In the recent update to Cube Cloud, we focused on the workspace and introduced or updated the tools for building data models and optimizing query performance: Query History and SQL Runner. On top of that, we added a few visual touches to make the time you spend in the Cube Cloud workspace more enjoyable.
All features in this update are available in Cube Cloud for users on all tiers, including free development instances.
In Cube Cloud, we strive to provide a complete set of tools for building and operating semantic layers. We also know that authoring data models is a thorough and iterative process: exploring the data, defining a metric, reviewing the result, and repeating until ready. Now, with SQL Runner, you can perform all these steps within the Cube Cloud workspace.
Available from the side menu, SQL Runner allows running arbitrary SQL queries against any of configured data sources. You can explore the database schema and preview raw data in SQL Runner, author the data model in Cube IDE, and run queries in Playground to validate the result. If needed, copy the generated SQL query from Playground to SQL Runner or find the query in Query History and click Open in SQL Runner. Of course, you’re still free to use your favorite Worksheets in Snowflake or SQL Workspace in BigQuery; however, SQL Runner might be a great alternative when working in the Cube Cloud workspace.
Here’s how SQL Runner works:
Sometimes, data source configuration is dynamic and depends on the incoming query and its security context; for instance, when multitenancy is used. SQL Runner will recognize pre-configured security contexts and also let you provide a custom one. Conveniently, Cube Store is available for querying as yet another data source, so you can inspect the contents of pre-aggregations.
With Cube, you can build fast and responsive data experiences for end users. To support that, we’ve released the updated Query History in Cube Cloud, available from the side menu and formerly known as Queries. Every query is analyzed and visualized in Query History with charts, performance metrics, and specialized representations like flame graphs to help you with the following tasks: Getting a high-level understanding if your queries run fast enough. Finding those ones that require optimization and fine-tuning. Understanding the root cause and performing optimization.
Query History provides visibility both into incoming queries (e.g., from BI tools or data applications) and queries that Cube subsequently runs against data sources. You can watch queries live or review the last 72 hours of historical data; analyze the high-level picture or drill down into a particular query.
See Query History in action:
Frankly, even with the best tooling available, analytical query performance is an exceptionally complex topic. In a few weeks, we’ll be hosting a webinar to demonstrate how Query History and other Cube Cloud features can be leveraged to help you analyze and optimize query performance. Register today.
Renaming tabs in Playground
If you’ve ever run queries in Cube Cloud, you probably noticed that Playground preserves open query tabs so you can resume your work when needed. However, it’s not very evident that you can also double-click a query tab to give it a meaningful name instead of something like “Query 2.”
This way, queries to your semantic layer will be even more semantic 😜
This feature would make the Cube Cloud workspace more enjoyable for some of you—and make no difference for the rest. Now, you can enjoy the dark theme that can be configured to match system settings or be always on.
However, we’re not leaving you in the dark 🙃
What’s next in Cube Cloud
We’re focused on making the Cube Cloud workspace even more powerful. So, please stay tuned for the upcoming updates that we’ll be publishing in the Changelog.
You can try Query History, SQL Runner, Playground, and the dark mode today. Sign up for Cube Cloud for free and see them in action.
We’re always happy to hear your feedback. Please don't hesitate to get in touch or reach out in our Slack community of more than 8,000 data practitioners.
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