Optimizing Microsoft Fabric for Data Engineering

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼-𝗕𝗮𝘀𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 : 𝗣𝗮𝗿𝘁 𝟳 🔥 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: Your pipeline ingests JSON data from an API every hour, but the schema changes frequently. How will you handle this? 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 👩🏻💻: Will use schema-on-read with Spark to infer fields dynamically and store the raw payload in a Bronze table. Then I’d use Auto Loader or schema evolution features in Delta to handle changes gracefully. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: You need to migrate 5 TB of on-prem SQL Server data to Azure Data Lake. How would you design the migration? 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 👩🏻💻: Use Azure Data Factory with parallel copy activities, compress data before transfer, and land it in ADLS Gen2. Then I’d validate row counts and checksums to ensure no data loss. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: You notice that a Spark job is taking too long because of data skew. How will you solve this? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: Find the skewed keys, apply salting or repartitioning and leverage techniques like broadcast join for smaller tables. This balances load across executors and improves performance. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: Your Delta table has grown very large and queries are slowing down. What steps will you take? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: I’d run OPTIMIZE with Z-ORDER on frequently queried columns, perform VACUUM to clear old files and partition data appropriately to improve query performance. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: You need to design a near real-time dashboard from streaming IoT data. How would you do this? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: I’d use Azure Event Hub → Stream Analytics/Spark Structured Streaming → write to Delta/Power BI dataset. This enables ingestion, processing and visualization with minimal latency. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: A batch job failed halfway through processing. How do you ensure data consistency? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: I’d design idempotent pipelines with checkpointing, use Delta transactions to roll back partial writes, and reprocess only the failed batch instead of restarting everything. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: How would you secure sensitive data like SSNs or credit card numbers in your pipeline? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: I’d apply column-level encryption or hashing at ingestion, mask values for analytics and use Azure Key Vault to manage secrets and encryption keys securely. ________________________________________________ 1000+ Recently asked Data Engineering Interview Questions for you...collected by me : https://lnkd.in/dhgwvv_w #dataengineering

Data Lakehouse with Microsoft Fabric – The Future of Unified Analytics One of the biggest challenges organizations face today is managing data that comes in from all directions—structured, semi-structured, and unstructured. Add to that the need for both real-time and batch processing, and it’s clear why many data teams struggle with complexity, silos, and inefficiencies. This is exactly where the Data Lakehouse architecture with Microsoft Fabric shines. It combines the strengths of a Data Lake and a Data Warehouse, offering a single, unified platform for all types of data and use cases. Ingestion Data doesn’t arrive in one format. It streams from IoT devices, sensors, and applications in real time, while batch data comes from relational databases, files, and pipelines. Fabric supports both modes, ensuring nothing gets left behind. Storage Everything lands in OneLake, a single storage layer. Whether it’s raw files, semi-structured logs, or large relational datasets, OneLake provides a