Essential Data Quality Checks for Data Engineers to Master
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Chapter 1: Understanding Data Quality in Modern Data Systems
In an era dominated by Big Data and Cloud computing, the volume of data generated and transferred into systems like Data Warehouses, Data Lakes, and Data Lakehouses is increasing rapidly. It is essential to ensure high data quality during integration processes. Below, we outline five vital checks that every Data Engineer should focus on when developing ETL/ELT data pipelines.
Section 1.1: Completeness Verification
Completeness verification involves ensuring that all anticipated data elements are included in a data set. This check confirms whether all required rows and fields have been transferred and identifies any invalid or missing values. Conducting this verification is crucial for guaranteeing that the dataset contains all necessary information for precise analysis and informed decision-making. For practical strategies on achieving this, refer to the following article:
How to Improve Data Warehouse Quality: A Walkthrough Example with Google’s BigQuery
towardsdatascience.com
Section 1.2: Ensuring Data Accuracy
Data accuracy is a fundamental component of quality. It pertains to the correctness and precision of data values. Data Engineers must validate accuracy to uncover discrepancies, anomalies, or errors. This process may involve cross-referencing data with trusted sources, conducting statistical analyses, or applying validation rules to highlight potential inaccuracies.
Section 1.3: Validity Assessment
While accuracy pertains to the correctness of the data, validity assessments focus on whether the data adheres to established rules, standards, or constraints. These checks may include verifying data types, ranges, or formats. Validating data against these criteria is essential, particularly as conversions and adaptations between source and target systems can introduce errors.
Section 1.4: Duplicate Record Detection
Although this point overlaps with previous checks, it deserves special attention. Duplicates can arise from source system errors or during data transmission, leading to skewed insights, inefficient processing, and wasted storage space. Data Engineers should implement mechanisms to identify duplicate records within a dataset or across multiple datasets. Techniques such as record matching, fuzzy matching, or hashing algorithms can help detect and eliminate duplicates, ensuring cleaner and more trustworthy data.
Section 1.5: Monitoring Timeliness and Performance
Timeliness and performance analysis are increasingly critical in Data Engineering. In today’s environment, data should ideally be available in near real-time. Data Engineers should continuously monitor data input and processing pipelines to ensure timely delivery. Any deviations from expected timelines may signal issues with data sources, extraction processes, or delivery mechanisms.
Summary
In modern data platforms, the accuracy and timeliness of data are paramount. Data Engineers must implement specific checks to ensure that the data meets established expectations and KPIs. This article has discussed five best practices that should be integrated into your data pipeline or platform. For further insights on this topic, consider exploring the linked articles and sources below.
Explore the importance of engineering data for quality in this informative video.
Discover five essential secrets that make data engineering easier and more effective!
Chapter 2: Best Practices for Data Quality Management
Sources and Further Reading
[1] MIT, Data Quality Handbook for Data Warehouse
[2] Asking ChatGPT for Best Practices in Data Quality Checks (2023)
[3] Wikiversity, Duplicate Record Detection (2023)