Data migration disaster: $1M lost due to undocumented script.

KEY BEST PRACTICES WHEN MIGRATING FROM SQL SERVER INTO SNOWFLAKE #sqlserver #bestpractices #snowflakesql #Migratingdata #fyp Streamlining SQL Server to Snowflake data pipelines requires careful planning, execution, and optimization. Here are some best practices to help you achieve a smooth and efficient transition: *Planning and Assessment* - *Assess Current Environment*: Evaluate your current SQL Server setup, identifying critical data assets, dependencies, and potential challenges. - *Define Goals and Objectives*: Clearly define your migration goals, success metrics, and assign roles to team members ¹. *Data Migration* - *Prioritize Data Assets*: Migrate critical data assets first, such as data consumption points (BI tools and dashboards), to minimize disruptions. - *Lift-and-Shift Approach*: Use a lift-and-shift approach to migrate data with minimal changes, reducing complexity and downtime. - *Validate Data Consistency*: Verify data consistency between SQL Server and Snowflake using data validation tools and methodologies ² ³. *Performance Optimization* - *Right-Sizing Warehouses*: Use Snowflake's auto-suspend and auto-resume features to control costs and optimize performance. - *Monitoring and Alerting*: Implement monitoring and alerting tools to track pipeline health, data freshness, and performance issues ⁴. *Security and Governance* - *Data Security*: Ensure data encryption during transit and at rest in Snowflake, and implement role-based access control and multi-factor authentication. - *Data Governance*: Establish strong data governance practices, including data quality, compliance, and security policies ⁵. *Tools and Technologies* - *Migration Tools*: Use automated migration tools like Snowflake SnowConvert, CData Sync, or Datafold's DMA to streamline the migration process. - *Data Integration Platforms*: Leverage data integration platforms like Hevo or Estuary Flow to simplify data pipelines and ensure real-time data synchronization ¹ ⁴ ⁶. *Ongoing Optimization* - *Cost Management*: Monitor and optimize Snowflake costs using tools like Snowflake Account Usage views and resource monitors. - *Performance Tuning*: Continuously monitor and optimize query performance, warehouse sizing, and data loading strategies ⁴.

🚀 Recovering Historical Data During DWH Migration In one of my past projects, we faced a critical challenge while migrating from SQL Server DWH → Snowflake. Historical data in Salesforce had been deleted or updated, making it impossible to extract a complete dataset. But the business needed full historical completeness to trust the new platform. We solved it by: ✅ Ingesting old SQL Server data into Snowflake via Fivetran ✅ Reconciling surrogate keys → generating MD5 hash keys from natural keys ✅ Rebuilding fact-to-dimension relationships with regenerated keys ✅ Validating counts, aggregates, and referential integrity The outcome? A repeatable framework for handling historical migrations, ensuring both accuracy and trust in the new Snowflake DWH. 🔗 Read the full article here - https://lnkd.in/dXtDNxux

