How do you address data quality issues that arise due to external factors beyond your control in the project?

We are dealing with multiple upstream/external vendor feeds ingesting into our system. Consider the below classifications. 1. Data Format - This is the low-level fix. The predefined data expectations have inconsistencies in data formats or structures before loading. 2. Data issue - This is high-level validation. In this case, the data that arrived from upstream looks extremely reliable (Schema matches, header names and control file validations etc.). But the values received are complete junk. We could overcome this by setting up data quality monitoring processes to track key metrics and identify potential issues early on and configuring alerts to notify you when data quality falls below certain thresholds related to external factors.

In a project where we managed data feeds from multiple external providers, we set up AWS CloudWatch for monitoring data quality. Automated checks were implemented using AWS Lambda functions to validate data completeness, accuracy, and consistency as it arrived in Amazon S3. These checks would trigger notifications via Amazon SNS if data fell below predefined quality thresholds, allowing us to promptly investigate and resolve issues. This system significantly improved our data reliability and response time to quality issues.

How do you define data quality, and what an issue looks like? Start from the straightforward, low hanging fruit - validate the data schema: tables, columns, types should match your expectation. Implement schema evolution with alerts or data contracts to keep up with changes. Continue with the less straightforward, tests like value ranges or referential integrity. Finally, you can also consider anomaly detection, but beware of the expected noise - and consider treating anomaly alerts as something to be investigated with lower prio.

Data quality issues are like sneezing; they are often beyond our control. However, we can take necessary measures to manage them. The first step should be finding the root cause, rather than making changes in your data solutions. Try to get in touch with your upstream stakeholders who are responsible for the data sources. This involves fixing the data quality issue at the source itself. Additionally, data monitoring plays a key role in identifying issues as soon as they arise. We should make use of the data monitoring features available in ETL applications to mitigate the risk of failures.

Maintaining data integrity amidst external challenges is pivotal for project success as a data engineer. Identifying specific issues caused by unreliable data sources, unexpected third-party changes, or external environmental factors is crucial. These factors can introduce inconsistencies or delays, impacting data accuracy and completeness. By meticulously pinpointing these issues, data engineers can devise robust strategies to mitigate their effects, ensuring high-quality data remains at the core of every analysis and decision-making process.

In a recent project, I utilized AWS Glue DataBrew for data profiling to understand the quality of data from various sources. By analyzing data patterns, inconsistencies, and missing values, we identified outliers and anomalies. This statistical analysis helped establish a baseline for good quality data, which was crucial for ensuring consistent data standards across different sources. This profiling process allowed us to gain valuable insights into external factors affecting data quality, leading to more informed decision-making and improved data management practices.

If you don't know what to do, don't take vague advice from linkedin articles. Instead, consider what you really care about in the data, and the impact of something going wrong, and test for that. Don't waste time "data profiling" - this bottom up approach will result in an endless stream of work with unclear outcomes.

Navigating data quality challenges posed by external factors is pivotal for ensuring robust insights. External influences such as unreliable data sources or unexpected changes in APIs can compromise the integrity of project data. Utilizing data profiling becomes indispensable in this scenario, allowing for the meticulous identification of anomalies, assessment of data distributions, and understanding of dependencies affected by external factors. This proactive approach empowers data engineers to pinpoint specific issues and implement targeted strategies to uphold data accuracy and reliability throughout the project lifecycle.

Addressing data quality issues due to external factors can be managed effectively with data profiling. Start by conducting a comprehensive data profiling analysis to identify inconsistencies, missing values, and anomalies. Establish quality benchmarks and use automated profiling tools for continuous monitoring. Communicate findings with external data providers to establish clear data standards. Use the insights from profiling to clean and transform the data, ensuring it meets the required quality standards.

Consider the action to be taken when a test fails to pass. Do you want to recover quickly? consider using schema evolution into a raw layer where data can be curated and patched after the fact. Data contracts, in a situation of external sources we cannot influence, would simply mean discarding data that doesn't conform. This approach works well to recover from if you also load your bad data to an environment where analysts can curate it. Data contracts that discard the bad data to a trashbin outside the analysts' playground would make reintegration and recovery of the non confirming data a complex team project.

Ensuring data integrity in data engineering amidst external challenges is critical. An effective strategy involves establishing robust protocols. This starts with implementing rigorous data validation procedures to promptly detect anomalies. Automated monitoring systems play a pivotal role in proactively identifying and swiftly resolving data discrepancies. Additionally, maintaining transparent communication channels with stakeholders and external data providers is essential to foresee and mitigate potential disruptions effectively. By integrating these protocols into project workflows, teams can strengthen data integrity, ensuring consistent and reliable insights that support informed decision-making throughout the project lifecycle.

Establishing data quality protocols involves defining clear standards for accuracy, completeness, and consistency. Implement procedures for data entry, validation, and cleaning to maintain these standards. Assign specific roles for managing data quality and set up regular monitoring and auditing processes. Use automated tools for efficient tracking and compliance. Continuously review and update protocols based on feedback and evolving needs.

Its the most important point. Maintain open and regular communication with stakeholders to keep them informed about potential external factors that could impact data quality. For instance, if a third-party data provider is experiencing outages, promptly notify your team and stakeholders about the issue, its potential impact, and any mitigation steps being taken. This transparency helps manage expectations and builds trust.

Establishing clear channels between stakeholders, data providers, and project teams is crucial for promptly addressing and mitigating issues as they arise. This ensures that all parties are informed and aligned on the impact of external factors on data quality, allowing for proactive adjustments to processes and protocols. By fostering transparency and collaboration, organizations can navigate these challenges more effectively, maintaining data integrity and achieving project success.

If you are sending your bad data to a separate table in the raw layer, or if you are loading to a single table with schema evolution, you will need to curate what happens with changes or inconsistencies. The analyst who has the data in their raw layer can communicate with the producing team the issues and figure out how to proceed.

Through Data cleansing we can correct inaccuracies, filling in missing values, and removing duplicates to ensure data accuracy and consistency. It also includes standardizing formats to maintain uniformity across datasets. Validation is performed to ensure data meets predefined quality standards. These steps improve data reliability and make it more useful for analysis and decision-making.

In a complex project involving data from various sources, we faced numerous data quality issues, such as duplicates and incorrect data formats. We implemented a comprehensive data cleansing strategy leveraging multiple cloud services. Using AWS Glue for ETL jobs, we performed initial data parsing and deduplication. To further enhance data quality, we integrated Azure Data Factory for orchestrating data flows and Azure Databricks for advanced data processing and cleansing operations. Additionally, Google Cloud's Dataflow was used for real-time data processing to catch and correct errors as they occurred.

As a data engineer, I've learned that continuous improvement is crucial for managing external data quality issues. We regularly review and update our data management processes, adapting to new challenges. I've found that investing in advanced monitoring tools helps us catch issues early. We've also improved our communication protocols with data providers, which has reduced misunderstandings. Staying updated with new technologies has been key in enhancing our data quality checks. This proactive approach has significantly improved our ability to maintain high-quality data standards despite external factors.