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How do you validate and verify data formats in your ETL pipelines?

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Data is the lifeblood of any business, but it can also be a source of frustration and errors if it is not properly formatted and verified. In this article, you will learn how to validate and verify data formats in your ETL pipelines, which are the processes of extracting, transforming, and loading data from various sources into a target destination. By following some best practices and using some tools, you can ensure that your data is consistent, accurate, and ready for analysis.

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Shubham Acharya

Data Engineering @ Wayfair | Ex - Paytm

1 Why data format validation and verification matters

Data format validation and verification are crucial steps in any ETL pipeline, because they ensure that the data you are moving and transforming is compatible with the target destination and the intended use cases. Data format validation checks that the data conforms to a predefined schema, structure, or standard, such as JSON, CSV, XML, or Parquet. Data format verification checks that the data is correct, complete, and free of errors, such as missing values, duplicates, or outliers. Without data format validation and verification, you risk losing data quality, integrity, and reliability, which can lead to poor decisions, wasted resources, and customer dissatisfaction.

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Shubham Acharya

Data Engineering @ Wayfair | Ex - Paytm
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For any data integration pipeline(structured data ingestion in particular), maintaining the schema is a very important step for the system. To make sure that the analysis jobs on data don't fail, corresponding checks are needed to be put in place to ensure that all relevant fields are coming in and that too in the desired format. If these checks are not in place the system goes into situations where we are making analysis/decisions based on incomplete, inaccurate data. Need for data format validation may vary between structured and unstructured ingestion jobs, but eventually a schema validation needs to be in place before reporting/analysis/capturing data insights.

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2 How to validate data formats in your ETL pipelines

When validating data formats in ETL pipelines, the type, source, and destination of the data should be taken into consideration. One common method is to use a schema or metadata file that defines the expected data format. This file can be used to compare the incoming data with the expected format and flag any discrepancies or errors. For example, JSON schema files can be used to validate JSON data with a tool like AJV or jsonschema. Additionally, data quality frameworks or libraries such as pandas, PySpark, Great Expectations, or Deequ can be used to validate data formats using functions or assertions/tests. A custom script or function can also be created to perform specific validations on the data format, such as checking the file extension, delimiter, encoding, header, and footer. For example, Python or Bash commands like file, cut, iconv, head and tail can be used to validate data formats.

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Shubham Acharya

Data Engineering @ Wayfair | Ex - Paytm
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Data format validation, this is part of schema validation process during data integration pipelines. Basically the ask here is to check if the incoming data is compliant with the existing data in the system, following the existing data schema used for analytical use cases. There are multiple ways of doing data format validation in pipelines. Some of them are, 1. Custom script/function - The benefit here is of customising the data checks. 2. Following predefined schema/metadata file - Validating incoming schema with expected format and throwing errors in case of mismatch. This is easier to implement and upscale for adding any new checks. 3. Using existing data quality frameworks or libraries such as pandas, PySpark to validate data formats.

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3 How to verify data formats in your ETL pipelines

Verifying data formats in your ETL pipelines is similar to validating them, but it involves more checks on the data values and quality. To do this, you can use a data profiling tool or library to provide statistics and insights on the data format, such as the number of rows, columns, nulls, duplicates, outliers, or anomalies. For example, pandas-profiling or Spark-DQ offer functions like profile_report or dqCheck. Additionally, you can use a data comparison tool or library to compare the data format before and after the transformation or between the source and destination. Diffy or Delta Lake are examples of such tools that have functions like diff or merge. Lastly, a data testing tool or library can be used to run automated tests on the data format and report any failures or issues. pytest or unittest are examples of such tools that have functions like assert or assertEqual.

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Shubham Acharya

Data Engineering @ Wayfair | Ex - Paytm
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Verification of data format is not same as Validation of data format. They are similar at max, verification involves more checks on the data values and quality. There are various tools/solutions which we can bring to bear to achieve this like, pandas-profiling,Spark-DQ, etc. From a personal experience, Delta Lake is very useful in these exercises(check out the merge functionality). And if nothing then there is always an option of bringing out the old school scripting approach where we can customise our use case as per need by automating the process. Look into python libraries such as pytest, unittest, etc to see how you can leverage these to your need.

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4 Best practices for data format validation and verification

For effective and efficient data format validation and verification, it is important to define and document standards and expectations across ETL pipelines and data sources. Validation and verification should be implemented early, preferably at the source or extraction stage, and automated with tools, frameworks, libraries, scripts, or functions. Additionally, monitoring and logging results and performance should be done regularly with alerts or notifications sent out in the event of issues or failures. Finally, review and update methods and rules periodically to accommodate changes in data sources, destinations, or use cases.

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5 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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KUNAL BHAT , PMP

I help Fortune 1000 organizations drive digital transformation through GenAI integration | Business Analyst | 40% efficiency gains | PMP
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Validating and verifying data formats in ETL pipelines involves several steps. First, define expected formats for each data source. Then, implement checks at various stages of the pipeline using tools like schema validation libraries or custom scripts. During extraction, ensure data adheres to predefined schemas. In transformation, validate against business rules and desired formats. Lastly, verify formats post-loading to the destination. Automated tests can flag inconsistencies, while manual review and reconciliation help confirm data accuracy. Continuous monitoring and feedback loops refine validation processes, ensuring data integrity throughout the pipeline.

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How do you validate and verify data formats in your ETL pipelines?

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1 Why data format validation and verification matters

2 How to validate data formats in your ETL pipelines

3 How to verify data formats in your ETL pipelines

4 Best practices for data format validation and verification

5 Here’s what else to consider

Data Management

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How do you validate and verify data formats in your ETL pipelines?

1

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3

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5

1 Why data format validation and verification matters

2 How to validate data formats in your ETL pipelines

3 How to verify data formats in your ETL pipelines

4 Best practices for data format validation and verification

5 Here’s what else to consider

Data Management

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