Users will be able to create a new blank DBT transformation and edit it using a command-line interface (CLI).

Orchestrations will be enabled in development branches. Users will be able to create a new orchestration in their branch and run it. However, they won’t be able to set a scheduler and notifications. Having a clear overview of what was merged and when in the development branch will improve the overall user experience.

To complete jobs faster, users will be able to change the size of the DWH backend by selecting from three available options (small/medium/large). They will receive an automatic notification when a particular heavy job might need its backend size increased.

Users will be able to run multiple parallel component configurations, e.g., when downloading multiple tables with one extractor with multiple config rows. They can run any configuration rows of any component with configuration rows in parallel and define the level of parallelism.

In new transformations, users will be able to organize transformations in logical groups. They will be able to create a new folder with a custom name and then move their current or new transformations there.

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Users will be able to set multiple outputs for one table in one transformation, and to create multiple queries within one shared code block. They will also be able to enjoy seamless two-way code synchronization between Jupyter Notebook and Python transformations and many UX enhancements. In addition, when a transformation error occurs, users will be automatically taken to the highlighted problematic query.

Our enterprise customers will be able to import their own Teradata. A new stack with a customer Teradata backend will be created and all basic platform features will be available.

Users will be able to create or copy an existing pipeline (from extracting data and its manipulation, to exporting to the final destination and setting up regular orchestrations) in a user-friendly environment within minutes.

Interactive blue cards on the dashboard will guide users through the whole journey of automating their first pipeline, from setting up an extractor up to their first working orchestration.

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To complete jobs faster, users will be able to change the size of the transformation backend by selecting from three available options (small/medium/large). They will receive an automatic notification when a particular heavy job might need its backend size increased.

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In pay-as-you-go (PAYG) projects, users will have an opportunity to set up selected components (such as the most frequently used data sources) via a newly implemented simplified user interface. Basic and advanced modes will be available.

Users will be able to manage project configurations directly from code using the Keboola CLI. Synchronizing projects to the local environment or GIT repository will allow them to speed up developing and iterating on top of Keboola from their IDE where they have automatic code check, CI/CD pipelines, etc.