Unleash The Power of Airflow
In the era of data-driven decision making, Apache Airflow (Airflow) has emerged as a popular open-source framework among data teams. It offers programmatic authoring, scheduling, and monitoring of complex data workflows, significantly improving data orchestration, ETL/ELT processes, and workflow automation. However, the complexities surrounding its implementation, maintenance, and dependency management often prevent organizations from reaping the full benefits of the Airflow platform.
To overcome this challenge, Qbiz Airflow Solutions present an effective approach to upgrades, migrations, optimizations, and implementations, allowing businesses to enhance their data workflows and deliver the actionable insights their business needs.
The Problem:
Inefficient, Stalled, Or At-risk
Airflow Implementations
Despite the wide support from various cloud and service providers such as Amazon (AWS MWAA), Google (GCP Composer), Microsoft (ADF), and Astronomer (Astro), adopting and implementing an effective Airflow solution comes with numerous hurdles including:
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A lack of understanding regarding the full extent of Airflow’s capabilities and applications that can be applied to meet workflow requirements. Complexities in installation details and version upgrades can create compatibility and interoperability issues with Airflow internals, Python versions, databases, tools, and external systems.
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Challenges around integration with external systems and multi-cloud deployments including AWS, GCP, Microsoft, on-prem systems, etc., without the proper planning and implementation expertise
Complications around designing and tuning for optimal Airflow performance and scalability as data volumes grow and as workflows become more complex. -
Unnecessary exposure and risks around security and access control due to unrecognized compromises in DAG architecture and patterns.
Using Airflow for machine learning (ML) workflows brings about additional complexities and costs in managing data pipelines for model training and deployment requiring in-house expertise that is often unavailable.
The Answer:
Optimize Data Orchestration With Qbiz Airflow Solutions
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MIGRATIONS
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Legacy ETL/ELT tool migrations to Airflow
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Airflow version upgrades
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Self-managed to Astro / MWAA/Composer/ADF
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Testing and tooling
Qbiz Airflow Solutions offers a time-tested approach to migrating legacy ETL/ELT tools or self-managed Airflow implementations to your choice of platforms including Astro, MWAA, Composer or ADF. We utilize time-tested processes, custom automations, and purpose-built tools to identify and recommend libraries as well as dependencies in need of replacements resulting in predictable and rapid migrations and upgrades.
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ML WORKFLOW DEVELOPMENT
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Design & implementation of ML workflows
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Integration with ML frameworks/tools
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Monitoring and logging setup
By assessing your machine learning (ML) requirements, Qbiz will design and implement a ML pipeline, integrate with required frameworks and tools as well as set up monitoring and logging to keep track of Airflow performance. Qbiz also helps with resource optimization and getting development code deployed to production enabling reduced costs, faster time-to-insight and decision-making.
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Deployment Assessments
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Airflow environment audits
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DAG inventories & cleanup
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Process and design pattern optimizations
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Gap analyses and implementations
Qbiz experts offer comprehensive audits for customers looking to troubleshoot and enhance their existing Airflow implementations. Our audits identify data quality, performance, and process issues and provide recommendations for improvement. Additionally, we provide documentation and knowledge transfer in the areas of configuration, DAG design and monitoring resulting in improved scalability and optimization.
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BEST PRACTICES
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Data governance and data quality control
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Collaboration and enablement
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Developing for scalability and extensibility​
The Qbiz team offers comprehensive reviews, recommendations, education, and implementation on Airflow best practices. These include best-in-class DAG patterning and organization, complimentary tooling and integrations, testing, coding standards and guidelines, scale and extensibility design, and data governance. Your data teams will be set up for success and make better use of their Airflow implementations.
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PROVEN EXPERTISE
A major mobile game company collaborated with Qbiz to optimize their self-managed Airflow platform.
Problem:
The data engineering team built up an untenable amount of technical debt across a system of 700+ DAGs including sub-DAGs and interdependent DAGs all of which increased complexity. Error alerts around failed jobs escalated to the point where the team didn’t have the resources to react and fix issues let alone respond to new business requests.
Solution:
With Qbiz Airflow Solutions, the team identified misconfiguration and resource inefficiencies, built operators to integrate critical systems and created a graphical DAG architecture enabling the data team to gain a full understanding of critical workflows and dependencies.
Results:
- Resolved errors enabled accurate and timely information delivery to the business.
- Improved data quality and implemented automated checks to reduce legal and financial exposure.
- Implemented best practice monitoring and debugging processes to ensure reliability and SLA compliance
- Provided the engineering team with reusable patterns and standards ending the rising technical debt.
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Engage With Qbiz To Realize
The Full Benefits Of Airflow
Qbiz Airflow Solutions provide a comprehensive approach to data systems orchestration, ensuring that you get the most out of this powerful, open-source platform. Whether you need assistance with migration, assessments, ML workflows or best practices, our Airflow experts are here to help you achieve your data orchestration goals. Contact us today to discuss how we can tailor our services to meet your specific needs.