Monitoring Data Quality for Your Big Data Pipelines Made Easy

Introduction

Imagine yourself in command of a sizable cargo ship sailing through hazardous waters. It is your responsibility to deliver precious cargo to its destination safely. Determine success by the precision of your charts, the equipment’s dependability, and your crew’s expertise. A single mistake, glitch, or slip-up could endanger the trip.

In the data-driven world of today, data quality is critical. Data-driven insights help to shape strategies and shape the future of businesses. Like ship captains, data engineers and specialists navigate their companies through a vast sea of data. Big data pipelines are their tools, not compasses.

Transport large volumes of data via these pipelines serves as the foundation of data handling. However, there are a lot of hidden risks and inconsistent data in these waters. Big data pipelines, their function in data-driven decision-making, and the difficulties in preserving data quality are all covered in detail in this article. Data specialists safely deliver important insights by navigating the complexities of data management, much like experienced ship captains do.

Learning Objectives

• Understand the Significance: Grasp the critical role of data quality and integrity in today’s data-driven decision-making processes.
• Recognize Challenges: Identify the unique challenges posed by big data in maintaining data quality, with a focus on Volume, Velocity, and Variety.
• Master Key Metrics: Learn about the essential metrics to ensure comprehensive data integrity, such as completeness, uniqueness, and accuracy.
• Familiarize yourself with Tools & Alerts: Get acquainted with the open-source tools available for data quality checks and the importance of real-time alerting systems for quick issue resolution.

 Why Monitor Data Quality?

Data-driven decisions are only as good as the data itself.

Imagine making a pivotal business decision based on flawed data. The repercussions could be disastrous, leading to financial losses or even reputational damage.

Monitoring data quality helps in the following ways:

• Ensuring Reliability: Data-driven decisions are only as good as the data itself. Imagine a bank processing UPI (Unified Payments Interface) transactions. If the bank’s data quality is compromised, it could lead to incorrect fund transfers, misplaced transactions, or even unauthorized access. Just as a banknote’s authenticity is crucial for it to hold value, the reliability of financial data is paramount for accurate and secure operations. Monitoring data quality ensures that the financial decisions and transactions are based on accurate and reliable data, preserving the integrity of the entire financial system.
• Avoiding Costly Mistakes: Bad data can lead to misguided insights. The consequences can be dire from financial institutions making erroneous trades based on faulty providers administering wrong treatments due to inaccurate patient records data to health. Monitoring and ensuring data quality helps mitigate such risks. Ensuring data quality can mean better customer targeting, accurate financial forecasting, and efficient operations for businesses. Good data quality can be the difference between profit and loss.
• Building Trust: Stakeholders rely on data. Ensuring its quality solidifies their trust in your infrastructure. Data is often shared between departments, stakeholders, or even between businesses. If the data quality is consistently high, it fosters trust.

Challenges in Monitoring Big Data Quality

Big data brings its own set of challenges:

• Volume: The sheer size makes manual checks near-impossible.
• Velocity: With rapid data generation, real-time quality checks become crucial.
• Variety: Different data types and sources add layers of complexity.

Key Metrics to Monitor

To effectively monitor data quality, you need to focus on specific metrics:

• Completeness: This metric ensures that all required data is present. Incomplete data can lead to incorrect analysis or decisions. By monitoring completeness, you can identify missing data early and take corrective actions, ensuring that data sets are holistic and comprehensive.
• Uniqueness: Monitoring uniqueness helps identify and eliminate duplicate records that can skew analytics results and lead to operational inefficiencies. Duplicate data can also confuse and lead to misguided business strategies.
• Timeliness: Data should not only be accurate but also timely. Outdated data can lead to missed opportunities or incorrect strategic decisions. By ensuring data is updated in real-time or at suitable intervals, you can guarantee that insights derived are relevant to the current business context.
• Consistency: Inconsistent data can arise due to various reasons like different data sources, formats, or entry errors. Ensuring consistency means that data across the board adheres to standard formats and conventions, making it easier to aggregate, analyze, and interpret.
• Accuracy: The very foundation of analytics and decision-making is accurate data. Inaccurate data can lead to misguided strategies, financial losses, and a loss of trust in data-driven decisions. Monitoring and ensuring data accuracy is pivotal for the credibility and reliability of data insights.

Tools and Techniques

Several open-source tools can assist in maintaining data quality. We will discuss two of them in this blog.

Deequ

Deequ is a library built on top of Apache Spark and designed to check large datasets for data quality constraints efficiently. It supports defining and checking constraints on your data and can produce detailed metrics.

As shown above, Deequ Architecture, built atop the Apache Spark framework, inherits the distributed computing capabilities of Spark, allowing it to perform data quality checks on large-scale datasets efficiently. Its architecture is fundamentally modular, centering around.

• Constraints: rules or conditions that the data should satisfy. Users can define custom constraints or employ Deequ’s built-in checks. When applied to datasets, these constraints produce metrics, which are then stored and can be analyzed or used to compute data quality scores.
• Storing historical data quality metrics enables data quality tracking over time and helps identify trends or anomalies.
• Integrating seamlessly with Spark’s DataFrame API, Deequ can be effortlessly integrated into existing data processing pipelines. Its extensible nature allows developers to add new constraints and checks as required.

Here’s a basic example using Deequ:

from pydeequ.checks import *
from pydeequ.verification import *

check = Check(spark, CheckLevel.Warning, "Data Quality Verification")

result = VerificationSuite(spark).onData(df).addCheck(
 check.hasSize(_ == 500).hasMin("column1", _ == 0)
).run()

Apache Griffin

Apache Griffin is an open-source Data Quality Service tool that helps measure and improve data quality. It provides support to validate and transform data for various data platforms.

