Data and code quality are essential components of data engineering. Data quality refers to accuracy, completeness, consistency, and relevance, whereas code quality refers to the code's design, structure, and readability. Ensuring the quality of data and code is critical as it affects the accuracy and reliability of data insights. This blog will guide you with the top 10 data engineering practices in 2023 to ensure data and code quality in data engineering solutions and more.
A QA (quality assurance) process is essential in data engineering to ensure the data you collect, analyze and store is accurate, consistent and reliable.
In a survey of over 500 data engineers, nearly 60% reported that they spend more time fixing bad data than creating new data pipelines. (Alteryx).
Below illustration represents some reasons why quality assurance is necessary in data engineering:
Data engineering is crucial to data science and machine learning. It involves creating and managing large-scale data infrastructure, enabling data scientists to develop and deploy their models. Here, we will discuss ten best practices to ensure data and code quality in data engineering projects.
Before initiating any data engineering project, defining data requirements is highly essential. It ensures the data collected is accurate, consistent, and relevant to the objectives. Defining data requirements involves assessing the type of data to be collected, data sources, format and structure, frequency of data collection, data analysis techniques, and expected outcomes. Defining data requirements help researchers, data engineers, and data analysts to do away with ambiguity, minimize errors and provide assurance that the data collected is accurate for analysis.
Version control system (VCS) is a category of tools that allows software development teams to look after the changes in source code whenever they need. Adopting a version control system in 2023 will help businesses to enable their data engineers to work on the same code base overcoming the risk of conflicting changes. The version control system tracks all the changes to the codebase and allows developers to work on different branches. Also, a version control system maintains the complete history of changes to the codebase and helps in assessing the source of the errors and rolling back to a previous version.
Automated testing is critical in data engineering as it helps to ensure the accuracy and reliability of data pipelines. Writing automated tests helps to identify and prevent data quality issues that occur at the time of data processing. When automated testing verifies data integrity at each stage of the pipeline, data engineers get assurance on the accuracy and reliability of output data. Also, writing automated tests help data engineers to manage changes to the data pipelines and minimize the risk of data loss or theft.
Using a data pipeline framework in data engineering is essential for businesses to simplify their process of building and handling data pipelines. A data pipeline framework offers a standard set of tools and processes to build data pipelines and help businesses to streamline their data processing activities by minimizing time and effort. Also, a data pipeline framework works on enhancing data quality by offering a set of standardized processes such as data validation, cleansing, and transformation.
Features like data breaches and cyberattacks are increasingly common. Hence to do away with such things and protect sensitive data, businesses should implement an access control system that includes user authentication, authorization, and others. It will allow only authorized users to access sensitive data and enhance data security and safety. Also, businesses should opt for data masking to protect their sensitive business data by replacing real data with false data to prevent unauthorized access.
Validating the quality of data is a critical aspect of ensuring data accuracy and reliability for businesses while making informed decisions. Hence, it is essential for data engineers to analyze and understand the data and identify inconsistencies, anomalies, and others to ensure data accuracy and consistency. Also, businesses should focus on data cleansing, which involves identifying and correcting data errors by removing duplicate records and filling in missing data values.
A data quality validation process can save organizations an average of 20% in time and resources compared to correcting errors in the analysis phase.
To ensure the efficiency and accuracy of processed data, monitoring data pipeline performance will be essential in this for businesses. Hence, they should define key metrics such as throughput, latency, processing time, and error rates to measure the performance of data pipelines. Also, they should use data pipeline monitoring tools to track and visualize the metrics, offer real-time alerts on performance issues and allow fast resolutions.
The use of performance monitoring tools can lead to a reduction in the cost of data management by up to 30% through the optimization of resource utilization.
Documentation is important for ensuring the code and data pipeline's maintainability. However, it requires a comprehensive approach that involves comments, user guides, version control, descriptive naming conventions, data dictionaries, and flow diagrams. Data engineers should add user comments throughout the code to describe each section of the code so that others can comprehend the usability of the code and make changes as and when required even though they have no role in writing the original code.
Organizations that invest in data pipeline documentation are up to 20% more likely to adopt best practices in data management, leading to a more sustainable and scalable data infrastructure.
Businesses should look forward to continuous improvement of the code and data pipeline to ensure better performance and higher data accuracy. For this, they should monitor the performance of code and data pipelines and identify necessary areas of improvement. Businesses should automate the processes in the data pipeline, such as data validation, testing, and deployment, to minimize human effort and enhance efficiency. Also, it will be essential to stay current with the latest tools and technologies in data engineering to use the most effective solutions for the codes and data pipelines.
Organizations that invest in code and data pipeline improvement are up to 30% more likely to adopt best practices in data management, leading to a more sustainable and scalable data infrastructure.
Data engineers should collaborate with data scientists for successful data-driven projects this year. They should share their knowledge with data scientists through proper documentation and code samples and allow them to understand the ability of the data pipeline and their roadmap to work with existing data. Also, data engineers should provide their feedback on the data and models they are working on to data scientists and get support to improve the quality and accuracy of data analysis.
Ensuring data and code quality is essential for the success of data engineering projects and the organization's ability to make informed decisions based on accurate and reliable data. By implementing best practices and tools to monitor and maintain data and code quality, organizations can ensure that their data systems are robust and efficient, leading to improved insights and better business outcomes. It also contributes to the success of projects, helps build trust with stake holders, and ensures compliance with industry regulations. Outsource to Phygital to access to ensure high-quality data and effective coding. With years of experience, Phygital can deliver your data engineering requirements in one place. Contact now!