Srinivasan Shanmugasundaram’s Post

🎯Comparison for snowflake and data bricks you can decide what you want to choose ✅ Snowflake is tailored for analytics, while Databricks excels in big data processing and ML. | | **Architecture** | ✅Snowflake is a cloud-native warehouse, while Databricks uses a Lakehouse built on Apache Spark. | | **Core Use Case** | ✅Snowflake is ideal for structured/semi-structured data, Databricks handles all data types. | | **Data Formats** | ✅Snowflake supports structured/semi-structured data; Databricks supports all data types, including unstructured. | | **Performance** | ✅Snowflake is optimized for BI queries; Databricks is built for large-scale, real-time processing.| | **Scalability** | ✅Snowflake scales compute and storage independently; Databricks relies on distributed computing. | | **Ease of Use** | ✅Snowflake offers simplicity for analysts; Databricks requires advanced technical skills. | | **ML Support** | ✅Snowflake integrates external ML tools; Databricks has native ML and AI capabilities. Follow Harikesh Tyagi🎯 for more content. #DataEngineering #BigData #ETL #DataPipeline #CloudComputing #DataWarehouse #DataLake #SQL #Python #ApacheSpark #AWS #Azure #GCP #DatabaseManagement #Analytics

Delta ecosystem in Azure Databricks: Delta refers to the technology introduced with Delta Lake, which serves as the underlying framework for storing data and tables in the Databricks lakehouse. 🔹 Delta Lake: Delta Lake is the foundation for storing data and tables in the Databricks lakehouse. It was designed to handle transactional real-time and batch big data. Delta Lake extends Parquet data files by adding a file-based transaction log for ACID transactions and scalable metadata handling. 🔹 Delta Tables: These tables are built on top of Delta Lake. They provide a convenient table abstraction, allowing you to work with large-scale structured data using SQL and the DataFrame API. Delta tables are commonly used for data lakes, especially when data is ingested via streaming or large batches. 🔹 Delta Live Tables: These manage data flow between multiple Delta tables, simplifying the work of data engineers in ETL development and management. The pipeline is the primary unit of execution for Delta Live Tables.  Delta ecosystem provides robust data management, efficient querying, and seamless data flow within the Databricks environment. Image from: Databricks #Dataengineering #Databricks #DeltaTable 

I am thrilled to announce that I have successfully completed the Azure Databricks certification, enhancing my expertise in Databricks architecture, cluster configuration, mounting DBFS, and utilizing filter and aggregate transformations with PySpark. This course has provided me with invaluable insights into optimizing data workflows, implementing efficient data transformations, and leveraging the power of Databricks for scalable data processing and analytics. I look forward to applying these skills to drive innovation and deliver impactful solutions in data-driven environments. #AzureDatabricks #PySpark #Sql #DataAnalytics #AzureDataEngineer #Azuredatafactory #AzureSynapse #hiringazuredataengineering

Recently, I worked on an ETL process using Azure Data Factory (ADF) to import data into Azure Databricks Delta Lake. While the process itself was straightforward, I noticed that the Azure Databricks documentation lacked some critical details. After some trial and error, I was able to identify and resolve the issue. I wanted to share the steps with you in case you encounter the same challenge. #AzureDatabricks #AzureDataFactory https://lnkd.in/gXeeeRN6

"Handling Schema Evolution with Delta Lake in Azure Databricks" 💡 Problem-Solving Tip: Managing schema changes over time can be complex, particularly when you are working with evolving data sources in your data lake. 📈 Challenge: In a data lake project, the schema of the ingested data was changing frequently due to updates in the upstream systems. These changes often broke the data pipelines or caused errors during analysis. 🔧 Solution: Delta Lake Schema Evolution: I used Delta Lake’s schema evolution features in Azure Databricks, which allowed me to accommodate schema changes automatically during the data ingestion process. Merge and Update: Leveraged MERGE INTO operations to efficiently apply updates and inserts into Delta tables, ensuring that evolving data was handled seamlessly. Handling Nulls and Defaults: I implemented strategies for managing null values and default values when columns were added or removed, ensuring backward compatibility. Schema Validation: Enabled schema enforcement to ensure that unexpected changes didn’t corrupt data or disrupt downstream workflows. 🚀 Result: This approach allowed for flexible and robust handling of schema evolution, reducing downtime and maintaining data integrity even as the source schema changed. 💬 Tip: Schema evolution is a challenge, but Delta Lake provides powerful tools to manage it efficiently. How do you handle changing schemas in your data pipelines? #Azure #Databricks #DeltaLake #DataEngineering #SchemaEvolution #ETL #BigData

