How to handle schema changes in data ingestion with Smart Partition Evolution

🌊Data Lakes 101: Store First, Think Later If you’ve ever dealt with messy data from multiple sources and thought, “Man, I wish I had a magical place to dump all this raw info without worrying about formats or structure,” then welcome to the world of Data Lakes!   So, what exactly is a Data Lake?   Imagine a massive storage pond where you toss in everything — logs, JSON files, CSVs, images, videos, sensor data, you name it — no filters, no transformations, just raw data in its natural form. That’s a Data Lake in a nutshell.   Why not just use a database or data warehouse?   Good question! Databases and data warehouses are like well-organized filing cabinets — everything has a label and a slot. They require you to clean, structure, and format your data before storing it. This process is called ETL (Extract, Transform, Load).   Data lakes? They’re a bit more chill. You do ELT (Extract, Load, Transform) — meaning you first load the raw data, and then decide later how you want to shape or analyze it. Perfect for when you don’t know all your use cases upfront or you want to explore the data first.   What kind of data can you throw in?   Almost anything! • Structured data (tables, CSVs) • Semi-structured (JSON, XML) • Unstructured (images, audio, video) • Streaming data from IoT devices or apps • Logs from servers or apps   Because it’s schema-on-read, you don’t have to worry about how the data looks until you actually want to use it.   So next time your data looks like a wild jungle — logs here, JSON there, maybe a video or two — don’t panic. Toss it into a data lake, take a breath, and start exploring when you’re ready.   #DataLake #BigData #CloudComputing #DataEngineering #DataArchitecture #StreamingData #DataScience #ModernDataStack #BackendDevelopment

Ever wondered what's the architecture of Delta Lake? : Yep its Medallion architecture. 🔷 Understanding Medallion Architecture in Data Lakehouse 🔷 If you're building data platform with tools like Delta Lake or Databricks, you've likely come across the Medallion Architecture. A tiny breakdown of its three-tiered design: 🥉 1. Bronze Layer – Raw Data 🔹 Purpose: Ingest and store raw, unprocessed data. 🔹 Sources: Kafka, IoT devices, batch loads, APIs, databases. 🔹 Format: Parquet, Delta, JSON, etc. 🔹 Key Traits: Little to no transformation 🥈 2. Silver Layer – Cleansed & Enriched Data 🔹 Purpose: Clean and standardize data to make it usable. 🔹 Processes: 🔹Removing duplicates or invalid entries 🔹Standardizing formats and data types 🔹Joining with reference or other Bronze datasets 🔹 Key Traits: Structured, high-quality datasets ready for analytics or modeling 🥇 3. Gold Layer – Business-Level Data 🔹 Purpose: Provide curated, high-value datasets for reporting, dashboards, and decision-making. 🔹 Contents: 🔹Aggregated KPIs (e.g., monthly sales by region) 🔹Dimensional models (fact/dim tables) 🔹Clean, optimized datasets for BI or ML use 🔹 Users: Data Engineer, BI analysts, data scientists, business leaders 🔁 By organizing your data into Bronze → Silver → Gold, you improve quality, traceability, and usability at every step. That's it! #DataEngineering #DeltaLake #MedallionArchitecture #Databricks #DataPipeline #Lakehouse #BigData #Analytics #DataQuality #DataOps #DataIntegration #CloudComputing #BigData #Analytics #DeltaLake #Data #CGI #CGIINDIA