Day 191 - 🔹 𝗦𝘁𝗲𝗽 𝟭: 𝗔𝘀𝘀𝗲𝘀𝘀 𝗬𝗼𝘂𝗿 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 (𝘍𝘳𝘰𝘮 𝘵𝘩𝘦 𝘴𝘦𝘳𝘪𝘦𝘴: 𝘚𝘵𝘦𝘱-𝘣𝘺-𝘚𝘵𝘦𝘱 𝘎𝘶𝘪𝘥𝘦 — 𝘔𝘪𝘨𝘳𝘢𝘵𝘪𝘯𝘨 𝘚𝘘𝘓 𝘚𝘦𝘳𝘷𝘦𝘳 𝘋𝘢𝘵𝘢 𝘞𝘢𝘳𝘦𝘩𝘰𝘶𝘴𝘦𝘴 𝘵𝘰 𝘍𝘢𝘣𝘳𝘪𝘤 𝘋𝘢𝘵𝘢 𝘞𝘢𝘳𝘦𝘩𝘰𝘶𝘴𝘦) When it comes to migrating a SQL Server Data Warehouse into Fabric, the temptation is to jump straight into pipelines and data movement. But here’s the truth: 𝗶𝗳 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗸𝗻𝗼𝘄 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 𝘁𝗼𝗱𝗮𝘆, 𝘆𝗼𝘂𝗿 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗹𝗶𝗸𝗲𝗹𝘆 𝗯𝗲 𝗺𝗼𝗿𝗲 𝗽𝗮𝗶𝗻𝗳𝘂𝗹 𝘁𝗵𝗮𝗻 𝗶𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲. Think of it like moving houses. Would you pack up everything blindly, without checking what’s useful, what’s outdated, and what’s broken? Probably not. Data migration works the same way. 𝗪𝗵𝘆 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 ✅ 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 — You can’t move what you don’t know exists. Shadow tables, undocumented ETL jobs, or unused reports can create hidden risks. ✅ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — Not every table, stored procedure, or index deserves to be migrated. Some may no longer be relevant to the business. ✅ 𝗘𝗳𝗳𝗼𝗿𝘁 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻 — By cataloging dependencies and complexities up front, you can set realistic timelines and avoid unpleasant surprises mid-project. 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗮𝘀𝘀𝗲𝘀𝘀 𝗶𝗻 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 1️⃣ 𝗗𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀  • Which operational systems feed into your warehouse?  • Are you pulling from ERP, CRM, flat files, or APIs? 2️⃣ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀  • Inventory all schemas, tables, views, indexes, partitions, and stored procedures.  • Pay attention to row counts and growth rates—this will impact Fabric capacity planning. 3️⃣ 𝗘𝗧𝗟 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀  • Document how data is loaded today. Are you using SSIS, T-SQL scripts, or scheduled jobs?  • Identify transformations that may need to be rebuilt in Fabric pipelines or Dataflows. 4️⃣ 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀  • What reports, dashboards, or applications rely on your warehouse?  • Map dependencies to ensure nothing breaks when you cut over. 5️⃣ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗽𝗮𝗶𝗻 𝗽𝗼𝗶𝗻𝘁𝘀  • Where do users complain about slowness?  • These are opportunities to leverage Fabric’s elastic scaling and modern optimization. 6️⃣ 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 & 𝗰𝗼𝘀𝘁𝘀  • How much space are you consuming today?  • Understanding this helps project future costs and optimize partitioning in Fabric. 𝗣𝗿𝗼 𝘁𝗶𝗽𝘀 𝗳𝗼𝗿 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 🔍 Use SQL Server’s system tables and DMVs to quickly generate object inventories.  📝 Document everything—not just for migration, but as a long-term knowledge asset.  🎯 Separate “business critical” vs “nice to have” objects. Migration is a chance to declutter. #MicrosoftFabric #sqlserver #businessintelligence #datawarehousing #dataanalytics

Your organization generates terabytes of transactional data daily. Do your executives still make critical decisions based on outdated spreadsheets and gut instinct? Transforming raw Oracle data into actionable business intelligence requires more than good intentions. It demands strategic data warehouse design and optimization. Our latest white paper explores proven approaches that turn data complexity into competitive advantage, including: ✅ Strategic Schema Design — Choose between star and snowflake architectures for optimal query performance ✅ Efficient ETL Workflows — Streamline data extraction, transformation, and loading processes ✅ Advanced Query Optimization — Leverage partitioning, indexing strategies, and materialized views ✅ Performance at Scale — Handle massive datasets while maintaining lightning-fast analytics ✅ Decision-Ready Insights — Transform historical data into real-time business intelligence 👉 Read the full white paper at the link in the comments to master the concepts underlying Oracle data warehousing. Performance One Data Solutions helps organizations unlock the full potential of their Oracle data environments. Contact us to turn your data warehouse into a strategic business asset. #DataWarehousing #BusinessIntelligence #OracleDatabase #ITLeadership #DataAnalytics #EnterpriseIT #DatabaseSolutions #DBAManagement #DataManagement

