Maximize Pipeline Throughput and Data Quality with Acceldata + Airflow
Gain full visibility into your data pipelines with continuous monitoring and upstream-downstream lineage tracking.
Problems addressed by this integration
- Struggle to manage and trust critical data as assets grow across diverse technologies.
- Face challenges ensuring data reliability, leading to decision errors and compliance risks.
- Lack real-time data quality insights, increasing the risk of using low-quality or non-compliant assets.
Overview
The Acceldata-Airflow integration optimizes pipeline performance and throughput while ensuring data quality across the DAG.
With the Acceldata-Airflow integration:
- Analysts and Data Scientists: Gain comprehensive visibility into data pipeline health, ensuring timely access to high-quality data for analysis.
- Business Users: Monitor key data flows and performance metrics, for informed, timely decisions based on reliable data.
- AI/ML Teams: Track and optimize the flow of data across AI/ML models, ensuring data integrity and improving model accuracy.
- Data Engineers: Integrate automated monitoring and lineage tracking into Airflow for enhanced troubleshooting.
- Governance and Compliance Teams: Track data lineage and full traceability to ensure compliance with regulatory standards.
Use Cases
Provides visibility into the entire data pipeline, monitoring task execution, failures, retries, and dependencies for a comprehensive view of Airflow DAG performance.
Automatically visualize data and issue lineage, ensuring integrity, clarity in data flow, and quick identification of root causes.
Automate data quality checks at key stages to prevent errors, minimize downstream impacts and cascading costs, and ensure reliable data.
Detect quality, performance issues, and anomalies, surface them to the right people, and resolve with insights into root causes and lineage.
Features
- Pipeline Lineage: Visualize task dependencies and execution flow within your Airflow DAGs for complete pipeline tracking.
- Data Lineage: Automatically reconstruct data transformations and movements, providing a full view of how data flows through systems.
- Real-Time Monitoring: Track task execution times, success/failure rates, and resource consumption for proactive management.
- Automated Data Quality Checks: Validate data at each stage of the pipeline to ensure consistency and accuracy.
- Custom Alerts: Set up alerts based on task performance and data quality to catch issues early.
- Issue Tracking and Root Cause Analysis: Automatically identify issues, trace their root causes, and track lineage to quickly resolve problems.
- Easy Integrations: Simple and comprehensive APIs/SDKs to integrate pipeline and data lineage information into Acceldata using Python and Java.
Specification Requirements
Related assets and information
Ensure Reliable Airflow Workflows with Monitoring Strategies
The Essential Features of Data Pipeline Monitoring Tools
Acceldata integrations
Partner with Acceldata
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