centralized repository that scales with your business. No more juggling multiple storage systems. Transformation This is where the magic happens. Using Dataflows Gen2, Spark Notebooks, Stored Procedures, or ML models, raw data evolves into enriched, curated, and ready-to-use datasets. The lakehouse approach ensures data remains flexible but also structured enough for analytics. Modeling Before data is consumed, it often passes through a semantic model or a relational serving layer. This step aligns business rules, definitions, and logic so users across the company can trust the data they work with. Visualization The final step is turning transformed data into insights. With Power BI, business users can explore interactive dashboards, reports, and analytics without worrying about the underlying complexity. Data scientists and analysts can also plug in advanced models directly. Why this matters: A Data Lakehouse built on Microsoft Fabric is not just another buzzword. It solves the core problem of data silos by providing: -> A single platform for all types of data. -> The flexibility of a lake with the performance of a warehouse. -> Real-time and batch ingestion in one ecosystem. -> Built-in governance, security, and scalability. #MicrosoftFabric #DataLakehouse #DataEngineering #BigData #DataScience #Analytics #CloudComputing #Azure #PowerBI #BusinessIntelligence #DataStrategy #DataAnalytics #DataDriven #ModernDataStack #DataManagement #CloudData #MachineLearning #AI #DataTransformation #DataIntegration #StreamingData #BatchProcessing #IoTData #OneLake #AzureData #DataOps #ETL #ELT #DataPipeline #DataGovernance #DataSecurity #DataQuality #DataWarehouse #DataLake #DigitalTransformation #DataPlatform #DataSolutions #FutureOfData #RealTimeData #DataVisualization #AdvancedAnalytics #CloudSolutions #EnterpriseData #BI #SQL #DataScienceCommunity #DataEngineer #DataAnalyst #Fabric #ScalableData #DataInsights #Innovation #DataInfrastructure #SmartData #DataEcosystem

Day 9 - Post 3 → Ingesting Data into OneLake (Common Pitfalls & Solutions) We’ve now seen what OneLake is and how to ingest into it. But in real projects, ingestion isn’t always smooth. Let’s talk about the challenges teams face — and how Fabric + OneLake solves them. ❌ Problem 1: Data Silos & Duplication Teams often copy the same data into multiple lakes/warehouses. Costs spiral, governance breaks, and multiple “truths” emerge. ✅ Solution: OneLake Shortcuts (OneCopy) → Data stays at source, but appears inside OneLake. No duplication Single source of truth Unified security ❌ Problem 2: Slow, Manual Uploads Analysts drop CSV/Excel files manually → works at first, but doesn’t scale. Leads to schema mismatches, errors, and stale data. ✅ Solution: Pipelines + Connectors → 200+ sources, scheduling, monitoring, error handling. Automated, repeatable ingestion Standardized schema Integration with transformations (Notebooks, Dataflows) ❌ Problem 3: Lack of Real-Time Freshness Traditional ETL → batch jobs run once daily → business decisions lag behind. Real-time events (IoT, transactions, clickstream) need faster refresh. ✅ Solution: Event-driven pipelines & shortcuts → Stream fresh data using Event Hubs/Kafka → into OneLake Use Shortcuts to access external data “as-is” without waiting for copy jobs ❌ Problem 4: Hard to Govern Across Environments In dev/test/prod → pipelines often break due to hardcoded paths. Difficult to enforce consistent security policies. ✅ Solution: Variables + OneLake Access Policies → Environment-specific shortcuts handled via variables Policies ensure fine-grained, consistent access ❌ Problem 5: Integration with BI tools Ingested data is often locked in formats hard for analysts to query. Extra step: export → import into Power BI. ✅ Solution: Lakehouse/Warehouse-native ingestion → Data lands in OneLake tables, instantly queryable in Power BI With Direct Lake mode, BI queries skip refresh → sub-second analytics 💡 Takeaway: Ingestion is not just about moving data. It’s about governance, freshness, cost, and usability. With connectors, APIs, pipelines, and shortcuts — OneLake turns ingestion into a strategic enabler for analytics. #Fabric30Days #DirectLake #MicrosoftFabric #PowerBI #Security #SSO #RLS #DataGovernance #Lakehouse #DataSecurity #DeltaLake #OneLake #FabricCommunity #Warehouse #DataAnalytics #Parquet #DataEngineering #Spark #AnalyticsEngineering #DataArchitecture #ETL #DataOps #SemanticModel #DataWarehousing #Azure

𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Data-driven landscape requires scalable and efficient solutions to manage, process, and analyze large scale volumes of data. Enterprise Data Analytics Solutions built on big data processing techniques enable businesses to extract actionable insights, improve decision-making, and drive innovation. 𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗮 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 An effective enterprise data analytics architecture typically includes: 𝟭- 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 Data is generated from business applications, IoT devices, customer interactions, and external systems. This includes both structured and unstructured data. 𝟮- 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗘𝗧𝗟 Data must be ingested from various sources, transformed into usable formats, and loaded into analytical systems. ETL (Extract, Transform, Load) pipelines automate this process, ensuring consistency and scalability. 𝟯- 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Data is stored in scalable repositories such as data lakes and data warehouses. Processing frameworks like Apache Spark enable distributed, in-memory computation for large-scale analytics. 𝟰- 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Once processed, data is modeled to support business intelligence and visualized through dashboards and reports to inform strategic decisions. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 Enterprise solutions leverage several key techniques: - Batch Processing for handling large volumes of historical data - Stream Processing for real-time analytics on continuous data flows - Distributed Computing to scale data processing across multiple nodes - Machine Learning & AI for predictive and prescriptive analytics 𝗨𝘀𝗶𝗻𝗴 𝗔𝘇𝘂𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 While many platforms can support big data analytics, Azure offers a robust ecosystem as an example: - Azure Synapse Analytics – Combines big data and data warehousing - Azure Data Lake Storage Gen2 – Stores massive volumes of raw data - Azure Stream Analytics – Real-time data processing - Azure Data Factory – orchestration and manage data-driven workflows   - Azure Databricks – Apache Spark-based advanced analytics 𝗞𝗲𝘆 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 🟩 Faster decision-making 🟩 Streamlined operations 🟩 Better customer insights 🟩 Increased revenue 🟩 Greater business agility 🟩 Stronger compliance 🟩 Cost optimization 🟩 Competitive advantage #BigData #SystemDesign #Azure #CloudComputing #MicrosoftFabric

🚀 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐢𝐞𝐬 🔍 As data engineers, we spend countless hours fine-tuning query performance, optimizing storage, and controlling costs. Let’s dive into the evolution of table optimization in Databricks and see why Liquid Clustering is a true game-changer. 🏗️ 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐄 + 𝐙-𝐎𝐑𝐃𝐄𝐑 (𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡) OPTIMIZE sales_table ZORDER BY (customer_id, region); ✔️ Co-locates data for better query performance ❌ Heavy compute and full-table scans ❌ Manual scheduling and maintenance ❌ Poor fit for streaming and fast-changing data 💧𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐢𝐪𝐮𝐢𝐝 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠? Liquid Clustering is Databricks’ next-gen table optimization feature, recommended for: ✅ Streaming Tables (STs) ✅ Materialized Views (MVs) 𝐈𝐭’𝐬 𝐢𝐝𝐞𝐚𝐥 𝐟𝐨𝐫: Tables filtered by