Sathish Babu S.G’s Post

Interesting read on Databricks Fundamentals. Delta Lake and Photon are two pivotal technologies within the Databricks ecosystem, revolutionizing the way organizations manage and analyze their data. 1. Delta Lake - Open-source storage layer that brings ACID Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and big data workloads. It provides reliability, performance, and data management capabilities for big data analytics. Other Key Features in Delta Lake - Schema enforcement prevents users from accidentally polluting their tables with mistakes or garbage data. - Schema Evolution - Delta Lake supports schema evolution, allowing schemas to evolve over time while maintaining backward compatibility. This enables flexibility in data ingestion and schema management, reducing development overhead and improving productivity. 2. Photon: Photon is a distributed query engine optimized for Databricks Delta Lake. It accelerates query performance and improves resource utilization for analytics workloads by leveraging advanced query optimization techniques and in-memory processing.

Databricks is a unified analytics platform that provides a collaborative environment to work with big data and machine learning. It offers a cloud-based platform that integrates with Apache Spark and provides tools for data ingestion, exploration, visualization, and machine learning model development and deployment. Delta Lake is a storage layer that brings ACID transactions to Apache Spark and big data workloads, ensuring data reliability, scalability, and performance. It enables features like schema enforcement, data versioning, and time travel queries. The Unity Catalog in Databricks provides a unified metadata service that enables seamless metadata management across different Databricks services and data formats, improving data discovery and governance.

🚀 Why Delta Lake in Databricks? 🚀 Delta Lake is integral to Databricks because it enhances the capabilities of Apache Spark by bringing relational database features to big data processing. Delta Lake adds ACID transactions, data versioning, and schema enforcement to the flexible and scalable Spark environment, making it a game-changer for managing data at scale. Key reasons for using Delta Lake in Databricks: Optimized Spark Workloads: Delta Lake improves Spark's processing by ensuring consistency, reliability, and fault tolerance through ACID transactions. This is critical when running large-scale distributed data pipelines. Seamless Batch & Streaming Support: With Spark’s Structured Streaming API, Delta Lake allows you to handle real-time streaming and batch data within the same pipeline, making it ideal for dynamic use cases. Data Versioning & Time Travel: Store multiple versions of your data for historical analysis and rollback, allowing advanced data management without additional complexity. Unified Data Processing: Combine structured, semi-structured, and unstructured data into a unified framework that Spark can process efficiently. Delta Lake enhances Databricks’ ability to manage all data types in one platform. Schema Evolution: As your data evolves, Delta Lake handles schema changes dynamically, allowing for smooth transitions in your data processing workflows without breaking Spark jobs. By integrating Delta Lake into Databricks, you get the best of Spark for fast and scalable data processing along with the reliability and structure of a relational database. #Databricks #ApacheSpark #DeltaLake #BigData #DataEngineering #StreamingData #DataLakehouse

𝐖𝐡𝐲 𝐲𝐨𝐮 𝐦𝐢𝐠𝐡𝐭 𝐜𝐡𝐨𝐨𝐬𝐞 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐨𝐯𝐞𝐫 𝐣𝐮𝐬𝐭 𝐮𝐬𝐢𝐧𝐠 𝐒𝐩𝐚𝐫𝐤: Databricks goes beyond Apache Spark, offering many advantages and enhancements that make it a powerful platform for data engineering, data science, and machine learning tasks. Here are some reasons/comparisons . 1. Managed Spark: Databricks manages Spark clusters for you, so no more worrying about setting up or maintaining infrastructure. 2. Optimized Performance: With Databricks’ proprietary Photon engine, jobs often run faster and more efficiently compared to standalone Spark. 3. Built-in Collaboration: Databricks provides notebooks where data engineers, data scientists, and analysts can collaborate easily in real-time. 4. Automatic Scaling: Databricks auto-scales resources based on demand, ensuring optimal performance without manual intervention. 5. Simplified ETL: Using Delta Lake on Databricks simplifies ETL workflows with improved data reliability and consistency. 6. Unified Data Engineering & Analytics: Databricks combines data processing, machine learning, and analytics on a single platform. 7. Advanced Integrations: It seamlessly integrates with Azure, AWS, and other cloud services, making it easy to pull in data from various sources. 8. Security and Compliance: Databricks provides enterprise-level security features, including data encryption and compliance with standards like GDPR. 9. Job Scheduling: Built-in tools for orchestrating and scheduling jobs make managing pipelines easier. 10. Delta Lake : Data Reliability: Delta Lake provides ACID transactions, ensuring data consistency and reliability that basic Spark lacks. Time Travel: Delta Lake allows you to access and restore previous versions of data, which is essential for auditing and recovery. 11. Continuous Updates: With Databricks, you’re always on the latest Spark version, ensuring access to new features and improvements without manual upgrades. To summarize, while Apache Spark is powerful on its own, Databricks offers a more user-friendly, collaborative, and performance-optimized platform with enterprise features that reduce infrastructure management, enhance data reliability, and streamline workflows. Explore this databricks official documentation for more knowledge https://lnkd.in/gmyWmGN3 🔄 Please Like, repost ✅, if you find them useful !! 🤝 Follow 👨💻Abhisek Sahu for a regular curated feed of Data Engineering insights and valuable content! #databricks #apachespark #pyspark #deltalake #dataengineering #bigdata

