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Track data processing metrics and system health to ensure high availability and performance.

Data monitoring definition:

Data monitoring is the process of observing and measuring data processing systems to ensure they are working correctly and efficiently. This involves tracking key performance indicators (KPIs), identifying issues, and taking corrective actions to optimize the pipeline's performance.

In the context of modern data pipelines, data monitoring can involve monitoring the pipeline's health, data quality, and performance metrics. This includes tracking things like data volume, processing times, error rates, and system resource utilization.

Practical examples of data monitoring in Python include using libraries like Prometheus or Grafana to collect and visualize pipeline metrics in real time. These tools allow you to track KPIs like throughput, latency, and error rates and set up alerts to notify you when metrics fall outside of predefined thresholds.

Another example of data monitoring is using log analysis tools like ELK stack (Elasticsearch, Logstash, and Kibana) to monitor system logs for errors or anomalies. These tools can help you identify issues and diagnose performance problems in real time.

Overall, data monitoring is critical to ensuring the integrity and reliability of modern data pipelines, and it requires a combination of automated monitoring tools and human oversight to be effective.

Coding practices for data monitoring

In Python, there are several coding practices associated with data monitoring to identify issues or anomalies are identified and addressed in a timely manner.

Use monitoring libraries: Python has several libraries that are designed for monitoring data, such as StatsD, Graphite, and Prometheus. These libraries provide tools for tracking metrics, setting up dashboards, and alerting on anomalies. Using these libraries can help ensure that monitoring is consistent and reliable across different projects.

Use logging and exception handling: In Python, logging and exception handling are important tools for monitoring data. By logging events and exceptions, developers can get a clear picture of what's happening in the system, and can identify any issues that arise. This can help with troubleshooting and identifying patterns of behavior that might indicate a problem.

Define thresholds and alerts: To monitor data effectively, it's important to define thresholds and alerts that can trigger notifications when certain conditions are met. This can be done using Python libraries like Prometheus and StatsD, or by writing custom code. By defining thresholds and alerts, developers can ensure that they are notified of issues as soon as they arise, and can take action to address them before they become serious problems.

Implement automated testing: Automated testing is an important part of data monitoring in Python. By setting up automated tests, developers can ensure that data is being processed correctly, and that any issues are caught early. This can help with identifying issues before they impact end-users, and can improve the overall quality of the system.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


Select, organize and annotate data to make it more useful for analysis and modeling.


Identify and remove duplicate records or entries to improve data quality.


Analyzing the number of features or attributes in the data to improve performance.


Enhance data with additional information from external sources.


Extract data from a system for use in another system or application.


Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


Store the results of expensive function calls and reusing them when the same inputs occur again.


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


Transform your data so it is fit-for-purpose.


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Ensure that data in different systems or databases are in sync and up-to-date.


Check data for completeness, accuracy, and consistency.


Maintain a history of changes to data for auditing and tracking purposes.