📌 𝗙𝗿𝗲𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 🚀 If you’re preparing for 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀, knowing the 𝗺𝗼𝗱𝗲𝗿𝗻 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 is a must. Here are the 𝟱 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 every Data Engineer should know 👇 🔹 𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗘𝗧𝗟 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗙𝗹𝗼𝘄: Source → Staging → Warehouse 𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: Simple BI dashboards & reports 𝗗𝗿𝗮𝘄𝗯𝗮𝗰𝗸: Can’t handle today’s huge or unstructured data 🔹 𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗙𝗹𝗼𝘄: Source → Raw Data Lake → Processing → Analytics 𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: Machine Learning + Advanced Analytics 𝗗𝗿𝗮𝘄𝗯𝗮𝗰𝗸: Without proper rules, it turns into a “data swamp” 🔹 𝟯. 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿𝘀: Batch + Speed + Serving 𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: Real-time + historical use cases (IoT, fraud detection) 𝗗𝗿𝗮𝘄𝗯𝗮𝗰𝗸: Expensive & hard to maintain two pipelines 🔹 𝟰. 𝗞𝗮𝗽𝗽𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗙𝗹𝗼𝘄: Stream Processing → Serving Layer 𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: Streaming-first systems (clickstreams, IoT devices) 𝗗𝗿𝗮𝘄𝗯𝗮𝗰𝗸: Not great for very large historical batch data 🔹 𝟱. 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲) 𝗟𝗮𝘆𝗲𝗿𝘀: 🟤 Bronze → Raw data ⚪ Silver → Clean & enriched data 🟡 Gold → Business-ready data 𝗪𝗵𝘆 𝗶𝘁’𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: ✔️ Ensures governance + data quality ✔️ Works for both structured & unstructured data ✔️ Supports analytics & machine learning in one place 💡 𝗤𝘂𝗶𝗰𝗸 𝗧𝗶𝗽: 👉 Think of : ETL --> old-school, Data Lake --> raw storage, Lambda/Kappa --> real-time, and Medallion --> modern standard. 📌 𝐒𝐚𝐯𝐞 & 𝐒𝐡𝐚𝐫𝐞 if you found this useful ✅  👉 Like, repost, and follow me — Yaseen Mohammad — for more Data Engineering content!  💬 Want to connect or have questions? You can book a 1:1 session with me here: 🔗 https://lnkd.in/d7EbJs9r Let’s grow together in tech! 💻📊 #dataengineering #azuredataengineering #bigdata #databricks #cloudcomputing #dataengineer #microsoft #learning

🚀 Optimizing Delta Lake with Liquid Clustering Managing large datasets in Databricks often comes down to one challenge: how do we organize data for fast queries without making writes expensive? Traditionally, we had two tools: 1️⃣ Partitioning – easy but rigid. Too many partitions = small files, too few = poor pruning. 2️⃣ Z-ORDER – better clustering inside files, but static. Needs periodic OPTIMIZE runs. 🔑 Enter Liquid Clustering: an adaptive approach that keeps your Delta tables efficiently organized without the maintenance overhead. 📊 How it works • You define clustering columns (for example: customer number, IMEI, service access id). • Delta Lake automatically maintains data organization along those dimensions as new data arrives. • Queries filtering on those columns skip irrelevant files, improving latency dramatically. • No need for rigid partition schemes or frequent manual Z-ORDERing. 💡 Example Instead of manually partitioning or running Z-ORDER, you simply cluster your table: “Cluster this table by customer number and IMEI” Now a query like “find all events for a given customer number” only scans a fraction of the data instead of the whole dataset. ✅ Benefits • Faster queries → better file skipping. • Less maintenance → automatic organization, no repeated OPTIMIZE. • Write-friendly → handles evolving data without partition pain. • Scalable → works across large, multi-tenant or high-cardinality workloads. Liquid Clustering is a game-changer for telecom, IoT, finance, and any workload with frequent upserts plus selective queries. #Databricks #DeltaLake #DataEngineering #BigData #Lakehouse #DataOptimization 🔗 Learn more: https://lnkd.in/gqTFMTkz

In data engineering, one of the most important questions we face is: 👉 How do we load only the new or changed data efficiently? Two widely used strategies are: Watermarking and Change Data Capture (CDC). Both avoid costly full reloads, but their use-cases differ. Here’s a breakdown ⬇️ 📍 Watermarking (Incremental Loads) ✅ Tracks the last processed point (often a timestamp, identity column, or version number). ✅ Easy to configure in ADF copy activity or Databricks notebooks. ✅ Best for append-only data: IoT events, transaction logs, telemetry, clickstreams. ⚠️ Limitation: If source data is updated or deleted, watermarking won’t capture it. 📍 Change Data Capture (CDC) ✅ Captures inserts, updates, and deletes directly from the source (via SQL Server CDC, Debezium, ADF Change Tracking, etc.). ✅ Ensures true data fidelity in Delta Lake – including Slowly Changing Dimensions (SCDs) and full auditing. ✅ Works best for OLTP systems, ERP/CRM migrations, and scenarios where business rules depend on changes. ⚠️ Slightly more complex setup and often requires extra infra (logs, Kafka, CDC tables). 🚀 In Real Projects Start with Watermarking → when the source is append-only and simplicity is key (ex: sales transactions, telemetry feeds). Move to CDC → when you need complete historical accuracy (SCD Type 2, audit logs, backtracking business events). Use Both Together → Watermarking as a baseline for detecting new records. CDC for handling updates/deletes on top of the incremental load. Example: A retail system where new sales come via Watermarking but price updates/cancellations are handled via CDC. 🔹 Key takeaway: It’s not always Watermarking vs CDC. In modern data platforms, you often need both strategiesworking together for a truly resilient data pipeline. #DataEngineering #AzureDataFactory #Databricks #DeltaLake #CDC #Watermarking #ETL #BigData #Azure #DataPipelines #Toronto #Database #sql #data #engineering #python #spark

