Rishabh Pandey’s Post

Key features of Delta Lake on Databricks: ACID Transactions: Ensure data reliability with atomicity, consistency, isolation, and durability. Schema Enforcement: Automatically validate data schema, preventing corrupted data. Time Travel: Access and revert to previous versions of data for auditing and debugging. Scalable Metadata Handling: Efficiently manage massive datasets without slowing down queries. Unified Batch & Streaming: Seamlessly process both batch and streaming data in a single pipeline. Data Lineage: Track the history and flow of your data for better governance. #DataEngineering #BigData #Databricks #CloudComputing

Be sure to register for Databricks webinar tomorrow “Data Engineering in the Age of AI!” This introductory workshop caters to both data engineers seeking hands-on experience and data architects aiming to deepen their knowledge, offering a comprehensive understanding of fundamental data engineering and streaming concepts, including. ·        Introduction to the Data Intelligence Platform ·        Getting started with Delta Live Tables (DLT) for data pipelines ·        Creating data pipelines using DLT with streaming Tables, and Materialized Views ·        Change Data Capture with SCD1 and 2 ·        Mastering Databricks Workflows with advanced control flow and triggers ·        Generative AI for Data Engineers ·        Understand data governance and lineage with Unity Catalog ·        Benefits of Serverless Compute https://lnkd.in/esMM8SQZ #AI #DataEngineering #DataArchitects

Databricks Workshop: Data Engineering in the Age of Al Streaming data pipelines with Delta Live Tables and Databricks Workflows Tuesday, March 19, 2024 | 9:00 AM-10:30 AM PT The workshop covers essential topics like Delta Live Tables for data pipelines, Change Data Capture, advanced Databricks Workflow control, and Generative Al for Data Engineers. Participants will also delve into data governance and lineage using Unity Catalog and explore the benefits of Serverless Compute. Attendees will gain hands-on experience through practical exercises, including data ingestion from various sources, creating batch and streaming pipelines, and utilizing GitHub for collaboration. Plus, a 10-minute Q&A session with expert facilitators ensures all questions are answered. Register here: https://lnkd.in/ebM8J9F7 #DataEngineering #Workshop #DataPipelines #Streaming #DataGovernance #AI #GitHub #DataEnthusiasts #dataengineer #datascience #dataanalytics #databricks #bigdata

Databricks workshop is structured to provide a solid understanding of the following fundamental data engineering and streaming concepts: 1-Introduction to the Data Intelligence Platform 2-Getting started with Delta Live Tables (DLT) for data pipelines 3-Creating data pipelines using DLT with streaming Tables, and Materialized Views 4-Change Data Capture with SCD1 and 2 5-Mastering Databricks Workflows with advanced control flow and triggers 6-Generative AI for Data Engineers 7-Understand data governance and lineage with Unity Catalog 8-Benefits of Serverless Compute Kaniz Fatma Samantha Menot Venkat Krishnan

What is Structured Streaming in Spark and How to Use It for Real-Time Data with Databricks ? Structured Streaming in Spark with Databricks allows us to process real-time data streams as if they were tables, using the same API as batch processing. This simplifies handling continuous data like sensor outputs, social media updates, or online transactions. How we can use it: - Data Input: Incoming data (e.g., JSON files or Kafka messages) is treated as an "input table" that keeps growing. - Queries: We write queries as if working with a static table. Spark runs them incrementally. - - Output Modes: - Complete Mode: Outputs the entire updated result every time. - Append Mode: Outputs only new rows added since the last update. - Update Mode: Outputs only rows that were updated since the last trigger. Real-Life Example: Imagine we're processing live traffic data from a city's sensors to detect congestion. Structured Streaming can track the number of vehicles passing through various intersections in 1-minute windows and update your dashboard in real time. #Databricks #DataEngineering #WhatsTheData

🚀 **Unlock the Power of Distributed Computing with Ray on Databricks!** 🚀 Tired of slow processing large JSON, CSV, Text files? Discover how Ray chunks it—by leveraging parallel processing on Databricks. Learn how to optimize chunk sizes for peak performance and explore real-world applications that enhance scalability and efficiency. Dive into the future of data processing and supercharge your workflows today! 💡📊 #DistributedComputing #Ray #Databricks #BigData #DataProcessing #Scalability #ETL #RealTimeAnalytics

Here at Konverge.AI , we are so much looking forward for #LakeFlow -- unified solution for data ingestion, transformation, and orchestration, built upon Delta Live Table (DLT) framework of Databricks -- announced in Data AI summit held over last 2 days #DataAiSummit2024. The benefits we are looking out for in LakeFlow: 1. LakeFlow is going to be equipped with a host connectors. This would avoid the initial plumbing required to fetch the data from external sources, push/convert it into object storage such as abfss, s3, from where DLT pipelines extract. Though this can be achieved using Partner Connect and Lakehouse federation, but LakeFlow will ease this process further. 2. For implementing CDC, we have to depend on tools like Debezium. For Enterprise customers, the usage of such open source tools involves more convincing and discussions. Lakeflow having the support for CDC will avoid such dependence. 3. Lakeflow is going to have an intuitive pipeline UI. Even non-coders will be able to define an end-to-end pipeline really fast. It will be interesting to see how LakeFlow will fare vis-a-vis the existing Workflow jobs. 4. For orchestrating the jobs, we still require external tools like Apache Airflow. We hope to see LakeFlow addressing this too. 5. In DLT pipelines, we can achieve data quality checks using "expect" function. We are curious to see, how the data quality checks will look like, in LakeFlow UI. 6. Monitoring the DLT pipelines is achieved by utilizing table valued functions (TVF) over event logs. But for the actual Monitoring process, we still require the likes of Grafana and Promethus. Now we are going to have all this, as a part of LakeFlow itself. #databricks #dataaisummit #dataengineering #dataanalytics #LakeFlow #cdc We are sure this LakeFlow development is a major breakthrough making Databricks even more user-friendly and non-coder-friendly. https://dbricks.co/3VAB5Ge

Want to know more about how the latest developments in Data and AI can bring value to your business? Or would you like to hear from Market-leading Organisations on how they transitioned in becoming Data Forward? Click on the link below to sign up:

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Apache Spark enables businesses to scale their data operations effortlessly, process vast datasets quickly, and extract actionable insights at record speed. Whether managing real-time data streams, optimizing machine learning models, or refining complex data workflows, Spark keeps enterprises at the forefront of innovation. Modak’s Spark expertise enables organizations to build efficient, secure, and agile data pipelines. We’re continuously pushing the limits of what Spark can accomplish, creating a future where data fuels innovation and growth. Read more here: https://lnkd.in/gcftscMc #Modak #Spark #SparkEngineering #ApacheSpark #Data #Transformation