Hevo changelog hevodata.com

We have added a highly requested feature that will allow you to read the data from an older date in a Pipeline Job.

It is currently in Beta and hence has been enabled for only some of our users. To check if it is enabled for you, please hit the actions menu for any of the Pipeline Jobs and check if you see “Change Position” option.

In case this is not enabled for your account and you would like to give it a try, just ping us back.

The option to change the pipeline schedule was originally available on the pipeline dashboard. This has now been moved to the Action Menu on the pipeline dashboard.

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For easy management of data in the Replay Queue, we have added a 30-day retention period. After this period lapses, the data will automatically be removed from Hevo.

To give better visibility into failed jobs in a pipeline, we have added the details of the jobs failed in any pipeline on the drawer page. This will help you identify the pipelines that need attention at a single glance.

Activity Logs from your account can be synced with CloudWatch. This will allow you to search or download the logged activity within Cloudwatch. Additionally, this will allow you to take log backups on S3 or even trigger custom alerts as per need.

The pipeline job statuses have been redefined on Hevo. More granular statuses have been added for better and easy visibility on the current state of the jobs.

Read more on Job Statuses here

Hevo has extended support for BigQuery Partitioned tables and allows to direct your data to the desired partition table within BigQuery. This can improve your query performance, and control costs by reducing the number of bytes read by a query.

Documentation: Table Partioning in BigQuery

We have added support to extract data from Additional Fields provided by AppsFlyer. This will help you extract and analyze your AppsFlyer Data better.

Here is the list of fields that we have added support for.

We are excited to bring you Hevo's Data Lake as a Service. This is in Private Beta and is open for guided pilots.

Now, you do not have to bring all your raw data to the warehouse. Using our ready-to-scale Data Lake as a service, you can build your Data Lake from scratch using Hevo, model the data, and only push aggregated data to the warehouse. All of this, in a matter of few minutes.

Previously, Hevo used to suggest Decimal data types to be mapped to BYTES datatype in BigQuery. Now, Hevo suggests BigQuery’s numeric type which the right column type to map the Decimal data type.