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How can you validate data in real-time pipelines?

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Data validation is the process of ensuring that the data you collect, process, and analyze meets certain quality standards and business rules. It is essential for any data-driven organization to ensure the accuracy, completeness, consistency, and timeliness of their data. However, data validation can be challenging in real-time pipelines, where data is continuously streamed from various sources and consumed by different applications. How can you validate data in real-time pipelines without compromising performance, scalability, and reliability?

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk…

1 Define validation rules

The first step to validate data in real-time pipelines is to define the validation rules that apply to your data. Validation rules are the criteria that determine whether your data is valid or not, such as data type, format, range, uniqueness, nullability, referential integrity, and business logic. You can define validation rules at different levels of granularity, such as schema, record, field, or attribute. You can also use different methods to specify validation rules, such as declarative, imperative, or hybrid. Depending on your use case, you may need to use different tools and frameworks to implement validation rules, such as schema registry, data dictionary, metadata management, or custom code.

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk, increasing agility, realizing strategies, and driving innovation.
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Totally agree, real-time data quality is crucial so defining clear validation rules upfront using schema registry, and metadata management tools helps catch issues early. However, take care not to over-engineer validation logic which could impact performance and agility.

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2 Apply validation logic

The second step to validate data in real-time pipelines is to apply the validation logic to your data streams. Validation logic is the code or function that executes the validation rules and returns the validation results. You can apply validation logic at different stages of your pipeline, such as source, ingestion, processing, or consumption. You can also use different techniques to apply validation logic, such as batch, streaming, or incremental. Depending on your architecture, you may need to use different components and platforms to apply validation logic, such as ETL tools, stream processing engines, data quality frameworks, or lambda functions.

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk, increasing agility, realizing strategies, and driving innovation.
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Spot on, baking in validation logic throughout the pipeline helps maintain data quality, but take care to apply it judiciously during processing to avoid latency. Leveraging tools like schema registry and data quality frameworks makes adding validation smoother.

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3 Handle validation errors

The third step to validate data in real-time pipelines is to handle the validation errors that occur when your data fails the validation rules. Validation errors are the anomalies or issues that indicate that your data is invalid or corrupted. You can handle validation errors in different ways, such as logging, alerting, rejecting, correcting, or enriching. Depending on your strategy, you may need to use different mechanisms and systems to handle validation errors, such as error queues, error tables, error events, error notifications, or error recovery workflows.

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk, increasing agility, realizing strategies, and driving innovation.
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Handling validation errors cleanly is crucial - log thoroughly, and alert stakeholders, but use error queues to isolate bad data to avoid blocking processing. Leverage tools with workflow capabilities to enable error recovery and reprocessing where possible.

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4 Monitor validation metrics

The fourth step to validate data in real-time pipelines is to monitor the validation metrics that measure the quality and performance of your data validation process. Validation metrics are the indicators or statistics that show how well your data validation process is working and how much impact it has on your data quality and pipeline efficiency. You can monitor validation metrics in different dimensions, such as volume, velocity, variety, veracity, or value. Depending on your objectives, you may need to use different tools and dashboards to monitor validation metrics, such as data quality scorecards, data quality reports, data quality alerts, or data quality dashboards.

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk, increasing agility, realizing strategies, and driving innovation.
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Monitoring validation metrics is key to ensuring quality - track error rates, latency, and accuracy KPIs with data quality dashboards. But focus on metrics that provide actionable insights and align with business objectives, rather than an overload of vanity metrics.

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5 Optimize validation process

The fifth step to validate data in real-time pipelines is to optimize the validation process to improve the quality and efficiency of your data validation process. Optimization is the process of finding and implementing the best possible solutions for your data validation challenges and opportunities. You can optimize the validation process in different aspects, such as design, implementation, execution, or evaluation. Depending on your situation, you may need to use different methods and techniques to optimize the validation process, such as data profiling, data cleansing, data enrichment, data deduplication, data standardization, or data testing.

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6 Review validation outcomes

The sixth and final step to validate data in real-time pipelines is to review the validation outcomes to assess the effectiveness and value of your data validation process. Validation outcomes are the results or impacts that your data validation process has on your data quality and business goals. You can review validation outcomes in different ways, such as feedback, analysis, or comparison. Depending on your purpose, you may need to use different sources and criteria to review validation outcomes, such as data consumers, data analysts, data stewards, data quality dimensions, data quality objectives, or data quality benchmarks.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you validate data in real-time pipelines?

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3

4

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1 Define validation rules

2 Apply validation logic

3 Handle validation errors

4 Monitor validation metrics

5 Optimize validation process

6 Review validation outcomes

7 Here’s what else to consider

Data Architecture

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How can you validate data in real-time pipelines?

1

2

3

4

5

6

7

1 Define validation rules

2 Apply validation logic

3 Handle validation errors

4 Monitor validation metrics

5 Optimize validation process

6 Review validation outcomes

7 Here’s what else to consider

Data Architecture

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