Overcoming Challenges with Large Data Volumes in Azure Databricks 💡 Problem-Solving Tip: Managing large datasets is one of the most common challenges for data engineers, especially when optimizing performance in Azure Databricks. Recently, I encountered a situation where processing terabytes of data was causing significant delays in our ETL pipelines. 📈 Challenge: We were processing massive amounts of raw data in Azure Databricks, but performance bottlenecks due to data shuffling and memory issues led to long processing times and resource overuse. 🔧 Solution: I implemented several optimization techniques using Apache Spark within Databricks: Partitioning: By understanding the data distribution, I optimized the partitions to ensure efficient parallelism. Using the .repartition() function in Spark allowed me to reduce shuffling and better distribute the workload across nodes. Caching: I used the .cache() function strategically to store intermediate results in memory. This reduced the need to recompute expensive transformations, particularly for iterative processing. Broadcast Joins: To handle large data joins, I switched to broadcast joins for smaller tables. This significantly reduced data transfer between nodes and enhanced the overall performance of the join operation. File Format Optimization: Storing data in Parquet format instead of CSV not only reduced file sizes but also improved query performance due to Parquet’s columnar storage format. Cluster Configuration: I fine-tuned the Databricks cluster configuration, adjusting the memory and core settings to match the workload requirements, ensuring the cluster was optimized for the job size. 🚀 Result: These changes led to a significant decrease in the pipeline's execution time—from several hours to under 45 minutes! This improvement not only saved time but also reduced costs by using the resources more efficiently. 💬 Tip: Always analyze your data processing pipeline for bottlenecks and use Spark optimizations like partitioning, caching, and broadcast joins to handle large datasets effectively. 🔗 Feel free to share your own optimization techniques in the comments! #Azure #DataEngineering #Databricks #Spark #BigData #ETL #DataOptimization #ProblemSolving #PerformanceBoost #DataEngineerTips #TechTalk

🚀 Delta Lake Architecture: The Ultimate Solution for Scalable, Reliable Data Lakes! 🌟 Delta Lake, built on top of Apache Spark and Parquet, transforms your data lake into a highly reliable, scalable, and performant system with ACID transactions. Here’s a breakdown of its architecture: 1️⃣ Data Lake Storage: Delta Lake uses existing storage systems (AWS S3, Azure Data Lake, HDFS) for cost-effective, scalable data storage in Parquet format. 2️⃣ Delta Log: The transaction log (_delta_log) tracks every change (inserts, updates, deletes) in a Delta table, enabling ACID transactions for consistent, reliable operations even during failures. 3️⃣ Delta Table: A Delta Table stores your data in Parquet format, enriched with a Delta Log for metadata and schema, allowing for powerful features like time travel and versioned queries. 4️⃣ Write Operations: Append: Add new data to existing tables. Upsert (Merge): Merge or update data in the table efficiently. Delete and Update: Granular operations for data modification. 5️⃣ ACID Transactions: Delta Lake ensures Atomicity, Consistency, Isolation, and Durability (ACID) for all operations, making sure your data remains consistent and reliable even in complex, distributed environments. 6️⃣ Optimized Metadata Handling: Delta Lake optimizes metadata management, handling large datasets seamlessly without sacrificing performance. 7️⃣ Batch and Streaming Data: Supports both batch and streaming data in the same table, empowering real-time analytics alongside historical processing. Delta Lake is the game-changer for anyone dealing with large-scale data processing, providing reliable, scalable, and consistent data lakes. 🚀🔐 #Azure #Databricks #Microsoft #DeltaLake #DataLake #BigData #ACIDTransactions #ApacheSpark #DataEngineering

