TM
TMTM
Three weeks into my role at Globalmart, an e-commerce company handling millions of daily transactions, I received a 2 AM call from our BI lead. The pipeline had failed because a source system added a new loyalty_points column to the customer data feed. By the time I fixed it, executive reports were three hours late.
This wasn't an isolated incident. We faced a recurring pattern:
Source systems changed schemas without notice
Pipeline broke
Engineers scrambled to fix it manually
Business teams missed critical data for decisions
Over six months, we experienced 59 pipeline failures due to schema changes—nearly 10 per month.
Globalmart's data came from multiple systems:
POS systems from 200+ retail locations
E-commerce platform
Inventory management
CRM tools
Third-party logistics
Each system was owned by different teams who often changed data structures without informing the data team.
The data model included interconnected entities: Customers, Orders, Products, Payments, Returns, and Shipping. Complexity lay not just in the number of tables but in their relationships—a single order involved updates across multiple entities.
Globalmart used a medallion architecture in Databricks on GCP:
Bronze Layer: Raw data landing
Silver Layer: Validated, cleansed data with business rules
Gold Layer: Aggregated, business-ready data
┌───────────────┐ ┌────────────────┐ ┌───────────────┐
│ Bronze Layer │ │ Silver Layer │ │ Gold Layer │
│ Raw Ingestion│────►│ Cleaned Data │────►│ Business │
│ │ │ │ │ Ready Data │
└───────────────┘ └────────────────┘ └───────────────┘
The pipeline assumed stable data structures—an assumption that proved problematic.
Schema changes took many forms:
Addition of new columns
Removal of columns
Data type changes
Column renaming
Restructuring of nested fields
The business impact was significant:
Table | Schema Changes | Pipeline Failures | Business Impact |
|---|---|---|---|
Customers | 24 | 17 | Customer analytics delayed 12 times |
Orders | 31 | 22 | Revenue reporting inaccurate 8 times |
Products | 18 | 11 | Inventory forecasting affected 7 times |
Payments | 14 | 9 | Finance reconciliation delayed 6 times |
The real business costs included:
Financial Impact: ~$20,000 per day for major reporting delays
Engineering Overhead: 15% of engineers' time spent fixing schema issues
Lost Trust: Decreasing confidence in the data platform
Scaling Problems: Growing frequency and severity of issues
During one sales event, a new promotional code field broke the pipeline, causing a six-hour analytics delay. The marketing team couldn't optimize ad spend in real-time, wasting an estimated $150,000.
Our initial attempts at solutions weren't effective:
Strict Schema Enforcement: Rejected non-conforming data, creating historical gaps
Manual Schema Updates: Couldn't keep pace with change frequency
Schema Validation Jobs: Still reactive rather than proactive
These approaches failed because:
Coordinating changes across dozens of source systems was nearly impossible
Manual updates couldn't scale
Strict enforcement sacrificed data continuity
Delta Lake offered promising capabilities for our schema evolution challenges:
Automatic Schema Evolution: Adapting to changes without intervention
Schema Enforcement: Maintaining data quality while allowing evolution
Time Travel: Accessing historical data versions
Transaction Log: Tracking all table changes
Our ideal solution needed to:
Automatically adapt to most schema changes
Maintain data integrity
Preserve historical consistency
Minimize processing latency
Provide visibility into schema changes
Maintain backward compatibility
As we analyzed further, we recognized schema evolution was part of a larger data engineering challenge. We needed to address:
Incremental Processing: Full table scans were becoming prohibitively expensive
Pipeline Orchestration: Managing processing dependencies
Data Quality: Ensuring consistency across evolving schemas
In Part 2, we'll look into the implementation of schema evolution with Delta Lake on Databricks, covering:
Schema evolution mechanisms
Handling different types of schema changes
Effective code patterns
Testing and validation approaches
Stay tuned for Part 2: Implementing Schema Evolution with Delta Lake on Databricks.
Tired of boring images? Meet the 'Jai & Veeru' of AI! See how combining Claude and Nano Banana Pro creates mind-blowing results for comics, diagrams, and more.
An honest, first-person account of learning dynamic pricing through hands-on Excel analysis. I tackled a real CPG problem : Should FreshJuice implement different prices for weekdays vs weekends across 30 retail stores?
What I thought would be a simple RBAC implementation turned into a comprehensive lesson in Kubernetes deployment. Part 1: Fixing three critical deployment errors. Part 2: Implementing namespace-scoped RBAC security. Real terminal outputs and lessons learned included
This blog walks you through how Databricks Connect completely transforms PySpark development workflow by letting us run Databricks-backed Spark code directly from your local IDE. From setup to debugging to best practices this Blog covers it all.
This blog unpacks how brands like Amazon and Domino’s decide who gets which coupon and why. Learn how simple RFM metrics turn raw purchase data into smart, personalised loyalty offers.
Learn how Snowflake's Query Acceleration Service provides temporary compute bursts for heavy queries without upsizing. Per-second billing, automatic scaling.
A simple ETL job broke into a 5-hour Kubernetes DNS nightmare. This blog walks through the symptoms, the chase, and the surprisingly simple fix.