high-cardinality columns Skewed data distributions Rapidly growing datasets Tables with changing access patterns Cases where partitioning creates too many or too few partitions 🔑𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 Enable it during table creation or on an existing table: ALTER TABLE table_name SET TBLPROPERTIES ( 'delta.liquidClustering.enabled' = true, 'delta.liquidClustering.columns' = 'customer_id, region'); ALTER TABLR table_name CLUSTER BY 'customer_id' 𝐎𝐫 𝐬𝐢𝐦𝐩𝐥𝐲: CREATE TABLE table_name CLUSTER BY (customer_id); CLUSTER BY now activates Liquid Clustering Once enabled, Databricks continuously handles clustering and compaction. ✔️ Continuous background clustering & compaction ✔️ No manual OPTIMIZE jobs needed ✔️ Works seamlessly with streaming & batch ✔️ Flexible clustering key changes anytime ✔️ Low maintenance and high scalability 🚀 𝐈𝐟 𝐖𝐞 𝐀𝐥𝐫𝐞𝐚𝐝𝐲 𝐇𝐚𝐯𝐞 𝐙-𝐎𝐑𝐃𝐄𝐑, 𝐖𝐡𝐲 𝐃𝐨 𝐖𝐞 𝐍𝐞𝐞𝐝 𝐋𝐢𝐪𝐮𝐢𝐝 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠? Both Z-Ordering and Liquid Clustering optimize data layout, but Liquid Clustering was built to solve Z-Ordering’s pain points: 💡 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐙-𝐎𝐫𝐝𝐞𝐫𝐢𝐧𝐠 🔸 Static Optimization: Point-in-time operation, stale as data grows 🔸 Manual Maintenance: Requires scheduling and tuning 🔸 Rigid Layout: Doesn’t adapt to changing workloads 🔸 Partition Dependency: Tied to early partition design choices 🔍 𝐖𝐡𝐲 𝐈𝐭’𝐬 𝐚 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐞𝐫 💡 Always Fresh: Data stays optimized automatically 💡 No Partitions Needed: No rigid boundaries 💡 Smarter Layouts: Space-filling curves for multi-column optimization 💡 Hands-Free Maintenance: Databricks manages it all 💡 Future-Proof: Change clustering keys anytime, system adapts 🔥 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞: Liquid Clustering transforms Delta tables into self-organizing, streaming-ready systems. It’s Z-ORDER without the hassle — less maintenance, lower costs, and consistent performance at scale. #Databricks #DeltaLake #LiquidClustering #DataEngineering #BigData #StreamingData #AzureDatabricks #Optimization

Virtual Single Data Space vs Global Namespace — not the same but synergistic! Virtual single data space - (e.g., Apace DataManager): An overlay/catalog + control plane that makes many silos look unified for discovery & policy (scan/index, copy/sync/verify, lifecycle). It doesn’t replace your filesystems; it references them (even offline media). Think: one searchable map of all your data with buttons to move/verify it. Global namespace: A single, mountable filesystem (one POSIX/SMB path) that apps read/write through. The platform handles placement/migration, so paths are consistent across sites/clouds. Think: one filesystem that spans locations. Representative vendors: Hammerspace, IBM Storage Scale (Spectrum Scale), WEKA, VAST Data, DDN EXAScaler/Lustre. Simple analogy: Virtual data space = Google Maps of your data (see everything, plan routes, schedule movers, confirm delivery). Global namespace = A single super-highway your I/O actually drives on. When to pick which Choose a virtual single data space (Apace DataManager) when you want: • One view across NAS/object/tape (incl. offline) • Non-disruptive governance: scan/index, checksum-verified copy, tiering w/o re-platforming • Edge→core orchestration and “send metadata first” patterns Choose a global namespace (Hammerspace / IBM Storage Scale / WEKA / VAST / DDN) when you need: • One mount point for apps everywhere • Live mobility/placement with consistent POSIX/SMB semantics • Developers to stop caring where the bytes live Best practice - use both: Put a global namespace in front of active, online storage for app I/O. Use Apace DataManager as the virtual data space to unify everything (including legacy/offline), drive verified movement & lifecycle, and power search/analytics across silos. In AI racks, keep rack-local staging & policies inside the pod (no hair-pin); let the global namespace publish what needs sharing across sites. Bottom line: A virtual single data space (Apace DataManager) unifies control & visibility; a global namespace (Hammerspace/IBM Storage Scale/WEKA/VAST/DDN) unifies the I/O path. Pair them for simple governance and seamless app access. #data #storage #ApaceDataManager #Hammerspace #IBMStorageScale #WEKA #VASTData #DDN #datamanagement #AIinfrastructure #edgecomputing #NVMe #S3 #EdgeAI

