The Essential Guide to Data Quality in Data Engineering

As a data engineer, ensuring the quality of the data flowing through your pipelines is critical to the success of your organization. High-quality data is reliable, accurate, and consistent. Poor-quality data can lead to incorrect insights, inaccurate reporting, and ultimately, poor decision-making.

In this guide, we'll cover the fundamentals of data quality and explore best practices and tools to help you ensure that your data is of the highest quality.

The Fundamentals of Data Quality

Accuracy

The accuracy of data refers to how close the data is to the actual or true value. For example, if you are calculating the total revenue for a company and the actual value is $1,000,000, accurate data would have the value of $1,000,000 in the database.

Completeness

Completeness refers to whether all the data that should be present is actually present. In the case of complete data, all data fields are populated with the required data. If you're missing significant data, or certain parameters are not filled in, it could result in an incomplete or skewed data set.

Consistency

Consistency refers to the uniformity and standardisation of data. Data sets that have inconsistent values and formats, like phone numbers or addresses, lead to confusion and errors.

Validity

Validity refers to whether the data falls within predefined ranges or business rules. For example, if you set a field to accept only numerical input, any non-numeric characters will be deemed as invalid.

Ensuring Data Quality in Your Pipeline

Data Profiling

Data profiling is a process that helps you to understand the structure and quality of your data, enabling you to identify any issues or inconsistencies. By understanding the data's basic properties such as the data type, range or distribution, you can identify field anomalies early on and ensure they are fixed at the source.

Data Quality Checks

Data quality checks validate and ensure the entire data set remains consistent the acceptance criteria during the ETL process. Before accepting the data it is considered, columns are validated to ensure they are correct, and each row is benchmarked to verify it meets the standard.

These checks filter out bad data and guarantee the data quality. Bandwidth, memory, compute resource availability and data volumes will dictate the level of checks that can be performed.

Data Monitoring

Data monitoring is an ongoing process which involves monitoring the data quality throughout the data pipeline to detect potential issues. Alerts are raised if any anomalies are identified. This can be done in a scale-up and scale-out manner across the pipeline.

Data Quality Tools

There is an abundance of tools available for ensuring data quality. Some of the most popular open-source data quality frameworks that data engineers use are:

Great Expectations

Great Expectations is an open-source framework that provides a set of tools to help data engineers define, document, validate, and monitor data quality. It works with databases, data lakes, and third-party data providers. It allows for customised data quality profiles that can be compared to data metrics for continuous integration/continuous deployment pipeline testing.

Apache Griffin

Apache Griffin is another open-source framework that ensures data quality by automating data validation testing. It integrates with big data tools like Hadoop, Spark, and Kafka. The testing rules are easily configurable in the graphical user interface. Apache Griffin is scalable and can handle large amounts of data flow.

Trifacta

Trifacta is yet another data quality tool that has become very popular among data engineers. It is user-friendly and capable of handling data quality issues within a few clicks. Trifacta supports various database engines, Hadoop file systems and cloud providers. Its core feature is visualising the data quality of each column to validate the data flows.

Conclusion

In this guide, you've learned critical concepts and tools for ensuring the quality of the data flowing through your pipelines. By implementing data profiling, quality checks and data monitoring processes and using available open-source data quality tools like Great Expectations, Griffin and Trifacta, you can ensure that your data meets the highest quality standards.

Category: Data Quality