Common Data Wrangling Challenges to Address

Common issue faced by DataEngineers: Data engineers face a variety of challenges in maintaining the integrity and efficiency of data pipelines. Common issues include job failures, slow pipeline execution, data quality problems, and issues with scheduling, memory management, schema changes, partitioning, API ingestion, data store consistency, and time constraints. Here's a breakdown of how to address these challenges: 1. Job Failures in ETL Pipelines: Identify the failure point: Use monitoring tools, logs analysis, and error handling mechanisms to pinpoint where the pipeline broke. Implement retry mechanisms: Automate retries for temporary errors to avoid discarding extracted data. Address root causes: Investigate the underlying reasons for the failure, such as invalid input, resource limitations, or code errors. Automate recovery: Implement automated recovery solutions to prevent manual intervention and reduce downtime. Document and version control: Maintain clear documentation and version control for the pipeline to aid in troubleshooting and prevent recurring issues. 2. Slow Data Pipeline Execution: Optimize data processing: Parallelize tasks, choose efficient data formats, and optimize data structures for faster processing. Monitor and alert: Proactively monitor pipeline metrics like execution time and resource usage to identify bottlenecks early. Resource allocation: Ensure adequate resources are allocated for each pipeline stage. Incremental loads: Consider implementing incremental loading strategies to improve efficiency. Regular maintenance: Keep resources updated and address potential contention issues on source or destination systems. 3. Data Quality Issues: Data validation: Implement data quality checks and validation rules throughout the ETL process to catch errors early. Data profiling: Profile datasets to identify anomalies, inconsistencies, missing values, and duplicate records. Data cleansing: Apply data cleansing techniques like deduplication, validation rules, and imputation to ensure accuracy. Automated tools: Utilize data quality management tools to detect and address quality issues at scale. Data audits: Conduct periodic audits to assess data quality and identify trends. 4. Scheduled Job Failures: Check scheduling configurations: Verify that the job schedule is correctly configured and that the scheduling tool is running as expected. Monitor the scheduler: Monitor the scheduling tool's logs for errors or warnings. Verify dependencies: Ensure that the scheduled job's dependencies are also functioning correctly. Test and validate: Test the job's execution manually and through automated testing to ensure it triggers as expected.

Over the last 5 years, I've spoken to 100+ Data Engineering leaders. They all struggle with the same data quality issues: 1. 𝐈𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐜𝐫𝐨𝐬𝐬 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Matching customers across various systems is a major challenge, especially when data sources use different formats, identifiers, or definitions for the same customer information. 2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Organizations often lack sufficient resources or clear foresight from management, leading to poorly designed data architectures that contribute to data quality problems over time. 3. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐜𝐡𝐞𝐦𝐚 𝐂𝐡𝐚𝐧𝐠𝐞𝐬: Frequent and undocumented schema changes, especially in production databases, disrupt data pipelines and lead to data integrity issues. 4. 𝐎𝐯𝐞𝐫𝐮𝐬𝐞 𝐨𝐟 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬: In some cases, converting everything to flexible data types (e.g., varchar) is a quick fix that can mask underlying data quality issues but makes the system difficult to maintain and troubleshoot over time. These common challenges underscore the importance of #datagovernance, #datamodeling, and overall #datastrategy. Anything I missed?

Bioinformatics isn’t just about fancy plots. It’s 90% data wrangling, 10% analysis. Making a heatmap? Easy. Cleaning the data? A nightmare. 🧵👇 1/ Data cleaning is the real challenge A bioinformatician once said: "Actually making that heatmap is easy. I spent most of my time getting the data right." And they were 100% right. 2/ Messy column names & formatting issues 🔹 Special characters in column names 🔹 Spaces vs. underscores vs. dashes 🔹 Different naming conventions for the same thing Example: MDA-MB-231 vs. MDAMB231 vs. MDA_MB_231 3/ Joining datasets is a detective game Ever tried merging two datasets only to realize that keys don’t match due to typos or formatting inconsistencies? Welcome to bioinformatics hell. 🔥 4/ Human errors are everywhere 🔹 Extra spaces ( "gene1 " vs. "gene1") 🔹 Mixed cases (TP53 vs. tp53) 🔹 Different date formats (MM/DD/YYYY vs. YYYY-MM-DD) 🔹 Hidden characters (trailing whitespace!) 5/ Tools that help with cleaning ✅ awk & sed for text manipulation ✅ pandas (str.strip(), str.lower()) ✅ dplyr::mutate_all(tolower) in R ✅ Fuzzy matching (agrep, fuzzywuzzy) I am going to use https://lnkd.in/eRzF_ASi 6/ Lessons learned ✅ Data wrangling takes way longer than actual analysis ✅ Always check for formatting inconsistencies before merging ✅ Expect human errors—your job is to detect & fix them 7/ Action item Next time you struggle with merging datasets, remember: it’s normal. Bioinformatics is just advanced data cleaning. What’s the worst data-wrangling nightmare you’ve faced? Let’s hear it! ⬇️ I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter https://lnkd.in/erw83Svn