Day 181 - 🔹 𝗦𝘁𝗲𝗽 𝟮: 𝗖𝗵𝗼𝗼𝘀𝗲 𝗬𝗼𝘂𝗿 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗵𝗼𝗱 (𝘍𝘳𝘰𝘮 𝘵𝘩𝘦 𝘴𝘦𝘳𝘪𝘦𝘴: 𝘚𝘵𝘦𝘱-𝘣𝘺-𝘚𝘵𝘦𝘱 𝘎𝘶𝘪𝘥𝘦 – 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘯𝘨 𝘋𝘢𝘵𝘢 𝘐𝘯𝘨𝘦𝘴𝘵𝘪𝘰𝘯 𝘪𝘯𝘵𝘰 𝘍𝘢𝘣𝘳𝘪𝘤 𝘓𝘢𝘬𝘦𝘩𝘰𝘶𝘴𝘦) Once you’ve defined why you’re automating data ingestion (Step 1), the next big question is:  👉 “𝘏𝘰𝘸 𝘥𝘰 𝘐 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘮𝘰𝘷𝘦 𝘥𝘢𝘵𝘢 𝘪𝘯𝘵𝘰 𝘍𝘢𝘣𝘳𝘪𝘤 𝘓𝘢𝘬𝘦𝘩𝘰𝘶𝘴𝘦?” Microsoft Fabric gives us multiple options—and the right choice depends on your data source, update frequency, and business needs. Let’s break down the three main methods: 1️⃣ 𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄𝘀 𝗚𝗲𝗻𝟮 Think of Dataflows as the “Excel Power Query” of Fabric, but on steroids.  • Perfect for business analysts or data engineers who want a 𝗻𝗼-𝗰𝗼𝗱𝗲/𝗹𝗼𝘄-𝗰𝗼𝗱𝗲 approach.  • Great for 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 or when you need transformations during ingestion.  • Works well for connecting to SQL Server, SaaS apps, or flat files.  ⚠️ Limitation: Not ideal for massive, high-volume data pipelines. 2️⃣ 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 This is Fabric’s answer to Azure Data Factory.  • Built for 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 and handling 𝗹𝗮𝗿𝗴𝗲𝗿-𝘀𝗰𝗮𝗹𝗲 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻.  • Lets you set up 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗹𝗼𝗮𝗱𝘀, 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴, 𝗿𝗲𝘁𝗿𝗶𝗲𝘀, 𝗲𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴—all the hallmarks of automation.  • Works best when you need to coordinate multiple steps (e.g., extract from SQL Server, land into OneLake, then trigger a transformation).  ⚠️ Slightly steeper learning curve than Dataflows, but far more powerful. 3️⃣ 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁𝘀 This one’s a game-changer.  • Instead of physically moving data, Shortcuts 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗹𝗶𝗻𝗸 between your Lakehouse and another storage system (like ADLS Gen2, OneLake, or external data).  • Zero-copy = faster, cheaper, and no duplication headaches.  • Best when data already lives in cloud storage and you just want Fabric to see it.  ⚠️ Not a transformation tool—more about 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗻𝗱 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻. ✨ 𝗛𝗼𝘄 𝘁𝗼 𝗖𝗵𝗼𝗼𝘀𝗲?  • If you need 𝘀𝗶𝗺𝗽𝗹𝗲, 𝗾𝘂𝗶𝗰𝗸 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 → go with 𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄𝘀 𝗚𝗲𝗻𝟮.  • If you need 𝗿𝗼𝗯𝘂𝘀𝘁, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 → choose 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀.  • If your data already exists in storage and you want 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 → use Shortcuts. Most real-world Lakehouse projects end up using a 𝗺𝗶𝘅 𝗼𝗳 𝗮𝗹𝗹 𝘁𝗵𝗿𝗲𝗲. For example: pipelines for core SQL Server loads, shortcuts for external storage, and dataflows for enrichment. 👉 In the next post, we’ll dive into Step 3: 𝗦𝗲𝘁 𝗨𝗽 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗟𝗼𝗮𝗱𝘀—because pulling the entire database every night is one of the fastest ways to burn both time and compute. #MicrosoftFabric #sqlserver #businessintelligence #datawarehousing #dataanalytics

