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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Schema Evolution: Handling Changing Data Structures
How to handle changing data structures, using a real public dataset whose schema changed three ways in three years: detecting an added column, a renamed column and five type changes, why a strict merge refuses and a permissive one keeps both spellings, reading by name, compatibility rules, and safe practices.
Handling Job Rejection and Bouncing Back
A rejection feels like a verdict, but interview data says single results are noisy: most candidates perform inconsistently from one interview to the next. Research also shows that treating yourself kindly after a failure increases the motivation to improve. Here is how to use both to bounce back and get better.
Handling Concept Drift in Live ML Systems
When the relationship a model learned changes, retraining is the usual answer, but how you retrain matters more than how often. On real bike-sharing data where demand grew by two-thirds under the same conditions, monthly retraining on all history barely helped; a sliding window or one feature halved the error.
Handling Many-to-Many Relationships in Power BI
Many-to-many relationships in Power BI, with Microsoft's guidance and a real bridge table: why every row can show the grand total, which relationship needs to filter both ways, why the rows then add up to far more than the total, duplicate names in dimensions, relating fact tables, and targets at a higher grain.
Handling Late-Arriving Data in Pipelines
A practical approach to late-arriving data: event time versus arrival time, lateness measured on 2.9 million real taxi trips, where a midnight report missed 0.81% of each day and a tail lingered for hours, then the fixes: waiting, reprocessing windows, streaming watermarks and late-arriving facts.
Power BI Error Handling in Power Query
Error handling in Power Query for Power BI: step-level and cell-level errors and their common causes, what Remove, Replace and Keep errors do, try with otherwise and catch for custom logic, and a real example where changing one column's type would have turned 10% of the rows into errors that Remove errors deletes.