Subayyal K.

🧠 Data Lake vs Data Warehouse vs Data Lakehouse vs Data Mesh The world of data architecture is evolving faster than ever and understanding the difference between these approaches is key to designing a scalable, future proof data strategy. Let’s break them down 👇 💧 Data Lake A central storage repository for raw, structured, and unstructured data , think of it as your data playground. ✅ Pros: Highly scalable, cost efficient, perfect for experimentation. ⚠️ Cons: Can turn into a “data swamp” without governance or proper indexing. 🏢 Data Warehouse A structured system built for reporting, analytics, and BI. ✅ Pros: Reliable performance, strong governance, ideal for complex queries. ⚠️ Cons: Rigid schema, expensive scaling, not great for unstructured data. 🧩 Data Lakehouse The best of both worlds — combining the flexibility of data lakes with the performance of warehouses. ✅ Pros: Unified architecture, supports both analytics and ML, simplified pipeline. ⚠️ Cons: Still maturing — tools and best practices are evolving. 🌐 Data Mesh A decentralized data architecture where each domain owns its data as a product. ✅ Pros: Promotes autonomy, scalability, and domain-driven design. ⚠️ Cons: Complex implementation, requires cultural and organizational change. ⚙️ So, which one should you choose? Use a Data Lake → if you need scalable raw data storage. Use a Data Warehouse → for governed, structured analytics. Use a Data Lakehouse → when you need unified data & ML capabilities. Use a Data Mesh → for large, domain-driven organizations scaling globally. 💡 Each model solves different challenges. The real power lies in knowing when and how to combine them. Image Credits : Aditya Sharma ✍ I share insights on Data Analytics, Data Engineering, SQL, Cloud, Careers, and Job Strategies , all drawn from real, hands-on experience. It’s practical. It’s valuable. And it’s free ❤️ 🤝 🤝If you find my posts useful, please Subscribe for Free Articles : https://lnkd.in/ebGPbru9? #dataengineering

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Data Spill in Apache Spark – Hidden Performance Killer 🔍 Understanding data spills is one of the most overlooked but critical aspects of Spark performance tuning, when Spark runs out of memory during shuffles or aggregations and spills data to disk. 👉 Why it happens: Insufficient memory for shuffle operations Improperly sized partitions Large skewed joins or wide transformations Incorrect configuration of sort/aggregate operators 👉 How to detect: Monitor Spilled Records, Shuffle Spill (memory/disk) in Spark UI Watch for excessive disk I/O and GC pressure Use event logs for historical spill analysis 👉 How to tune: Optimize partition sizing using repartition() or coalesce() Tune shuffle parameters like spark.shuffle.spill.compress, spark.reducer.maxSizeInFlight Use broadcast joins smartly Allocate memory wisely across execution/storage (spark.memory.fraction) Tuning data spill isn’t just about reducing disk writes — it’s about unlocking true performance and cost-efficiency at scale. If you want to explore this topic further, here’s a free preview of the course: 👉 Explore free preview: https://lnkd.in/g2yH_xPF Follow Prashant Kumar Pandey for more. #ScholarNest #ApacheSpark #DataEngineering #BigData

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🔹 Why Your Lookup Activity in ADF Might Be Slowing Down Your Pipelines and How to Fix It The Lookup activity is a favorite for fetching config or control data. But… if you don’t configure it right, it can silently impact your pipeline performance. Here’s what happens: By default, Lookup fetches up to 5,000 rows and loads all into memory. If your source returns more than one row, and you use it thinking it’s a single record unexpected behavior. Lookup is meant for control values, not large data reads. Best Practices: ~ Always add a TOP 1 / LIMIT 1 in your query when you need only a single value. ~ If you need to iterate over multiple rows, consider Copy Activity to staging + ForEach loop instead. ~ Monitor your Lookup output in debug to avoid surprises. 🔹 Bonus Tip: For Lookup over config tables or parameter sets cache them in variables instead of repeated Lookups in the same pipeline. Because sometimes… it’s not the heavy activities slowing you down. It’s the tiny Lookup running silently in every pipeline run. #AzureDataFactory #ADFPerformance #DataEngineeringTips #LookupActivity #ADFOptimization