As shown above, Graffin architecture is a holistic solution to data quality challenges, boasting a well-structured architecture to ensure flexibility and robustness.

At its core, Griffin operates on the concept of data quality measurements, using a variety of dimensions such as accuracy, completeness, timeliness, and more.

Its modular design comprises several main components-

• Measurement module for actual quality checks,
• Persistency module for storing quality metadata.
• Service module for user interactions and API calls.
• Web-based UI provides a unified dashboard, allowing users to monitor and manage their data quality metrics intuitively.

Built to be platform-agnostic, Griffin can seamlessly integrate with many data platforms ranging from batch processing systems like Flink/Spark to real-time data streams. Apache Griffin’s architecture encapsulates the essence of modern data quality management.

Here’s a basic example using Grafin:

You can set it up using this guide first. Once setup is done, we can set data quality rules and measure using the below.

Config Setup: This file specifies the data sources, the metrics to be computed, and the necessary checks.

{
  "name": "data-quality-check",
  "process": {
    "type": "batch"
  },
  "data.sources": [
    {
      "name": "source",
      "baseline": true,
      "connectors": [
        {
          "type": "hive",
          "version": "1.2",
          "config": {
            "database": "default",
            "table.name": "your_table_name"
          }
        }
      ]
    }
  ],
  "evaluateRule": {
    "rules": [
      {
        "dsl.type": "griffin-dsl",
        "dq.type": "accuracy",
        "out.dataframe.name": "accuracy_check",
        "rule": "source.id = target.id",
        "details": {
          "source": "source",
          "target": "target"
        }
      }
    ]
  }
}

Run Data Quality Job

$SPARK_HOME/bin/spark-submit --class org.apache.griffin.core.job.JobLauncher 
\--master yarn --deploy-mode client \/path-to/griffin-batch.jar 
\--config /path-to/quality-check.json

Once the job runs, Griffin will store the results in its internal database or your specified location. From there, you can query and analyze the results to understand the quality of your data.

Setting Up Alerts

Real-time monitoring becomes effective only when paired with instant alerts. By integrating with tools like Pagerduty, Slack or setting up email notifications, you can be notified immediately of any data quality issues.

However, a more comprehensive alerting and monitoring solution can use open-source toolings like Prometheus and Alertmanager.

• Prometheus: This open-source system scrapes and stores time series data. It allows users to define alerting rules for their metrics, and when certain conditions are met, an alert is fired.
• Alertmanager: Integrated with Prometheus, Alertmanager manages those alerts, allowing for deduplication, grouping, and routing them to the proper channels like email, chat services, or PagerDuty.

Refer to this guide to learn more about this setup.

Certainly! Alerting is crucial for batch and real-time pipelines to ensure timely processing and data integrity. Here’s a breakdown of some typical alert scenarios for both types of pipelines:

Alerts for Batch Pipelines

Batch pipelines typically process data in chunks at scheduled intervals. Here are some alerts that can be crucial for batch pipelines:

• Job Failure Alert: Notifies when a batch job fails to execute or complete.
• Anomaly Alert: Alerts when the data anomaly is detected. For example, the volume of data processed in a batch is significantly different than expected, which could indicate missing or surplus data.
• Processing Delay: Notifies when the time taken to process a batch exceeds a predefined threshold. A typical pipeline takes about 1hr, but it took more than 2hr and is still not completed. It could indicate some problems in processing.
• No Success: While monitoring for explicit failures is common, tracking for the absence of successes is equally essential. There might be scenarios where a pipeline doesn’t technically “fail,” but it might get stuck processing, or perhaps a failure metric isn’t triggered due to issues in the code. You can identify and address these stealthier issues by setting an alert to monitor for lack of success signals over a specific period.
• Data Schema Changes: Detect when incoming data has additional fields or missing expected fields.
• Sudden Distribution Changes: If the distribution of a critical field changes drastically, it might indicate potential issues.

Apart from these alerts, quality alerts can also be defined based on use cases and requirements.

Alerts for Real-time Pipelines

Real-time pipelines require more instantaneous alerting due to the immediate nature of data processing. Some typical alerts include:

• Stream Lag: Alerts when the processing lags behind data ingestion, indicating potential processing bottlenecks.
• Data Ingestion Drop: Notifies when the data ingestion rate drops suddenly, which could indicate issues with data sources or ingestion mechanisms.
• Error Rate Spike: Alerts when the rate of errors in processing spikes, indicating potential issues with the data or processing logic.

Conclusion

In an age dominated by data, the integrity of our data pipelines stands as the cornerstone of insightful decision-making. Ensuring data quality is not just an ideal but an essential practice, safeguarding enterprises from missteps and fostering trust. With tools like Apache Griffin, Deequ, and Prometheus at our disposal, we are well-equipped to uphold this standard of excellence, allowing us to navigate the vast seas of big data with confidence and precision.

Key Takeaways

• Reliable data is fundamental to making informed decisions. Flawed data can lead to significant financial and reputational damages.
• The three Vs – Volume, Velocity, and Variety – present unique hurdles in ensuring data integrity.
• Monitoring completeness, uniqueness, timeliness, consistency, and accuracy ensures comprehensive data integrity.
• Open-source tools such as Apache Griffin and Deequ enable efficient data quality checks, while alert systems like Prometheus ensure real-time monitoring and prompt issue resolution.

Frequently Asked Questions