What are the best tools for batch processing monitoring?

Apache NiFi Apache Oozie Apache Airflow Prometheus Grafana Control-M Jenkins Tivoli Workload Scheduler (TWS) AutoSys Datadog: Cloud-based platform for collecting and visualizing performance data with alerting capabilities.

Once the data has been spreading among the organization, you need to keep everything running smoothly. Is not good when the (key) users contact you about some issues with the data. You need to be one step ahead and track everything you can. Those monitoring batch certainly will help you to identify e fix everything before the users, and also improve how the data team/platform has been supporting the organization with good data for them.

Monitoring batch processing is essential to ensure data integrity, maintain performance standards, quickly identify and resolve failures, and optimize resource usage.

Batch processing monitoring is important because it ensures the reliability and performance of critical data processing tasks. It allows us to detect and address issues, track progress, and ensure data quality, ultimately leading to better decision-making and operational efficiency.

There are a couple more from Broadcom: CA 7 and ESP. Both are mainframe schedulers but can schedule work on any platform, including Cloud using agents.

In batch processing, monitor job completion status, execution times, data integrity, error rates, resource utilization (CPU, memory, I/O), system bottlenecks, failed dependencies, and retry counts to ensure efficiency and reliability.

Things to monitor in batch processing: 1. Track the start and end time, errors messages, or identify when a job get stuck in processing. 2. Analyze the duration of each task and identify any deviations from expected run times when huge volume of data. 3. Identify resource-intensive jobs that might impact system performance. 4. Test failover mechanisms to ensure job recovery in case of system failures

In my experience, When monitoring batch processing, we focus on the following key areas: Job Status: Keep track of job start and end times, and monitor for job failures. Resource Utilization: Monitor CPU, memory, and storage usage to optimize resource allocation. Data Quality: Check for anomalies, missing data, or data format issues. Logs and Metrics: Collect and analyze logs and metrics to diagnose issues.

Below is the most common typical design for batch processing. Extract: Every pipeline starts from aggregating the data from data sources or through upstream events. Transform: Once the data is aggregated, this goes for transformation such as joining tables, applying wide and narrow dependency transformations and much more. Load: Once the data is prepared according to the SLA's then this data is loaded into data warehouses or data lakes for further analysis or consume the data by different BU's. * Here Every stage is important for successful completion of job. so, it's essential to monitor the pipeline and an automatic INC's should be created, so that ticket will assign to the right team and will fix the Issue in case of any job failures.

Monitoring batch processing helps in identifying issues early on in a data pipeline and setting up alerts on top of it just makes it much more better in identifying flaws before downstream data users notice or even downstream processes fail because of bad data. Using Airflow or even Data Factory for monitoring batch processing would be a good bet since both of them provide a easy to use UI. One of them can be coded programmatically and the other is just user friendly with it’s web UI.

Starting with low cost services or even open source tool are the best approach. 1. Apache Airflow can track all steps of your dag, also can send those logs to a slack channel for a better monitoring; 2. Use Elementary for testing and showing anomalies; 3. Datadog can be used for your infrastructure; you can set some alerts here;

To monitor batch processing follow this SOP: 1. Use tools like Apache Airflow or Prometheus for tracking job statuses and metrics. 2. Set alerts for anomalies, such as job failures or delays. 3. Maintain detailed logs for troubleshooting. 4. Use dashboards for visualizing key metrics. 5. Regularly benchmark performance and track deviations.

Each tool has various features to track and manage the execution and performance of batch processing. In most of the projects, I used ADF Monitoring Dashboard, it gives activity-specific information, such as input/output datasets, durations, overview of your data factory's health, including pipeline runs, activity runs, triggers, and errors encountered during processing. Also, we can set up alerts and notifications when the job succeeds or fails. Also, in Azure, we can create custom monitoring solutions using Azure Log Analytics.

Effective monitoring involves using specialized tools and following best practices: Monitoring Tools: Leverage tools like Apache NiFi, Apache Oozie, and Apache Airflow, as well as commercial solutions like Datadog or Prometheus. Alerting: Set up alerts to notify you when predefined thresholds are exceeded. Dashboards: Create visual dashboards to provide real-time insights into job statuses and resource utilization. Logging and Auditing: Implement comprehensive logging and auditing to track job execution.

* Take Ownership on your Data pipelines and make sure you know in and outs of your application or pipeline runs. * Ensure your pipelines are running on-time with no delays and delivering the business requirements according to the SLAs configured. * Ensure your pipelines running schedule such as Daily/Weekly/Monthly/Quarterly deliveries * Make the INC or ticket generation system in-place if any job fails, assign the ticket and communicate to the right team to fix the failure ASAP. * Document the run-times if any jobs are taking longer time than usual timings. Feel free to add more points if I missed any.

1. Using dedicated tools like Airflow. 2. Setting real-time alerts for anomalies. 3. Maintaining detailed logs for analysis. 4. Visualizing metrics via dashboards. 5. Regularly benchmarking and optimizing. 6. Monitoring job dependencies and failures.

To ensure efficient batch processing monitoring: Define Clear Metrics: Define clear and relevant metrics to monitor. Automation: Automate monitoring and alerting to reduce manual intervention. Historical Data: Maintain historical data for trend analysis and long-term performance optimization. Scalability: Ensure your monitoring solution can scale as your batch processing requirements grow.

Effective batch processing monitoring offers several advantages: Improved Data Quality: Timely error detection leads to higher data quality. Cost Savings: Optimized resource usage reduces infrastructure costs. Business Continuity: Minimizes downtime, ensuring business continuity. Compliance: Ensures compliance with data governance and security standards.

Batch processing offers several benefits: it efficiently handles large volumes of data, streamlining operations and saving time. By grouping tasks, it reduces system interaction, enhancing speed. It's cost-effective, as it often runs during off-peak hours, utilizing idle resources. This approach also allows for easier debugging due to its structured nature. Furthermore, batch processing can be automated, freeing up resources for other critical tasks.

Looking for startups mediums can help you to select or even consider the best tools or uses cases for monitoring. Usually, the data teams use medium to share their thoughts, data architecture, guidelines, problems, lessons learned...