🔎 Data Warehouse vs Data Lake vs Delta Lake – Technical Breakdown As data ecosystems evolve, choosing the right architecture becomes critical. Let’s go deeper 👇 ⚡ Data Warehouse (DW) Data Type: Structured (schema-on-write) Storage: Relational (tables, columns, indexes) Performance: Optimized for OLAP queries (star/snowflake schemas) Use Case: Business Intelligence, historical reporting, trend analysis Limitation: Expensive scaling, poor fit for semi/unstructured data 💧 Data Lake (DL) Data Type: Structured + Semi-structured + Unstructured (schema-on-read) Storage: Object storage (HDFS, S3, ADLS, GCS) Performance: Raw storage; needs external engines (Spark, Presto, Hive) for compute Use Case: Data science, ML training, raw ingestion from IoT/logs Limitation: No ACID transactions → risk of “data swamp” ⚡💧 Delta Lake (Lakehouse architecture) Data Type: Structured + Semi-structured (schema evolution + enforcement) Storage: Parquet + Transaction Log (_delta_log) Performance: Supports ACID transactions, time travel, and upserts/merges Use Case: Streaming + batch unification, ML pipelines, analytics with reliability Advantage: Combines low-cost scalability of Data Lake + governance/reliability of DW ✅ In Short DW → Strong governance, limited flexibility Data Lake → High flexibility, limited reliability Delta Lake → Balance = flexibility + reliability (Lakehouse model) 📌 Modern architectures are moving toward Delta Lake (Lakehouse) because it solves the weaknesses of both DW and DL. #DataEngineering #BigData #Databricks #Azure #AWS #GoogleCloud #DataWarehouse #DataLake #DeltaLake #Lakehouse

Day 10/30: Data Partitioning Strategies - Optimizing Performance and Cost Data partitioning is a fundamental technique for managing large datasets efficiently in cloud environments. Effective partitioning strategies directly impact query performance, storage costs, and overall system scalability. Understanding why partitioning matters: Partitioning organizes data into manageable segments based on specific column values, enabling efficient data pruning during queries. Without proper partitioning, systems must scan entire datasets regardless of the query scope, leading to unnecessary resource consumption and slower performance. Well-designed partitioning aligns with common query patterns and business requirements to maximize efficiency. Common partitioning approaches and their applications: Horizontal partitioning divides tables based on row values, typically using date ranges or categorical columns that frequently appear in WHERE clauses. Vertical partitioning separates columns into different files or tables, useful for isolating frequently accessed columns from less-used ones. Directory-based partitioning in data lakes uses folder structures to organize data, allowing engines like Spark and Synapse to skip irrelevant files during query execution. Implementation considerations for different services: In Azure SQL environments, partitioned tables benefit from improved maintenance operations and targeted data management. In Delta Lake, partitioning combined with Z-ordering provides multiple levels of data organization for optimal performance. For streaming data, partition strategies must balance write throughput with read efficiency, often using time-based partitions for IoT and event data. Best practices for implementation: Choose partition columns that have high cardinality and appear frequently in filter conditions. Avoid over-partitioning, which can create numerous small files that degrade performance. Monitor partition sizes regularly and reorganize data when partition skew develops. Align partition strategies with data retention policies to simplify archive and deletion processes. Common challenges and solutions: Teams often encounter the small file problem when partitioning generates too many tiny files, solved through compaction processes. Partition skew occurs when some partitions contain significantly more data than others, requiring redistribution strategies. Some implementations suffer from partition discovery overhead in systems with thousands of partitions, mitigated through partition pruning techniques. Tomorrow we will explore performance optimization techniques across Azure data services. What partitioning strategies have you found most effective for your specific workloads and data patterns? #AzureDataEngineer #DataPartitioning #PerformanceOptimization #DataArchitecture