🌐 Transforming Data Management with Databricks' Photon Engine 🔧 Databricks has introduced Photon, a cutting-edge vectorized query engine designed to revolutionize the Lakehouse architecture. Vu Trinh’s insightful article highlights the motivations and technological innovations behind Photon, which aims to streamline data processing and enhance performance. 🔍 Key Features and Insights: 🔹 Vectorized Execution: Photon uses a vectorized model instead of traditional code generation, enabling runtime adaptivity and efficient handling of diverse datasets. 🔹 C++ Development: Built in C++ for better performance control, Photon surpasses the limitations of JVM-based engines, enhancing memory management and SIMD operations. 🔹 Seamless Integration: Photon integrates with Databricks Runtime (DBR) and Apache Spark APIs, allowing users to leverage existing workflows without modifications. 🌐 Real-World Applications: 🔹 Enhanced Query Performance: Improve data query speeds and efficiency, crucial for large-scale analytics. 🔹 Cost Efficiency: Reduce operational costs by optimizing resource usage and minimizing complex ETL processes. 🔹 Scalability: Handle a wide range of data types and volumes, ensuring robust performance across different datasets. 👉 https://lnkd.in/dzsDKJRW Let's discuss: 🔹 How can Photon’s innovations enhance your data management strategies? 🔹 What benefits do you see in adopting vectorized query engines? #Databricks #PhotonEngine #DataManagement #TechInnovation #MachineLearning #BigData #CloudComputing #DigitalTransformation #DataScience #TechTrends

Delta Lake is changing the game for big data workloads. As an open-source storage layer built on top of Apache Spark, Delta Lake brings ACID transactions to the table, ensuring data integrity even in the face of concurrent writes and reads. But that's not all. Delta Lake also offers scalability through the distributed computing capabilities of Spark, schema enforcement and evolution to maintain data quality, and time travel to access data snapshots at any point in time. Delta Lake even supports both batch and streaming workloads, providing a unified interface for processing real-time and historical data. And with optimized data lake storage, including efficient file formats and features like data compaction and skipping, query performance is improved. For data engineers and data scientists looking to build robust data pipelines and conduct analytics on large-scale data sets, Delta Lake is a reliable and efficient solution. Check it out! #DeltaLake #ApacheSpark #BigData #DataPipelines #DataAnalytics #DataEngineer #BigDataDeveloper

What is Delta lake and how is it different that other data storage frameworks? Delta Lake is the optimized storage layer that provides the foundation for tables in a lakehouse on Databricks. It enables organizations to access and analyze new data in real time. Data warehouses excel in structured data and fast queries but falter when confronted with unstructured data. Conversely, data lakes adeptly handle unstructured data but lack the performance needed for quick queries. Delta lakes combines the best of both data warehouse and data lake. What are the features and benefits of Delta Lake? 1️⃣ Open Format Compatibility: Delta Lake leverages Apache Parquet and seamlessly integrates with Apache Spark for flexible operations. 2️⃣ ACID Transactions: Delta Lake ensures data integrity with ACID properties, providing accurate audit trails. 3️⃣ Time Travel: Delta Lake's transaction log enables precise data recreation at any point in time. 4️⃣ Schema Enforcement: Delta Lake enforces data consistency, preventing corruption and enhancing reliability. 5️⃣ Data Manipulation Language (DML) Operations: Delta Lake supports merge, update, and delete commands for complex use cases. Which platforms support Delta Lake? It works seamlessly with popular platforms like Databricks, Apache Spark, Azure Synapse Analytics, AWS Glue, and Google Cloud Platform, making data management easier and more flexible for users everywhere In summary, Delta Lake transforms your data lake into a more reliable and performant data platform. It ensures data quality, supports complex data processing needs, and enhances overall performance. #dataengineering #WhatsTheData

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 

Delta Tables in Data engineering : These tables are transforming big data processing in Apache Spark, making pipelines reliable, fast, and easy. With features like ACID transactions, schema enforcement, and time travel, Delta tables simplify our data journey. --------------------------------------------------------------------------------------Here's a quick notes on what Delta tables offer: ACID Transactions: Delta tables ensure Atomicity, Consistency, Isolation, and Durability, guaranteeing data integrity even in the face of failures. Schema Enforcement: With Delta, you can enforce schema on your data, ensuring consistency and preventing unexpected changes. Time Travel: One of the coolest features! Delta allows you to access data snapshots at different points in time, enabling easy rollback, auditing, and analytics on historical data. Optimized Performance: Delta employs various optimization techniques like file management, indexing, and caching, resulting in faster query processing and reduced overhead. #databricks #dataconsistency #datalake #azure #deltalake #aws #datanegineering

"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