What tools and techniques do you use to test your data architecture pattern?

Data architecture patterns are the blueprints for designing, building, and managing data systems that meet the needs and goals of an organization. But how do you know if your data architecture pattern is effective, reliable, and scalable? In this article, you will learn about some of the tools and techniques that you can use to test your data architecture pattern and ensure its quality and performance.

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1 Data Quality Testing

Data quality testing is a process of verifying that the data in your data architecture pattern is accurate, consistent, complete, and relevant. It can help you identify and resolve data issues such as missing values, duplicates, outliers, and errors. To do this, you can use tools such as SQL, Pandas, or Apache DataFu for data profiling tasks such as data type detection, data summary, data validation, and data cleansing. Additionally, you can use data quality tools such as Informatica Data Quality, Talend Data Quality, or IBM InfoSphere QualityStage to define and execute predefined criteria or standards that the data must adhere to. This way you can measure and monitor the different data quality dimensions like accuracy, completeness, consistency, timeliness, and validity. Furthermore, these tools can generate data quality reports and dashboards.

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2 Data Integration Testing

Data integration testing is the process of verifying that data in your data architecture pattern is correctly transferred, transformed, and loaded from the data sources to the data targets. This testing can help you ensure that the ETL or ELT processes don't cause any data loss, corruption, or duplication. To do this, you can use various tools and techniques such as data comparison and data mocking. Data comparison involves comparing the data in the sources and targets to check for discrepancies or mismatches. You can use SQL, Diff, or QuerySurge to do schema comparison, row count comparison, data value comparison, and data transformation comparison. Data mocking involves creating synthetic or dummy data to simulate real data in sources or targets. Faker, Mockaroo, or dbForge Data Generator can be used to generate and populate tasks such as data sampling, masking, anonymization, and variation. In this way, you can test the integration processes without affecting production data.

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Aliaksandr Klimovich

Big Data Engineer | DQ | SDET
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There are some dev tools support DQ constraints like Databricks live tables or Dbt, otherwise we can use aws deequ, great expectations or something custom. In case of data transformation validation I would consider the strategy: 1. Dedicated test contour with business representative managable "gold" data and future comparison actual results with the precalculated ones using pytest, scalatest, junit, custom solution - no matter. In this approach data management option should be considered. 2. Doing transformation on our side and compare with the dev pipeline output Both have props and cons.

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3 Data Performance Testing

Data performance testing is the process of verifying that the data in your data architecture pattern can support the expected workload, volume, and speed of the data operations and queries. This process can help you evaluate and optimize the data performance dimensions, such as throughput, latency, scalability, and availability. To do this, you can use various tools and techniques, such as data benchmarking and data tuning. Data benchmarking involves measuring and comparing the data performance metrics of your data architecture pattern against a baseline or standard. It can help you identify any data performance bottlenecks or issues. You can use tools like JMeter, HammerDB, or Databricks for tasks like data load testing, stress testing, concurrency testing, and query testing. Data tuning involves adjusting and improving the data performance parameters of your architecture pattern based on the benchmarking results. It can help enhance and optimize aspects like storage, indexing, partitioning, and caching. Tools like SQL Server Management Studio, Oracle Database Tuning Pack, or Amazon Redshift Advisor are useful for tasks like configuration, optimization, compression, and maintenance.

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4 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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What tools and techniques do you use to test your data architecture pattern?

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1 Data Quality Testing

2 Data Integration Testing

3 Data Performance Testing

4 Here’s what else to consider

Data Architecture

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What tools and techniques do you use to test your data architecture pattern?

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3 Data Performance Testing

4 Here’s what else to consider

Data Architecture

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