Minimizing resolution time of any problems impacting your critical applications is key to maintaining operations and keeping end users and consumers happy. Delays in time-to-production such as errors with data pipelines or issues resulting in data inconsistencies can result in increased costs or even lost revenue. Machine learning (ML) can play a major role as it offers a paradigm shift in data platform monitoring. By analyzing historical data and identifying patterns, ML models can proactively predict and prevent potential problems.
Let’s look at some machine learning use cases for proactive data platform monitoring to keep applications running with optimal performance.
Predicting ETA for Daily Data Loads with Machine Learning
New applications and reporting add dependencies and complexities in data load pipelines. Critical applications require close monitoring and SLA tracking. Manual processes are time consuming and based on assumptions which are prone to human error.
Machine learning models can use DQF historical data and system health parameters to monitor the daily job progress and predict data load completion for applications. Daily stats such as file or data arrival time at data warehouse or data lake, time-to-complete initial stages can determine any irregular activities and adjust predictions accordingly.
ML models can learn and understand the patterns such as weekends, public holidays, trends, data volumes to co-relate its impact. With the help of system health and similar patterns, ETAs can be predicted for failures and fixes.
These models can provide benefits for readiness and confidence. Support staff can be enabled to quickly respond to product owners and client managers about the data availability. The support team no longer needs to spend time identifying ETL pipelines and see where ETL jobs are stuck.
Proactively identifying problems allows the support team to address issues before they appear to the product owners. To learn more on how Bitwise helped a leading payment technology provider to boost efficiency, check out this case study on predicting completion time of production jobs using ML for critical applications.
Smart Alert Mechanism for Proactive Monitoring
Common production support challenges include alerting the production support team for failures of critical applications, providing a direct link to open possible steps to resolve the failed jobs, immediate attention for critical job streams, and broadcasting the possible delay in the SLA to the end user.
Using a chat bot tool, such as Google Chatbot, can solve challenges for alerting and providing relevant help to production engineers and notifying all actional and critical alerts. A customizable solution should include DAG logs, SOPs and failure history coupled with comprehensive documentation. Solutions should be highly integrated to add different sources of alerts and integrate with tools like Service Now for incident lifecycle management (logging to closure).
Benefits include early problem detection and improved user experience, effective resource utilization due to reduced efforts, smart notifications with actionable insights, reduced turnaround time for the issue resolution that can work within the existing platform and leverage the available technology stack.
Check out a recent case study on smart alert notification via chat bot to learn how Bitwise built a real-time notification system with chat functionality for a global payment provider and reduced resolution time for critical issues.
Conclusion
There is little doubt that success in today’s markets depends on an organization’s ability to leverage data and insights to act quickly in a competitive landscape. Utilizing the latest technologies and machine learning practices to optimize proactive monitoring for your data platform production support is essential to get the most out of your data lake and data warehouse environments.
By leveraging accelerators like the AI/ML model for predicting job completion and smart alert notification system, production support teams can stay ahead of potential problems that could impact critical applications.
Bitwise has extensive experience in Data Warehouse and Business Intelligence and associated best practices, which uniquely positions the consulting and services company to provide effective and stable Data Platform Support solutions ensuring a seamless experience to business users through high availability of data for consumption.
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