Mage launched a new backfill framework to make backfills a lot easier. User can select a date range and date interval for backfill. Mage will automatically create the pipeline runs within the date range, and run them concurrently to backfill the data.
Docs
- Backfill framework overview: [https://docs.mage.ai/orchestration/backfills/overview](https://docs.mage.ai/orchestration/backfills/overview)
- Backfill guide: [https://docs.mage.ai/orchestration/backfills/guides](https://docs.mage.ai/orchestration/backfills/guides)
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Data integration
New sources
- [Microsoft SQL Server](https://github.com/mage-ai/mage-ai/blob/master/mage_integrations/mage_integrations/sources/mssql/README.md)
- [Outreach](https://github.com/mage-ai/mage-ai/blob/master/mage_integrations/mage_integrations/sources/outreach/README.md)
- [LinkedIn Ads](https://github.com/mage-ai/mage-ai/blob/master/mage_integrations/mage_integrations/sources/linkedin_ads/README.md)
- [Paystack](https://github.com/mage-ai/mage-ai/blob/master/mage_integrations/mage_integrations/sources/paystack/README.md)
- [Postmark](https://github.com/mage-ai/mage-ai/blob/master/mage_integrations/mage_integrations/sources/postmark/README.md)
New destinations
- [Trino](https://github.com/mage-ai/mage-ai/blob/master/mage_integrations/mage_integrations/destinations/trino/README.md) (all connectors)
Streaming pipeline
Add Kinesis as streaming source and destination(sink) to streaming pipeline.
- For Kinesis streaming source, configure the source stream name and batch size.
- For Kinesis streaming destination(sink), configure the destination stream name and partition key.
- To use Kinesis streaming source and destination, make sure the following environment variables exist:
- `AWS_ACCESS_KEY_ID`
- `AWS_SECRET_ACCESS_KEY`
- `AWS_REGION`
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Kubernetes support
- Support running Mage in Kubernetes locally: [https://docs.mage.ai/getting-started/setup#using-kubernetes](https://docs.mage.ai/getting-started/setup#using-kubernetes)
- Support executing blocks in separate Kubernetes jobs: [https://docs.mage.ai/production/configuring-production-settings/compute-resource#kubernetes-executor](https://docs.mage.ai/production/configuring-production-settings/compute-resource#kubernetes-executor)
yaml
blocks:
- uuid: example_data_loader
type: data_loader
upstream_blocks: []
downstream_blocks: []
executor_type: k8s
...
- When managing dev environment in Kubernetes cluster, allow adding custom config for the mage container in Kubernetes.
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DBT improvements
- Support running MySQL DBT models in Mage.
- When adding a DBT block to run all/multiple models, allow manual naming of the block.
Metaplane integration
Mage can run monitors in Metaplane via API integration. Check out the [guide](https://docs.mage.ai/integrations/metaplane) to learn about how to run monitors in Metaplane and poll statuses of the monitors.
Other bug fixes & polish
- SQL block: support SSH tunnel connection in Postgres SQL block
- Follow this [guide](https://docs.mage.ai/integrations/databases/PostgreSQL#ssh-tunneling) to configure Postgres SQL block to use SSH tunnel
- R block: support accessing runtime variables in R block
- Follow this [guide](https://docs.mage.ai/guides/r-blocks#runtime-variables) to use runtime variables in R blocks
- Added setting to skip current pipeline run if previous pipeline run hasnβt finished.
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- Pass runtime variables to test functions. You can access runtime variables via `kwargs['key']` in test functions.
View full [Changelog](https://www.notion.so/mageai/What-s-new-7cc355e38e9c42839d23fdbef2dabd2c)