fields in the model and dataset are being shown to end users for self-serve exploration? Otherwise, my users are overwhelmed by the sheer amount of fields available.”

Yes, we heard you. Introducing dataset views, where you can curate exactly what the end users will see.

This feature is now immediately available to all 4.0 customers. Learn more here.

What makes a good coding IDE? Among other things, it should tell you where your code got wrong, and how to fix them.

Well, we just made our coding IDE a little better: When doing analytics development, Holistics IDE now tells you where your modeling code is broken, and what exactly is wrong with them.

If you have any feedback for us, we're all ears.

With an expanded range of data formats for both visualization settings and modeling, presenting dates the way you like - has never been easier.

What do you think? Feel free to share your feedback and suggestions, we'd love it!

Say goodbye to maintaining dashboards with only gut feeling - Because Holistics just released the BI equivalent of God-eye from Fast and Furious 07.

This feature gives admins a bird-eye view of dashboard usage & user activity, making it a lot easier to understand how your dashboards are being used.

Dashboard maintenance will be much less painful when you know precisely which dashboards are used by whom and how often.

In this official launch, we’ve released a plethora of upgrades to our Usage Monitoring - including Lists of dashboards with no view, Tracking of Private Dashboards, Performance improvements, and many more…

You can read more about it here: 🎬 Usage Monitoring: Beta Program Ending & Official Release | Holistics Community

With Dashboard Metadata, you can quickly glance over the dashboard descriptions and know who made it, when the last edit was made, or who frequent viewers are, and more. We hope this will help you foster better cross-functional collaboration by providing more visibility into how your organization creates and shares reports.

Your can learn more about this feature here: Dashboard Metadata | Holistics Docs, or share your feedback with us in our community post.

Holistics now allows pasting a list of CSV values directly from clipboard into the filters. This seemingly small upgrade is much requested by our users. Small details matter.

How does this small feature help?

• Marketing sent you a CSV with email addresses, asking to see if any of them has made a purchase? Open Holistics reports, copy, paste.
• Ops need to double checking a list of order IDs to see if they’re all delivered? Simply paste the list of IDs in the right report.

Learn more here: Mass input a list of values for Filter & Condition

AQL Expression is the next-gen upgrade to our current Business Calculation mechanism, designed to give you a more powerful and flexible way of querying data for various analytics purposes, including but not limited to:

• Cross-model Calculation - Perform calculations across multiple models without having to manually merge the data via SQL Model.
• Nested Aggregation - Create more complex calculations by nesting functions and aggregations within each other.
• Add Filtering in an AQL Expression - Filter your data at the query level rather than having to do it in a separate step.
• Support Date/Time Intelligence Functions - Analyze your data over time using powerful date/time intelligence functions.
• Subgroup Calculation - Perform calculations on subgroups of your data without having to create a separate query for each subgroup.

And so much more! To illustrate AQL’s capabilities, let’s go over these 02 examples: Sub-group Calculation, and Cross-model Calculation.

01. Cross-model Calculation​

Let’s say you are a Country Manager of an Ecommerce Company and you want to calculate the Revenue by Country.

The formula to calculate it is the sum of the product price (from the Product model) multiplied by the quantity (from the Order Items model).

They are from 2 different models so you can write an AQL expression like the video below

Sample syntax

sum(order_items.quantity) * 1.0 /
  eval(sum(order_items.quantity),exclude(users.age_group))

02. Sub-group Calculation​

With AQL Expression, you can perform Subgroup Calculations flexibly and easily without having to create SQL (or transform) Models.

Here's the sample syntax:

sum(order_items.quantity) * 1.0 / 
eval(sum(order_items.quantity), exclude(users.age_group))

Learn more here: [Upcoming] Our brand new Analytics Query Language, AQL!