Transform your SQL Server‑to‑Snowflake pipelines for cost‑efficiency, reliability, and speed. ⚡️ In this CData Software blog post, learn how connector‑based ETL, change data capture, schema mapping, optimized loads, and proactive monitoring can power real‑time analytics—without the DevOps overhead. #CData #DataIntegration #Snowflake #SQLServer #ETL #RealTimeAnalytics #DataEngineering

If you've ever replicated a database only to wonder: "Did ALL my data actually make it!?" Then this tutorial is for you! 👍 Here's how to validate Postgres → Snowflake replication using Airbyte + data-diff: 1️⃣ Set up replication: Configure Airbyte with "Full Refresh | Overwrite" to catch deletes/updates 2️⃣ Install data-diff: pip install data-diff[postgresql] data-diff[snowflake] 3️⃣ Run validation: Compare tables using checksums - finds discrepancies in seconds, not hours 4️⃣ Investigate differences: data-diff shows exact row PKs that differ with +/- indicators Pro tip: Automate this with Prefect to run after every sync + get Slack alerts on mismatches. Full tutorial 👉 https://bit.ly/3I6wms4

🚀 Performance Tuning in Snowflake: A Comprehensive Guide If you’re working with Snowflake, you know that raw data is just the start. Turning it into lightning-fast, cost-effective insights? That’s where the real magic happens. This deep-dive by Aegis Softtech unpacks: ✅ Snowflake’s architecture & how it impacts performance ✅ Proven strategies across data loading, transformation & querying ✅ Best practices like query pruning, caching, warehouse tuning & more ✅ Tips to reduce compute cost while maximizing speed Don’t let performance bottlenecks slow your data pipelines. Learn how to tune Snowflake for both efficiency and excellence. 🔗 Read the full blog here: https://shorturl.at/1XaWz #Snowflake #DataEngineering #PerformanceTuning #CloudData #Analytics #DataWarehouse #Optimization

💡 How do you implement Slowly Changing Dimensions (SCDs) in Snowflake? In data warehousing, SCDs are key for handling changes in dimension data: ✅𝐒𝐂𝐃 𝐓𝐲𝐩𝐞-𝟏 → Overwrites old data with new values. No history kept. ✅𝐒𝐂𝐃 𝐓𝐲𝐩𝐞-𝟐 → Preserves history by closing old records and inserting new ones. ✅𝐒𝐂𝐃 𝐓𝐲𝐩𝐞-𝟑 → Tracks limited history by storing previous values alongside current ones. ✨ Many teams rely on Streams and Tasks to build SCD Type-2, but it can also be implemented without them, using just SQL-based logic. We have put together a detailed blog covering the implementation of SCD Type-1, Type-2, and Type-3 in Snowflake with practical SQL examples. Check it out here: https://lnkd.in/gHFjHgE8 #ThinkETL #Snowflake #SCD #SQL #DataEngineering #SlowlyChangingDimensions #CloudData #DataWarehouse #ETL #DataIntegration #DataPipeline #SQL #DataAnalytics