📌 Save this post for your Data Engineering prep! 🚀 Modern Data Engineering Architectures You Can’t Ignore Data platforms have evolved - we’ve moved from simple ETL pipelines to advanced multi-layered cloud architectures. If you’re a Data Engineer (or preparing for interviews), here are the must-know architectures 👇 🔹 1. Basic ETL Architecture ➡️ Flow: Source → Staging → Target (Warehouse) ➡️ Use case: Traditional BI & reporting ⚠️ Limitation: Not scalable for today’s big data & unstructured workloads. 🔹 2. Data Lake Architecture ➡️ Flow: Source → Raw Data Lake → Processing → Analytics ➡️ Use case: ML + advanced analytics with structured & unstructured data ⚠️ Limitation: Without governance, it risks becoming a “data swamp.” 🔹 3. Lambda Architecture ➡️ Layers: Batch + Speed + Serving ➡️ Use case: IoT, fraud detection, real-time + historical analytics ⚠️ Limitation: Expensive & complex to maintain dual pipelines. 🔹 4. Kappa Architecture ➡️ Flow: Stream Processing → Serving Layer ➡️ Use case: Streaming-first systems (clickstream, IoT) ⚠️ Limitation: Weak for large-scale historical batch data. 🔹 5. Medallion Architecture (Lakehouse) ➡️ Layers: • Bronze = Raw Data • Silver = Cleansed & Enriched • Gold = Curated, Business-Ready ✔️ Benefits: Strong governance, handles all data types, supports analytics + ML. 💡 Key Takeaway: To design future-proof data platforms, go beyond ETL. Understand when & why to apply these architectures. 📌 Interview Tip: Expect questions like: 👉 Lambda vs Kappa in real-world terms? 👉 How would you implement Medallion on Databricks? 📌 Pro Tip: Don’t just read - build a mini-project (start with Medallion). Hands-on practice will set you apart. 👉 Which architecture do you think will dominate the next decade of data engineering — Lambda, Kappa, or Medallion? #DataEngineering #BigData #Databricks #SystemDesign #CareerGrowth #ETL #ELT #DataLake #CloudData

Modern Data Engineering Architecture – From Raw Data to Business Value In today’s data-driven world, architecture matters. A robust Data Engineering setup ensures that data is: ✔️ Accessible ✔️ Reliable ✔️ Scalable ✔️ Actionable 🔹 Key Components of Data Engineering Architecture: 1️⃣ Data Sources – Databases, APIs, IoT, Logs 2️⃣ Ingestion Layer – Batch & Streaming pipelines (Kafka, ADF, CDC) 3️⃣ Storage Layer – Data Lakes, Warehouses, or Lakehouse 4️⃣ Processing Layer – ETL/ELT, batch & real-time (Spark, PySpark, Databricks, dbt) 5️⃣ Orchestration Layer – Scheduling & workflow automation (Airflow, ADF, Azure Synapse) 6️⃣ Governance Layer – Security, Catalog, Lineage, Quality 7️⃣ Consumption Layer – BI dashboards, ML models, APIs ✨ This architecture is the backbone for analytics, ML, and AI adoption, empowering businesses to turn raw data into actionable insights. 💡 What does your Data Engineering stack look like today? #DataEngineering #Architecture #BigData #Databricks #Snowflake #Azure #ETL #Lakehouse #DataAnalytics

🚀 Day 13 – Azure Data Engineering & DataOps Knowledge Sharing Challenge 🥳 🔹 Topic: Azure Data Factory – Triggers & Scheduling ✅ What are Triggers in ADF? Triggers in Azure Data Factory (ADF) allow pipelines to run automatically instead of manual execution. They make ADF a true orchestration tool by enabling time-based, event-based, and dependency-based automation. --- 🔹 Types of Triggers in ADF 1️⃣ Schedule Trigger Runs pipeline at a specific time or frequency. Example: Load daily sales data into Azure Synapse at 2 AM. 2️⃣ Tumbling Window Trigger Processes data in fixed-sized, non-overlapping intervals. Ideal for time-series / streaming-like workloads. Example: Process IoT sensor logs every 15 minutes. 3️⃣ Event-Based Trigger Fires when an event occurs in storage. Example: Start pipeline when a new JSON file lands in Azure Blob Storage / ADLS Gen2. 4️⃣ Manual Trigger Used for testing & ad-hoc execution. Example: Data engineer manually runs pipeline after code deployment. --- 🔹 Why Use Triggers? ✅ Automation – No manual intervention needed. ✅ Consistency – Ensures timely and reliable data availability. ✅ Scalability – Handles batch + real-time data seamlessly. ✅ Orchestration – Helps manage multiple dependent pipelines. --- 🔹 Real-World Example Workflow Event Trigger – New file lands in ADLS → triggers pipeline to clean & load data into Delta Lake. Tumbling Window – Same pipeline processes logs in 15-min chunks for real-time dashboards. Schedule Trigger – At end of day, pipeline aggregates daily data → pushes to Power BI for reporting. ✨ With Triggers & Scheduling, ADF transforms into a serverless ETL orchestrator, supporting both batch and near real-time pipelines. #AzureDataFactory #AzureDataEngineering #DataOps #ETL #BigData #DataPipelines #AzureSynapse #AzureDatabricks #DataAutomation #CloudComputing #MicrosoftAzure