Unified scheduling of end-to-end pipelines
A massive upgrade has been made to our scheduling feature. In DataPrep 1.0, the sources and destinations had separate schedules which had to be manually managed by staggering the schedule timing, which was hard. But with 2.0, the scheduling can now be set up at the pipeline level. A single unified schedule can be used to manage imports, processing, and exports for all sources and destinations within the pipeline.
Monitor jobs with ease
With automated pipelines, data transfer happens around the clock. Sometimes, there can be issues with fetching data, and the data transfer can fail. But with jobs, you can easily monitor failed jobs with granular details and identify the points of failure. This ensures your organization doesn't miss out on any valuable data.
Comprehensive version history
Maintain a complete audit trail with version history. Working with data can be challenging; but with the help of this feature, you can easily track changes and run experiments to get more out of your data. Not happy with your experimental changes to data? Easily switch back to the previously published version with the click of a button.
Backfill your data effortlessly
There can be outages in the data pipeline, this can be due to source or destination failure, changes in the data or model requiring changes in the preparation steps, and can take a while to be restored. However there is no easy way to catch up on processing the data during the outage, to solve this we are now introducing the Backfill feature. The backfill feature can be used to fetch data and simulate a scheduled process for a selected period of time. For example, if there was an outage of 2 days on a pipeline that was setup to run every hour. With backfill, you can now create a configuration to process all that data by simulating the schedule process for the missed two days, this will create 48 new jobs that will run in sequence and process the missed data for two days.
Efficient row processing with incremental fetch
In every scheduled run, choosing the incremental fetch option fetches only the new and updated data from the source for processing. This was available for few sources in 1.0, but we're expanding the support to all data sources in 2.0. Incremental fetch job runs faster, compared to full fetch as only the newly added rows are processed, also making it more cost effective.
Transforms made simple with OpenAI
Our new transforms, powered by OpenAI, help generate complex formulas based on logic written in simple English or transform data based on the sample output given. This simplifies the data preparation process and makes Zoho DataPrep more accessible, even for organizations that don't have a dedicated data analyst team.
Create instant pipelines with templates
We built DataPrep with a vision of helping businesses, big or small, have better access to all their business data—but we understand that the data game can be tricky when you're starting out.
To keep our vision alive and to help everyone start using their business data better, we've built a template gallery based on various business use cases that can easily be used to create a pipeline architecture. Just click on the template to add the formula instantly and transforms needed for the data workflow.
You can also save your own data pipelines as templates to make recreating pipelines easier or to apply the data pipeline over a different set of data. You can also share the data pipelines with your organisation or export them as files to share across different organisations.
New dashboard showcasing your pipeline status
Our new and improved dashboard shows the status of all your pipelines instantly. Easily monitor your scheduled runs, identify failed jobs, and ensure your data is up-to-date in your system.
Make data movement easier with other business applications
We are expanding the list of data connectors supported in Zoho DataPrep and as a start we have released the Zoho Creator integration.