To achieve continuous dbt observability and monitoring, IBM® Databand® features seamless dbt integration.
Teams are increasingly using dbt Core and dbt Cloud to quickly deploy analytics code. With interconnected data pipelines becoming more complex and distributed across multiple systems, it can be challenging to track and identify dbt issues before they impact the business.
Integration of dbt with IBM 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.
With Databand observability integration, receive proactive dbt alerts around execution times, test failures, model anomalies and more.
Save engineering time by centralizing metadata and root cause analysis from all your dbt commands under one roof.
By leveraging Databand’s lineage capabilities, you are able to see which tables are impacted across your dbt population.
Integrating Databand’s observability capabilities within dbt Core or dbt Cloud environments works by using one of the following methods:
1. Track dbt with Databand's Python SDK
Use Databand's Python SDK to track either dbt Cloud jobs or dbt Core commands that have been triggered through a Python orchestration tool such as Apache Airflow.
2. Track dbt Cloud jobs using Databand's dbt monitor
Use Databand's dbt Cloud monitor to track jobs by directly monitoring your dbt Cloud account. This allows Databand to track your dbt jobs regardless of how they are triggered.
Databand’s comprehensive set of capabilities helps simplify and centralize your dbt observability.
Leverage the power of Databand’s alerting capabilities to notify your teams of critical issues as soon as they happen. Generate alerts for incidents like failures of dbt commands, individual models or tests and report on duration anomalies for commands, models and tests. Plus, discover anomalous record counts for the tables in your models.
Databand automates the discovery of .sql and .yaml files to help simplify how analytics engineers and analysts access and debug SQL for their models and tests. It streamlines the process of identifying key information like table and schema materialization type, as well as investigating table logic to better understand how certain calculations are derived.
With all dbt commands viewable from a centralized console, save debugging time by quickly reviewing the state and duration of each dbt command. This includes individual dbt models and tests so you can pinpoint the root causes of dbt failures and resolve them fast.