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𝐑𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 . . . In today’s data-driven landscape, volume alone doesn’t guarantee value — data validation is essential to ensure trust, accuracy, and usability. PySpark, leveraging the power of distributed computing, not only facilitates large-scale data transformation but also enforces data quality at every step. From verifying schema consistency and detecting nulls to applying complex business validation rules, PySpark equips data engineers with scalable tools to ensure clean, reliable datasets before they flow into downstream systems for analytics or consumption. #PySpark #DataValidation #BigData #DataQuality #DataEngineering #CleanData #SparkProcessing #ReliableData #ETLPipeline #DistributedComputing #TechLeadership

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𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗔𝗽𝗮𝗰𝗵𝗲 #𝗦𝗽𝗮𝗿𝗸 𝗼𝗻 #𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀: 𝗦𝗼𝗺𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀  𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗦𝗽𝗮𝗿𝗸 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲  • Monitor resource utilization, data flow patterns, and bottlenecks using the Spark UI  • Understand the impact of data size, formats, and distribution on performance  𝟮. 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀  • Choose high-cardinality columns for even distribution  • Target 100-200MB per partition as a general guideline  • Use repartition() and coalesce() to control partition count  𝟯. 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀  • Filter early to reduce data volume  • Use broadcast joins for small tables  • Maintain consistent partitioning where possible  • Monitor shuffle spill metrics (memory vs disk)  𝟰. 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴  • Cache DataFrames that are reused in multiple operations  • Use cache() or persist() explicitly  • Remember to unpersist() when data is no longer needed  𝟱. 𝗤𝘂𝗲𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻  • Understand Catalyst optimizer and execution plans with explain()  • Leverage predicate pushdown and column pruning  • Use the appropriate join strategy for your data  𝟲. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲  • Delta Lake's Predictive Optimization automatically leverages your OPTIMIZE and Z-ORDER operations  • When you run queries, the query optimizer automatically considers your Z-ORDER indexes  • Benefits:   - No need to explicitly hint which indexes to use in your queries   - The system automatically prunes files based on Z-ORDER columns   - Queries are automatically optimized for better performance   - Reduces I/O by skipping files that don't contain relevant data  • Example: If you've run OPTIMIZE table ZORDER BY (date_col, region):   - A query with WHERE date_col = '2023-01-01' AND region = 'APAC' automatically benefits   - The optimizer uses Z-ORDER statistics to read only relevant files   - This happens transparently without any special query modifications  𝟳. 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀  • Use the Spark UI to monitor performance  • Filter early and select only needed columns  • Optimize join operations and join order  • Minimize UDFs in favor of built-in functions Discover more: https://lnkd.in/gRxQhmSf #data #dataengineering #distributedprocessing

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💡#Building Efficient ETL Pipelines in Snowflake – My Approach #2025 ------------------------------------------------------------------------------------ . . . . . . . As Data Engineers, one of the biggest challenges we face is designing scalable ETL pipelines that can handle growth without breaking performance. Recently, I worked on a Snowflake ETL project where the key focus was speed + cost optimization. Here’s how I approached it: 🔹 Data Ingestion – Used Snowpipe for near real-time ingestion and COPY INTO for batch jobs depending on data source. 🔹 Staging Layer – Created a staging schema to keep raw ingested data separate before transformation. 🔹 Transformations – Leveraged dbt + Snowflake SQL to apply business logic in modular, reusable models. 🔹 Performance Optimization – Partitioned/clustered large tables for faster queries Used materialized views for repeated transformations Scheduled transformations via Tasks + Streams 🔹 Orchestration – Integrated Airflow to manage job dependencies and retries. 🔹 Monitoring – Set up query performance monitoring & alerts with Snowflake Resource Monitors. ✨ Takeaway: A good ETL pipeline in Snowflake is not just about loading and transforming data – it’s about balancing performance, cost, and simplicity. 👉 Question for you: What’s the #1 best practice you always follow when designing ETL pipelines in Snowflake? #Snowflake #ETL #DataEngineering #CloudData #BigData #dbt #Airflow

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