Day 9 - Post 1 → Ingesting Data into OneLake (Why It Matters + Options) When working with Microsoft Fabric, everything begins with data ingestion. If you don’t get the data into OneLake, you can’t transform, analyze, or visualize it. But ingestion is not just about “loading tables and files” It’s about: Choosing the right method depending on freshness, scale, and governance Avoiding unnecessary duplication and costs Ensuring that all Fabric workloads (Power BI, Synapse, Data Science, Real-Time) see the same single source of truth ⚡ Why OneLake is different Unlike traditional data lakes where you manually manage multiple storage accounts, OneLake is: Universal → one logical lake across your org Integrated → every workspace, Lakehouse, Warehouse connects directly Governed → unified policies, security, and monitoring That means how you ingest into OneLake directly affects how smoothly downstream teams (BI, ML, Finance, Operations) can consume data. 🚀 Ingestion Options in OneLake There are three primary ways to bring data in: 1️⃣ Connectors & Pipelines (Fabric Data Factory) 200+ connectors (SQL, Salesforce, SAP, Snowflake, Blob, ADLS, etc.) Managed pipelines with scheduling, monitoring, and transformations 2️⃣ Uploads & APIs Drag & drop files into Lakehouse Explorer for ad-hoc use cases Programmatic ingestion via the OneLake REST API for automation and DevOps 3️⃣ Shortcuts (OneCopy) Instead of moving/copying, create a shortcut to existing data in ADLS Gen2, Amazon S3, Dataverse, or SharePoint Data appears in OneLake but stays in the source system → no duplication, no delay 💡 The beauty of Fabric is choice: you don’t have to force every dataset through the same path. You can mix and match ingestion techniques depending on: Freshness (batch vs near real-time) Scale (small Excel vs multi-TB transactional DB) Governance (securely managing shared data across teams) 🔑 Takeaway: Ingestion into OneLake is the foundation of your data strategy. Pick the wrong method → you risk duplication, latency, and rising costs. Pick the right one → you unlock seamless analytics across the entire Fabric ecosystem. In the next posts, we’ll go deeper into: 🔹 How each ingestion method works in detail 🔹 Common challenges & best practices to avoid pitfalls #Fabric30Days #DirectLake #MicrosoftFabric #PowerBI #Security #SSO #RLS #DataGovernance #Lakehouse #DataSecurity #DeltaLake #OneLake #FabricCommunity #Warehouse #DataAnalytics #Parquet #DataEngineering #Spark #AnalyticsEngineering #DataArchitecture #ETL #DataOps #SemanticModel #DataWarehousing #Azure

🔷 100 Days of Azure Data Engineering – Day 28 🚀 Window Transformation in ADF The Window transformation in Azure Data Factory (ADF) lets you perform analytics across rows of data without collapsing them into groups. 👉 Unlike Aggregate (which summarizes into fewer rows), Window preserves all rows and just adds new calculated columns. 🔐 Key Settings Over (Partition By) → Defines groups (e.g., CustomerID, CategoryID). Sort → Defines row order (e.g., by OrderDate ASC for cumulative totals). Range By → Defines the frame (e.g., from start → current row = running totals). Columns → Expressions like lag(Sales,1) or sum(Sales) that create new columns. 🛑 Sample dataset CustomerID,CategoryID,ProductID,OrderDate,SalesAmount C001,CatA,P001,01-01-2025,200 C001,CatA,P002,15-01-2025,300 C001,CatA,P003,01-02-2025,150 C002,CatA,P001,05-01-2025,400 C002,CatB,P004,20-01-2025,500 C002,CatB,P005,10-02-2025,250 C003,CatB,P004,08-01-2025,700 C003,CatB,P006,15-02-2025,350 C004,CatC,P007,12-01-2025,600 C004,CatC,P008,18-02-2025,450 C005,CatC,P009,20-02-2025,300 C005,CatC,P010,01-03-2025,550 🧮 Window Settings 👉Partition: CategoryID 👉 Sort: SalesAmount DESC 👉Expressions: Cummulative Sales = sum(SalesAmount) Previous Order Sales = lag(SalesAmount,1) Next Order Sales = lead(SalesAmount,1) Difference from Previous = SalesAmount - lag(SalesAmount,1) Difference from Next Sales = SalesAmount - lead(SalesAmount,1) Rank Products = rank() RowNumber Product =rowNumber() Least product = least(ProductID) 📊 Example Outputs CustomerID,CategoryID,ProductID,OrderDate,SalesAmount,cumm_sales,prev_sales,next_sales,diff_prev_sales,diff_next_sales,Least_product,Sales_Rank,Sales_Rank_Rownum C002,CatA,P001,2025-01-05,400,400,,300,400,100,P001,1,1 C001,CatA,P002,2025-01-15,300,700,400,200,-100,100,P001,2,2 