A data engineer started a large cluster for a short task and couldn’t stop it due to limited permissions, leaving it idle and causing unnecessary cloud costs. This highlights the need for proper access control and auto-termination.
Say goodbye to deployment headaches. Learn how Databricks Asset Bundles keep your pipelines consistent, reproducible, and stress-free—with real-world examples and practical tips for data engineers.
Tracking sensitive data across Snowflake gets overwhelming fast. Learn how object tagging solved my data governance challenges with automated masking, instant PII discovery, and effortless scaling. From manual spreadsheets to systematic control. A practical guide for data professionals.
My first hand experience learning the essential concepts of Dynamic pricing
Running data quality checks on retail sales distribution data
This blog explores my experience with cleaning datasets during the process of performing EDA for analyzing whether geographical attributes impact sales of beverages
Snowflake recommends 100–250 MB files for optimal loading, but why? What happens when you load one large file versus splitting it into smaller chunks? I tested this with real data, and the results were surprising. Click to discover how this simple change can drastically improve loading performance.
Master the bronze layer foundation of medallion architecture with COPY INTO - the command that handles incremental ingestion and schema evolution automatically. No more duplicate data, no more broken pipelines when new columns arrive. Your complete guide to production-ready raw data ingestion
Learn Git and GitHub step by step with this complete guide. From Git basics to branching, merging, push, pull, and resolving merge conflicts—this tutorial helps beginners and developers collaborate like pros.
Discover how data management, governance, and security work together—just like your favorite food delivery app. Learn why these three pillars turn raw data into trusted insights, ensuring trust, compliance, and business growth.
Beginner’s journey in AWS Data Engineering—building a retail data pipeline with S3, Glue, and Athena. Key lessons on permissions, data lakes, and data quality. A hands-on guide for tackling real-world retail datasets.
A simple request to automate Google feedback forms turned into a technical adventure. From API roadblocks to a smart Google Apps Script pivot, discover how we built a seamless system that cut form creation time from 20 minutes to just 2.
Step-by-step journey of setting up end-to-end AKS monitoring with dashboards, alerts, workbooks, and real-world validations on Azure Kubernetes Service.
My learning experience tracing how an app works when browser is refreshed
Demonstrates the power of AI assisted development to build an end-to-end application grounds up
A hands-on learning journey of building a login and sign-up system from scratch using React, Node.js, Express, and PostgreSQL. Covers real-world challenges, backend integration, password security, and key full-stack development lessons for beginners.
Discover the 7 major stages of the data engineering lifecycle, from data collection to storage and analysis. Learn the key processes, tools, and best practices that ensure a seamless and efficient data flow, supporting scalable and reliable data systems.
This blog is troubleshooting adventure which navigates networking quirks, uncovers why cluster couldn’t reach PyPI, and find the real fix—without starting from scratch.
Explore query scanning can be optimized from 9.78 MB down to just 3.95 MB using table partitioning. And how to use partitioning, how to decide the right strategy, and the impact it can have on performance and costs.
Dive deeper into query design, optimization techniques, and practical takeaways for BigQuery users.
Wondering when to use a stored procedure vs. a function in SQL? This blog simplifies the differences and helps you choose the right tool for efficient database management and optimized queries.
Discover how BigQuery Omni and BigLake break down data silos, enabling seamless multi-cloud analytics and cost-efficient insights without data movement.
In this article we'll build a motivation towards learning computer vision by solving a real world problem by hand along with assistance with chatGPT
This blog explains how Apache Airflow orchestrates tasks like a conductor leading an orchestra, ensuring smooth and efficient workflow management. Using a fun Romeo and Juliet analogy, it shows how Airflow handles timing, dependencies, and errors.
The blog underscores how snapshots and Point-in-Time Restore (PITR) are essential for data protection, offering a universal, cost-effective solution with applications in disaster recovery, testing, and compliance.
The blog contains the journey of ChatGPT, and what are the limitations of ChatGPT, due to which Langchain came into the picture to overcome the limitations and help us to create applications that can solve our real-time queries
This blog simplifies the complex world of data management by exploring two pivotal concepts: Data Lakes and Data Warehouses.
demystifying the concepts of IaaS, PaaS, and SaaS with Microsoft Azure examples
Discover how Azure Data Factory serves as the ultimate tool for data professionals, simplifying and automating data processes
Revolutionizing e-commerce with Azure Cosmos DB, enhancing data management, personalizing recommendations, real-time responsiveness, and gaining valuable insights.
Highlights the benefits and applications of various NoSQL database types, illustrating how they have revolutionized data management for modern businesses.
This blog delves into the capabilities of Calendar Events Automation using App Script.
Dive into the fundamental concepts and phases of ETL, learning how to extract valuable data, transform it into actionable insights, and load it seamlessly into your systems.
An easy to follow guide prepared based on our experience with upskilling thousands of learners in Data Literacy
Teaching a Robot to Recognize Pastries with Neural Networks and artificial intelligence (AI)
Streamlining Storage Management for E-commerce Business by exploring Flat vs. Hierarchical Systems
Figuring out how Cloud help reduce the Total Cost of Ownership of the IT infrastructure
Understand the circumstances which force organizations to start thinking about migration their business to cloud