ANOW! Automate integrates with dbt (Data Build Tool) to orchestrate and monitor data transformation workflows, supporting both dbt Core and dbt Cloud instances. This enables unified control over dbt runs, models, and tests, providing native access to run status and execution logs, ensuring data freshness and audit readiness.
The dbt (Data Build Tool) integration with ANOW! Automate facilitates robust data transformation directly within databases by enabling data analysts and engineers to write modular SQL code. ANOW! Automate extends this capability by providing comprehensive orchestration and monitoring for dbt workflows. It supports self-managed dbt Core instances for local project execution and dbt Cloud integrations for cloud-managed platforms, providing a single point of control across diverse data transformation environments.
ANOW! Automate interacts with dbt by offering distinct task types tailored to each dbt environment. For dbt Core, the integration uses a dbt Core endpoint configured with the path to the dbt command and project, enabling execution of dbt CLI commands such as clean, build, run, and test. For dbt Cloud, an endpoint is configured with the Account ID and API Key, allowing ANOW! Automate to trigger specific dbt Cloud jobs and monitor their status. This dual support ensures flexibility for enterprises with varied dbt deployment strategies.
This integration is designed for data engineers, data platform teams, and IT operations professionals managing complex data pipelines in hybrid IT landscapes. It provides the necessary tools to embed dbt transformations into broader enterprise workflows, ensuring data quality, compliance, and operational efficiency. By orchestrating dbt within ANOW! Automate, organizations can achieve greater control, visibility, and automation across their entire data estate.
Consolidate dbt runs, models, and tests into a single operational dashboard alongside other data estate workloads. This provides native access to run status, test results, and execution logs, improving incident response and providing a unified execution record for audit teams.
Apply DevOps principles to data pipelines, extending dbt's version-controlled and testable transformation logic to cover ingestion, quality, observability, and release management. ANOW! provides end-to-end orchestration, treating dbt test failures as workflow signals.
Trigger dbt runs the moment upstream ingestion completes, a file lands, or a source system signals readiness, replacing rigid time-based scheduling. This ensures transformations operate on the most current validated data, improving data freshness and SLA attainment.
Apply a dynamic SLA model that analyzes historical dbt run statistics, recalculates the critical path, and flags runs at risk of breaching targets. ANOW! executes predefined remediation actions, such as targeted reruns, escalation, or fallback logic.
Use Cases
DATA ENGINEERING
Manage the entire data flow from ingestion to consumption, embedding dbt as the transformation stage.
DATA GOVERNANCE
Integrate dbt test results as critical workflow signals to ensure data quality and compliance.
FINOPS
Implement cost-aware orchestration for dbt runs to optimize data warehouse resource usage.
DATAOPS
Apply standard CI/CD practices to dbt project changes and surrounding orchestration workflows.
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ANOW! Automate uses dedicated endpoint and task types for both. For dbt Core, it connects to your self-managed instances via local paths. For dbt Cloud, it uses your Account ID and API Key to interact with the cloud-managed platform, ensuring flexible deployment options.
For dbt Core, ANOW! Automate supports username and password authentication, which can be manually entered or pulled from Secret Vault. For dbt Cloud, authentication is handled via an API Key generated within your dbt Cloud account.
Yes, a single template workflow can serve multiple environments or tenants by dynamically computing dbt variables, targets, and selectors. This is achieved by pulling configurations from tables, file metadata, or preceding task outputs, reducing maintenance effort.
Unified monitoring consolidates dbt run status, test results, and execution logs into a single operational dashboard. This provides a comprehensive, unified execution record for audit teams, demonstrating adherence to processes and data quality throughout the transformation pipeline.
Yes, data engineers can wrap approved dbt jobs in integration templates with safe parameters. Analytics and business users can then launch these templates through a self-service interface, schedule runs, and receive notifications within IT-defined security and cost boundaries.
ANOW! Automate orchestrates critical workloads across Google Cloud services like Compute Engine and BigQuery, unifying operations with your hybrid IT landscape. We enable granular control over VM lifecycles and data pipelines, ensuring cost discipline and auditable compliance across your Google Cloud estate without custom scripting.
ANOW! Automate on Amazon Web Services (AWS) provides enterprise-grade workload automation and orchestration, consolidating diverse AWS jobs, container services, and data pipelines into a single operational dashboard. This gives you unified visibility and control across hybrid and multi-cloud environments, helping you meet up to 99.95% SLA for critical business processes.
ANOW! Automate extends Kubernetes' native scheduling, providing an essential orchestration and control plane that transcends cluster boundaries. It enables event-driven responses, creates and manages containers on demand, and executes critical business applications within pods, optimizing resource utilization across hybrid environments.
ANOW! Automate provides deep, native integration with Microsoft Azure, offering a single, governed control plane that orchestrates mission-critical workloads across Azure services and the broader enterprise IT estate, ensuring unified monitoring and control with event-driven precision.
ANOW! Automate integrates natively with Microsoft Teams, establishing bidirectional communication between orchestrated workflows and the collaboration platform. This allows real-time notifications, approvals, and operational commands to flow directly within Teams, significantly reducing mean time to detection and improving collaborative response without switching tools.
ANOW! Automate integrates with Amazon EMR to orchestrate big data processing workloads, automatically provisioning and scaling clusters for data pipelines. This integration ensures that complex data transformations execute efficiently, optimizing resource utilization and accelerating time-to-insight for data-driven enterprises.
ANOW! Automate orchestrates Azure Databricks workloads, enabling unified monitoring, event-driven job execution, and cost governance across hybrid cloud environments. We provide tasks for managing clusters, running jobs, and monitoring activity, ensuring predictable Azure spend and enterprise-grade SLA protection for critical data pipelines.
ANOW! Automate integrates with Azure Data Factory (ADF) to provide unified orchestration and control over your data pipelines within hybrid environments. This integration allows ANOW! Automate to trigger and execute both Azure Data Factory Triggers and Pipelines, consolidating fragmented data operations into a single operational dashboard for faster incident detection and consistent status views.
