Ritik Jain

🚀 4 Hours ➝ 25 Minutes — Real Spark Optimization Story (Databricks + Delta Lake) I was working on a Spark ETL job processing ~180M rows daily on Databricks using PySpark and Delta Lake. What was supposed to be a 1-hour batch job… ❌ Took over 4 hours ❌ Often failed with memory pressure errors ❌ Consumed excess compute and storage So, I dug in — and here’s exactly how I fixed it 👇 ⸻ 🔍 Root Causes Identified ❌ Data Skew – 80% records were in one key → skewed partitions ❌ Shuffle Overhead – huge data movement due to default join ❌ Tiny Files – 1000s of small Parquet files increasing I/O ops ⸻ ✅ Optimizations That Worked 1. Handled Data Skew with Salting + Broadcast • Added a random salt key (0–9) to the skewed column • Applied the same salting logic to the smaller dimension table • Used broadcast() for smaller side of join 👉 Reduced shuffle by 80% 2. Compacted Files Using Delta OPTIMIZE • Ran OPTIMIZE on the Delta table to merge tiny files • Added ZORDER BY user_id to accelerate access 👉 Query speed improved noticeably 3. Smart Repartitioning • Repartitioned using df.repartition("user_id") • Removed unnecessary .cache() calls 👉 Better parallelism, lower memory usage 4. Spark Config Tuning • spark.sql.shuffle.partitions → reduced to 64 • Enabled spark.sql.adaptive.enabled = true • Switched to Photon Runtime for performance gains 👉 Runtime improved, retries stopped ⸻ 📊 Final Results ⏱️ Runtime: 4 hours ➝ 25 minutes 💰 Saved >50% on compute cost ⚙️ No more job failures or timeouts 🔁 Job now runs twice a day reliably ⸻ 💡 Lesson Learned: Performance tuning in Spark is a data-aware skill — not guesswork. → Read your Spark UI → Tune based on partitioning, file sizes, and execution plans → Don’t optimize blindly — observe, test, iterate #Databricks #ApacheSpark #DeltaLake #BigData #DataEngineering #PySpark #ETL #Optimization #TechTips #SparkJobs #DataSkew #Photon #CareerGrowth #DataOptimization #AzureDataEngineer

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Trust me, Pyspark is not HARD. If you follow these 6 steps : 1. Understand the Basics of Spark - Learn about Apache Spark's architecture, including its components like Driver, Executor, and Cluster Manager. - Understand RDDs (Resilient Distributed Datasets), DataFrames, and Datasets. 2. Get Comfortable with PySpark DataFrames - Learn how to create DataFrames from different sources (CSV, JSON, Parquet, databases). - Perform essential operations like filtering, grouping, aggregations, and joins. 3. Master Transformations and Actions - Understand narrow vs. wide transformations (`map`, `filter`, `flatMap`, `groupByKey`, `reduceByKey`). - Get hands-on with actions like `collect()`, `count()`, `show()`, `first()`, and `take()`. 4. Work with PySpark SQL - Learn how to register DataFrames as temporary views and run SQL queries. - Master window functions (`rank`, `dense_rank`, `row_number`), aggregations, and analytical queries. 5. Optimize Performance - Learn about caching (`cache()`, `persist()`). - Understand partitioning, bucketing, and file formats like Parquet for optimization. - Tune Spark configurations (`spark.sql.shuffle.partitions`, `executor.memory`). 6. Practice with Real-world Use Cases - Solve end-to-end data engineering problems. - Work with large datasets to understand performance challenges. - Build ETL pipelines using PySpark, integrate with cloud platforms (AWS S3, Azure Blob Storage).

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A friend of mine interviewed at JPMorganChase for data engineer role (4 -5 )yrs exp3 below are combined few questions from both tech rounds. 1)How do you implement incremental data loads in ADF? 2)How do you handle failure and retries in ADF pipelines? 3)How do you parameterize pipelines in ADF? 4)How do you monitor and debug pipelines in ADF? 5)How do you orchestrate pipelines across multiple environments? 6)Have you used Azure Key Vault in ADF for securing credentials? 7)How do you optimize SQL queries for large datasets? 8)What is the difference between UNION and UNION ALL? 9)How do you handle NULL values in SQL joins and aggregations? 10)What is indexing, and how does it affect query performance? 11)How do you implement slowly changing dimensions (SCD) using SQL? 12How do you handle large data files in PySpark? 13)Explain the difference between repartition() and coalesce() in PySpark. 14)How do you perform window functions in PySpark? 15)What is lazy evaluation in Spark, and why is it important? 16)How do you optimize PySpark jobs for performance? 17)What is a broadcast join in PySpark, and when should it be used? 18)You have a daily batch pipeline that sometimes fails randomly. How would you debug and fix it? 19)How would you design a data pipeline that ingests files from ADLS Gen2 and loads them into a Delta Lake? 20)Describe how you would implement schema evolution in a PySpark-based pipeline. 21)How do you handle late-arriving data in your pipeline? 22) there was 2 codes that was on sql advance concepts like writing scd 2, a code on lead ,lag , row number use. #AzureDataEngineer #DataEngineerInterview#AzureDataFactory #PySpark#AzureDataEngineer#DataEngineerInterview#AzureDataFactory#PySpark #SQL#DataEngineering

