Sriram Sivaraman

🚀 Top 15 PySpark Transformations Every Data Engineer Should Know Working with big data in Databricks means you need to be efficient, scalable, and smart with your transformations. I’ve put together a simple, structured PDF guide covering the most essential PySpark transformations — from filtering and joins to window functions. Whether you’re cleaning raw data, preparing it for analytics, or building pipelines, these 15 transformations are the foundation every data engineer should master. 📂 Check out the attached file and let me know which transformation you use the most in your projects! #DataEngineering #PySpark #Transformation #Databricks #ETL #ApacheSpark

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Don’t get confused while learning PySpark. Understand these core concepts step by step to become truly proficient in PySpark. 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - PySpark Architecture - SparkContext and SparkSession - RDDs (Resilient Distributed Datasets) - DataFrames - Transformations and Actions - Lazy Evaluation 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: - Creating DataFrames - Reading Data from CSV, JSON, Parquet - DataFrame Operations - Filtering, Selecting, and Aggregating Data - Joins and Merging DataFrames - Working with Null Values 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗼𝗹𝘂𝗺𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Defining and Using UDFs (User Defined Functions) - Column Operations (Select, Rename, Drop) - Handling Complex Data Types (Array, Map) - Working with Dates and Timestamps 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Understanding Partitions - Repartitioning and Coalescing - Managing Shuffle Operations - Optimizing Partition Sizes for Performance 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮: - When to Cache or Persist - Memory vs Disk Caching - Checking Storage Levels 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗪𝗶𝘁𝗵 𝗦𝗤𝗟: - Spark SQL Introduction - Creating Temp Views - Running SQL Queries - Optimizing SQL Queries with Catalyst Optimizer - Working with Hive Tables in PySpark 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Data Cleaning and Preparation - Handling Missing Values - Data Normalization and Transformation - Working with Categorical Data 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Broadcasting Variables - Accumulators - PySpark Window Functions - PySpark with Machine Learning (MLlib) - Working with Streaming Data (Spark Streaming) 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Understanding Job, Stage, and Task - Tungsten Execution Engine - Memory Management and Garbage Collection - Tuning Parallelism - Using Spark UI for Performance Monitoring Once you master these PySpark fundamentals, you won’t just write code — you’ll design efficient, scalable, and production-ready data pipelines that harness the full potential of Spark.

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🚀 Snowflake Interview Questions for Senior Data Engineers – Part 2 In Part 1, we covered Architecture, Performance Optimization, and Data Modeling. Now, let’s dive into Security, Advanced Features, and Cost Optimization — areas where senior engineers are expected to think strategically. 🔹 4. Data Ingestion & Integration What are the different ways to load data into Snowflake (e.g., COPY, Snowpipe, external tables)? Can you explain how Snowpipe works and when it’s the right choice? How do external stages (S3, Azure Blob, GCS) integrate with Snowflake? What’s the role of file formats (Parquet, ORC, JSON, CSV) in Snowflake ingestion? How do you design a robust ELT/ETL pipeline into Snowflake? 🔹 5. Security & Governance How do Snowflake roles and grants work? What is the purpose of network policies in Snowflake? How do you enable row-level security (RLS) and column-level security (CLS)? Can you explain masking policies and their use cases? How do you implement data sharing securely across accounts or organizations? 🔹 6. Advanced Features How do streams and tasks enable near real-time pipelines in Snowflake? Can you explain the difference between replication and database failover in Snowflake? What is Time Travel in Snowflake, and how is it useful in recovery scenarios? How does zero-copy cloning work, and when would you use it? How would you use Snowflake’s external functions with AWS Lambda or Azure Functions? 🔹 7. Monitoring & Cost Management How do you monitor warehouse usage and query performance in Snowflake? What techniques do you use to optimize Snowflake cost without impacting performance? How do you analyze query history and warehouse utilization to detect inefficiencies? What are the best practices for managing multi-cluster warehouses? How do you track storage costs, including Time Travel and Fail-safe data? 👉 Thinking of making Part 3 all about real-world scenarios in Snowflake — like, “Your costs just doubled, how do you figure out why?” Should I go ahead and share those? Follow Avinash S. for more. #Snowflake #DataEngineering #CloudData #InterviewPrep #SeniorDataEngineer Would you like me to also create a Part 3 post that includes Scenario-Based Questions (e.g., “Your Snowflake costs doubled last month, how would you investigate?”) for a more practical angle?

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𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬, 𝐀𝐬𝐬𝐞𝐦𝐛𝐥𝐞! You: ✅ Read data from ADLS ✅ Write a clean UDF ✅ Save as Parquet PySpark: ❌ “Python worker exited unexpectedly” 😂 Been there? This meme sums up half our debugging life in PySpark. Whether it’s serialization issues, unregistered UDFs, or weird memory errors — the job runs fine locally, but dies dramatically on the cluster. 💡 Pro Tip: Before blaming the Spark gods, double-check: Your UDF inputs & outputs are serializable You aren’t passing DataFrame rows directly No rogue print statements inside UDFs Your worker nodes have matching Python versions Let’s laugh through the pain. Drop your most cryptic PySpark error in the comments 👇 Let’s see who suffered the most 🙃 #PySpark #DataEngineering #BigData #SparkHumor #MemeMonday #Debugging #Azure #DataLake #UDFFails

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🚀 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲? 𝗔 𝗕𝗼𝗹𝗱 𝗡𝗲𝘄 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝗶𝗻 𝗢𝗟𝗧𝗣 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗜 𝗘𝗿𝗮! Databricks just unveiled something revolutionary — 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲, a modern rethinking of OLTP databases for today’s AI-driven world. ⚡ Traditional operational databases were not built for modern workloads:  • 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆  • 𝗘𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲  • 𝗣𝗮𝗶𝗻𝗳𝘂𝗹 𝗘𝗧𝗟 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀  • 𝗡𝗼𝘁 𝗔𝗜-𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆 🌊 𝗘𝗻𝘁𝗲𝗿 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲 — a radically new architecture that combines: ✅ Open source foundations (built on Postgres) ✅ Separation of storage & compute for elastic scaling ✅ Serverless architecture — scales to zero ✅ Branching like Git for modern dev workflows ✅ AI-native design — built for thousands of AI agents ✅ Deep Lakehouse integration — no more fragile ETL This is not just a product — it’s a paradigm shift. 🔍 Imagine a world where databases scale instantly, branch like code, and are created by AI agents in milliseconds. That’s the 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲 𝗳𝘂𝘁𝘂𝗿𝗲. 💡 At Data Master Consulting, we’re thrilled by what this means for AI, data engineering, and app development. We’ll be sharing deep dives, tutorials, and real-world use cases very soon — so stay tuned! 📚 Learn more about Lakebase and why it’s redefining operational data: 👉 https://lnkd.in/daEtC5ab 🔥 This is the most exciting announcement in data infrastructure since the Lakehouse. Don’t just follow the trend — be ahead of it. #Databricks #Lakebase #Lakehouse #Postgres #AI #OLTP #Serverless #DataEngineering #ModernArchitecture #OpenSource #DatabaseInnovation #TheDataMaster #DataMasterConsulting #AIAgents #ElasticInfra #NavalYemul #TechLeadership #FutureOfData

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