C001,CatA,P001,2025-01-01,200,900,300,150,-100,50,P001,3,3 C001,CatA,P003,2025-02-01,150,1050,200,0,-50,150,P001,4,4 C003,CatB,P004,2025-01-08,700,700,,500,700,200,P004,1,1 C002,CatB,P004,2025-01-20,500,1200,700,350,-200,150,P004,2,2 C003,CatB,P006,2025-02-15,350,1550,500,250,-150,100,P004,3,3 C002,CatB,P005,2025-02-10,250,1800,350,0,-100,250,P004,4,4 C004,CatC,P007,2025-01-12,600,600,,550,600,50,P007,1,1 C005,CatC,P010,2025-03-01,550,1150,600,450,-50,100,P007,2,2 C004,CatC,P008,2025-02-18,450,1600,550,300,-100,150,P007,3,3 C005,CatC,P009,2025-02-20,300,1900,450,0,-150,300,P007,4,4 🚀 Summary ⚡ Window Transformation = Analytics without Aggregation. ⚡Lets you calculate: Nulls → lag() / lead() return NULL. Use defaults: lag(Sales,1,0). Rank vs DenseRank → rank() leaves gaps if same rank occurs (1,2,2,4) whereas dense_rank() doesn’t (1,2,2,3). NTile → ntile(4) splits into quartiles. Great for customer segmentation. (e.g., top 25% customers). RowNumber vs Rank → rowNumber() always unique. Rank handles ties. Frame Trap → last_value() may return current row unless you set unbounded preceding/following. #100DaysOfDataEngineering #WindowTransformation #ADFSeries #ETL #Learning

🔷 100 Days of Azure Data Engineering – Day 27 🚀 Aggregation Transformation in ADF 1️⃣ What is Aggregation Transformation? The Aggregation transformation in ADF Mapping Data Flows works like SQL GROUP BY. It helps you summarize big datasets into small, meaningful insights. 👉 Example: Instead of looking at 1 million sales rows, you can just see total revenue per region or average order value per product. 2️⃣ Group By & Aggregates Group By → How to divide data (Region, Product, Customer, Date). Aggregates → What calculation to do inside each group (SUM, AVG, MAX, MIN, COUNT, COUNTDISTINCT). 💡 If you Group By Region and calculate SUM(Amount) → you get total sales per region. 💡 If you don’t Group By anything → entire dataset becomes one group → one summary row. 3️⃣ Real-Time Scenarios (Expressions) 📊 Sales Transaction Dataset 🟦 Region: East | Product: Laptop | Date: 2025-01-01 | Amount: 800 | Discount: 50 | CustID: C101 🟦 Region: East | Product: Laptop | Date: 2025-01-01 | Amount: 600 | Discount: 30 | CustID: C102 🟦 Region: West | Product: Mobile | Date: 2025-01-01 | Amount: 200 | Discount: 10 | CustID: C103 🟦 Region: East | Product: Laptop | Date: 2025-01-02 | Amount: 900 | Discount: 0 | CustID: C101 🔹 Expressions 1️⃣ Region-wise Total Revenue groupBy(Region), TotalRevenue = sum(Amount) 2️⃣ Daily Net Revenue (Amount – Discount) groupBy(Region, SaleDate), NetRevenue = sum(Amount - Discount) 3️⃣ Customer Purchase Analysis groupBy(CustomerID), TotalSpend = sum(Amount - Discount), UniqueProducts = countDistinct(Product) 4️⃣ Highest Sale per Product-Day groupBy(Product, SaleDate), MaxSale = max(Amount) 5️⃣ Average Order Value (ignore zero-amounts) groupBy(Product), AvgOrderValue = avg(iif(Amount > 0, Amount, NULL())) 6️⃣ Revenue Contribution % by Product (within Region) groupBy(Region, Product), ProductRevenue = sum(Amount), TotalRegionRevenue = sum(Amount) over(Region), ContributionPct = (sum(Amount) / TotalRegionRevenue) * 100 7️⃣ Customer Retention Flag (multi-day buyers) groupBy(CustomerID), DaysPurchased = countDistinct(toDate(SaleDate)), RetentionFlag = iif(DaysPurchased >= 3, 'Repeat', 'One-time') 4️⃣ Hidden Facts 💡 You can aggregate on derived fields like toMonth(SaleDate) for monthly rollups. 💡 Multiple aggregates can run in one transformation → saves cost. 💡 Dropping unnecessary columns before aggregation improves performance. 💡 For rolling metrics (moving average, running totals) → use Window Transformation, not Aggregation. ✅ Key Takeaways Aggregation = Summarizing big data into small insights. Group By = How to divide data. Aggregates = What calculation to do inside each group. Use Aggregation for totals, averages, unique counts, top values. Drop unused columns before aggregation → faster pipelines. #AzureDataEngineering #100DaysOfAzureDataEngineering #AggregateTransformation #AzureDataFactory #ETL #Learning