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🔥 PySpark Mock Interview Guide – 50 Most Asked Q&A for 2025 Want to crack interviews at Databricks, Amazon, Verizon, TCS, Deloitte, or Infosys? This topic-wise PySpark Q&A PDF is a game-changer. Curated from real technical rounds, it covers core, advanced, and scenario-based PySpark questions. 🧠 What's Inside: ✅ PySpark Basics → What is SparkSession? How is RDD different from DataFrame? → Lazy Evaluation & Fault Tolerance simplified ✅ DataFrame Operations → withColumn(), selectExpr(), groupBy(), joins, null handling → dropna vs fillna, cast(), distinct(), schema inference ✅ Performance Optimization → repartition vs coalesce, caching vs persistence → Catalyst Optimizer, Tungsten Engine, Broadcast joins ✅ Debugging & Logging → Spark UI, .explain(), log4j, memory errors, checkpointing ✅ DSA & Real-World Scenarios → explode(), rank(), dense_rank(), window functions → partitioning strategies, UDFs, accumulators ✅ Deployment Knowledge → Working with large datasets, Parquet vs CSV, collect() vs show() → Unit testing in PySpark, best practices, memory management 💼 Best for roles like: 🔹 PySpark Developer 🔹 Data Engineer 🔹 Big Data Specialist 🔹 ETL Developer 🔹 Azure/Spark/Cloud Engineers 🏢 Top Companies Hiring: 📍 Databricks | Verizon | Microsoft | Amazon | PayPal | Snowflake | Tata Consultancy Services | Wipro | Accenture | Flipkart 💬 Comment "PYSPARK50" to get the free PDF 📌 Save this for your next tech round 👉 Follow Sachin Patil for weekly mock prep, roadmaps, and project dumps 🔍 #PySparkInterview #ApacheSpark #BigDataEngineer #DataEngineeringLife #RDDvsDataFrame #SQLForDataEngineers #SparkOptimization #ResumeToOffer #DataPipeline #FollowSachinPatil #MockInterviewPrep #KafkaStreaming #AzureDataEngineer #ETLDeveloper #DataScienceIndia #TechJobs2025 #CatalystOptimizer #UnitTestingSpark #SparkSQL #LinkedInLearning #PythonSpark

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Snowflake Series – Part 21: Automating Daily S3 → Snowflake Loads! 📅❄️ Tired of manually updating S3 paths for daily data loads? In this post, I’ll show you how to automate the process of loading data from day-partitioned folders (YYYY/MM/DD) in S3 directly into Snowflake—using a dynamic stored procedure. Here’s what you’ll learn: ✅ Navigating and reading date-partitioned folders in S3 ✅ Writing a Snowflake stored procedure for dynamic path resolution ✅ Automating daily COPY INTO operations ✅ Best practices for near–real-time and streaming-style ingestion ✅ Real-world use cases & interview-ready implementation 💡 Perfect for data engineers building automated pipelines, prepping for interviews, or handling real-time ingestion scenarios. 💻 Full code and Customerdata file to practice available on my GitHub: https://lnkd.in/dx5KtJAQ Let’s connect & share our data engineering journeys! Thank you Vishal Kaushal for the Support and guidance. #Snowflake #IncrementalLoad #DataEngineering #ETL #Automation #CloudData #Learning #SQL #BigData

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1. What all views are there in Databricks? Explain Materialized view. 2. How will you develop Generic pipelines in ADF? 3. What do you understand by Delta Live Table in Databricks? When to use. 4. Design an end-to-end data pipeline to ingest, process, and serve daily incremental data from multiple sources with CDC, ensuring scalability and fault tolerance. 5. How would you design a star schema vs a snowflake schema for a retail analytics data warehouse? Which one do you recommend? 6. Compare different file formats (Parquet, ORC, Avro, Delta) for data lake storage. How would you choose one for a use case involving frequent small updates? 7. What are potential pitfalls with ACID transactions in data lakes (e.g., Delta Lake) at scale, and how do you handle them? 8. You discover late-arriving data needs to be corrected retroactively for the past week. How would you handle backfilling while minimizing downtime or data corruption? 9. A key dependency in your pipeline (e.g., source API) fails for a day. How do you catch up data once the service resumes, ensuring order and completeness? 10. How do you integrate Azure Synapse Analytics with other Azure services?

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Databricks managed table comes with two interesting features — which can boost up the read optimization from delta table: ✅ 1. Metadata caching Normally, when you query a Delta table, Databricks has to read the _delta_log files to understand the current state of the table — like which files are active, what schema is being used, and so on. This can add overhead, especially for large tables with many transactions. With managed tables, Databricks automatically caches this metadata in memory. So instead of reading the log files every time, it can quickly access the metadata from memory. This makes read operations faster and more efficient, especially for frequent queries. In short: You save time because Databricks doesn’t need to scan the log files again and again — it already knows what’s in the table. ✅ 2. Auto liquid clustering Clustering helps organize data in a way that makes queries faster — especially when filtering on certain columns. In Delta Lake, liquid clustering is a newer feature that allows flexible and automatic clustering without needing to define strict partitioning rules. With managed tables, liquid clustering is enabled by default. You don’t need to manually run OPTIMIZE commands or set up clustering logic. Databricks takes care of it automatically in the background. In short: Your data layout gets optimized automatically, which improves query performance — and you don’t have to do anything extra.

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