By centralizing pipeline metadata, logs and statuses, IBM® Databand® provides the insights you need to consistently deliver high quality data across your Airflow environments.
Continuous observability within your Snowflake warehouse helps detect data incidents with Snowflake tables related to data quality, freshness and volume and resolve them fast.
Integration of dbt with Databand provides continuous observability across your jobs, tests and models so you can know when a dbt process breaks and how to quickly fix it.
DataStage integration provides continuous observability during the execution of your DataStage jobs to help ensure that you know whenever a job breaks and how to quickly fix it.
Databand provides Spark observability in the context of your broader pipelines so you can detect data incidents earlier and resolve them faster.
To achieve continuous Spark observability and monitoring, Databand features seamless Databricks integration through Spark workloads.
ADF integration provides data observability across your data factories so you can know when your pipelines break and how to fix them quickly.
Microsoft Teams
Custom alerting tools integrations
Scala
Java
Custom coding languages integrations
Deequ
Custom frameworks integrations
Apache Airflow
IBM DataStage
dbt Core and Cloud
Amazon MWAA
Databricks
Snowflake
Amazon Redshift
Google Cloud Storage
MySQL
PostgreSQL
Custom storage and compute integrations
Apache Spark
Databricks
Amazon EMR
Custom transformation integrations