🚀 Azure Databricks Interview Preparation: Day 5 of 30! Today, I focused on understanding the different methods of Data Ingestion in Azure Databricks. A solid grasp of data ingestion techniques is crucial for building efficient data pipelines. 🔍 Day 5 Focus: Data Ingestion Techniques: Azure Blob Storage & Data Lake: Using dbutils.fs or mounting the storage directly for easy data access. Database Connectivity: Using JDBC connections to ingest data from SQL databases efficiently. External Data Sources: Pulling data from external APIs and streaming data from Kafka or Event Hubs. Using Auto Loader: Auto Loader: A tool for incrementally and efficiently processing new data files as they arrive in cloud storage. It’s perfect for handling streaming and batch data at scale. Benefits: Schema evolution, efficient file discovery, and scalability for large datasets. 🌟 Key Insight: Choosing the right ingestion method depends on your data source and use case. For instance, using Auto Loader can simplify processing continuously arriving data, while JDBC is ideal for database integrations. 📚 Next Steps: Tomorrow, I’ll dive into Delta Lake and explore how it ensures data reliability and efficiency with features like ACID transactions. Data ingestion is the backbone of any data engineering project. Let's keep pushing forward! 💡 #AzureDatabricks #DataEngineering #DataIngestion #BigData #AutoLoader #CloudComputing #DataPipelines #AzureStorage #30DayChallenge #TechInterviews #InterviewPreparation #DataScience #CloudData #DataJourney #TechLearning

Must-know Databricks concepts to help you get ready for a Data Engineering round!!! 👇 1. What is Databricks, and how does it extend Apache Spark’s capabilities? 2. Explain Delta Lake and its role in enabling a Lakehouse architecture. 3. How do you set up and optimize clusters in Databricks for cost and performance? 4. What are Databricks Workspaces, and how do they support collaboration? 5. Describe how to build data pipelines using Databricks Jobs and Workflows. 6. How does Databricks handle data storage and integrate with platforms like S3 and ADLS? 7. Explain the concept of notebook workflows in Databricks and their use cases. 8. How would you monitor and troubleshoot Spark jobs within Databricks? 9. Describe the benefits of using Databricks SQL for BI and analytics. 10. What are the key features of Databricks for managing ML models with MLflow? 11. Explain how Databricks integrates with orchestration tools like Apache Airflow. 12. What is Auto-Scaling in Databricks, and how does it work? 13. How would you implement data governance and security within Databricks? 14. What’s the difference between Databricks Runtime and open-source Spark? 15. Describe a use case where Databricks accelerates data engineering or ML workflows. Add more from your side!! #DataEngineering #BigData #Databricks #Spark #CloudComputing #Lakehouse #ETL #DataPipelines #DataScience #MachineLearning #Analytics #CloudData #DataOps #DistributedComputing #DataArchitecture Zach Sumit Mittal Snowflake Databricks Apache Spark

What makes Databricks the rockstar of Data Engineering solutions landscape? A Data Engineer building Enterprise grade solutions need to dabble with multiple challenges namely :  ☑️ Manage 3Vs of Big data (Volume, Variety, Velocity) ☑️ Manage unpredictable spike in processing workloads ☑️ Monitor and optimize compute and storage ☑️ Manage costs ... and many more. One of the essential concepts to be learnt in order to efficiently work with Databricks is the idea of clusters, their types and how to work with them That's precisely covered in the following video as a part of our Data Engineering on Databricks learning experience series. Check out the video here : https://lnkd.in/gEk-RpU3 Wish to learn Databricks in a structured manner with access to integrated labs and certification aligned questions library? Check out the complete skill path here : https://lnkd.in/gA9NS5T2