❄️ Snowflake ❄️ The image depict two data landscape architectures: the Legacy Data Landscape and the Modern Data Landscape with Snowflake. Here's a comparison and the advantages of using Snowflake: Comparison: 1. Data Sources: 📌 Both architectures source data from OLTP databases, enterprise applications, third-party systems, web apps, and other sources. 📌 No significant difference in data input. 2. Data Processing: 📌Legacy: Uses ETL (Extract, Transform, Load) processes to move data into an Enterprise Data Warehouse (EDW) and subsequently into datamarts for BI/Analytics. 📌Snowflake: Employs ETL or ELT (Extract, Load, Transform) processes, integrating a Data Lake and leveraging Snowflake's EDW, which supports logical datamarts. 3. Data Storage: 📌Legacy: Relies on a traditional EDW and separate datamarts, with an optional Data Lake or Hadoop. 📌Snowflake: Combines a Data Lake with an integrated EDW (Snowflake), eliminating the need for separate datamart infrastructure by using logical datamarts. 4. BI/Analytics: 📌Legacy: BI/Analytics is performed on datamarts with a more fragmented approach. 📌Snowflake: Offers a unified platform for BI/Analytics, with direct access to logical datamarts and enhanced integration. Advantages of Using Snowflake: 1. Unified Data Platform: Snowflake integrates Data Lake and EDW functionalities into a single system, reducing complexity and the need for separate datamart infrastructure. 2. Flexibility with ELT: Supports ELT, allowing raw data to be loaded and transformed later, which can accelerate data availability compared to traditional ETL. 3. Scalability and Performance: Snowflake's cloud-native architecture provides elastic scaling, improving performance for large-scale analytics without additional hardware. 4. Cost Efficiency: By using logical datamarts instead of physical ones, Snowflake reduces storage and maintenance costs. 5. Simplified Management: Centralizes data management, eliminating the need to manage multiple systems (e.g., Hadoop or separate EDW and datamarts). 6. Enhanced Analytics: Offers a more integrated and efficient environment for BI/Analytics, leveraging its architecture for faster insights. Snowflake's modern approach streamlines data workflows, enhances scalability, and reduces operational overhead compared to the legacy architecture. #DataArchitecture #Snowflake #ModernDataLandscape #DataManagement #BusinessIntelligence #Analytics #DataLake #ETL #ELT #CloudComputing #DataEngineering #TechTrends #DataTransformation #BigData #BusinessAnalysis #Cloud

KEY FACTORS IMPACTING SNOWFLAKE PERFORMANCE AND SOME POSSIBLE RESOLUTIONS #snowflake #performance # summits2025 ## Query Complexity & Design - *Analyze query performance*: Use Snowflake's `QUERY_HISTORY` view to identify long-running queries. Look at metrics like `EXECUTION_TIME`, `BYTES_SCANNED`, and `PARTITIONS_SCANNED`. - *Optimize queries*: Simplify complex logic, use efficient joins (like pruning unnecessary joins), and minimize data scanned by using selective filters and projections. Example: Optimize a query that scans a large table by adding a selective `WHERE` clause to reduce data scanned. ## Data Structure & Clustering - *Monitor clustering depth*: Check clustering information using `SYSTEM$CLUSTERING_INFORMATION`. Adjust clustering keys for tables with skewed data distribution or high clustering depth. - *Use automatic clustering*: Snowflake's automatic clustering can help maintain optimal clustering for frequently queried data. Example: Re-cluster a table with a high clustering depth on a specific column to improve query performance. ## Concurrency & Workload Patterns - *Monitor concurrency limits*: Check warehouse metrics to see if queries are queuing due to concurrency limits. Adjust warehouse size or use multiple warehouses for different workloads. - *Manage workloads*: Use separate warehouses for different types of workloads (e.g., reporting vs. ad-hoc queries) to manage concurrency better. Example: Use a larger warehouse or multiple warehouses to handle peak concurrency demands during business hours. ## Caching & Result Reuse - *Leverage automatic caching*: Snowflake caches results for repeated queries on unchanged data. Structure queries to maximize cache hits for repeated operations. - *Consider query patterns*: Design queries to reuse cached results when possible. Example: Run reports with identical queries to leverage cached results and reduce computation time. ## Warehouse Size & Scaling - *Monitor performance*: Check warehouse performance metrics to determine if resizing is needed based on workload demands. - *Use scaling policies*: Configure scaling policies to automatically adjust warehouse size based on workload. Example: Configure a warehouse to scale up during peak hours and scale down during off-hours to optimize costs and performance.