Kai Waehner’s Post

Nice article to understand the combination of Lakehouse and Streaming 👇

Data Engineers, Qubole is here to make your life easier. Enjoy benefits such as... 🔹 High performance at a lower TCO 🔹 Higher productivity when building, testing, and managing ETL pipelines 🔹 A wide range of connectivity to Real-Time, NoSQL, and Data Warehouse stores ...when you use Qubole's open data lake platform! Our Pipelines Service is built to help you efficiently move data from sources such as Kafka and Kinesis to targets such as S3, HIVE, and Snowflake. Check out our demo to learn how we can help you easily deliver better data pipelines at a lower cost: https://loom.ly/dUw6Nhs . . . . . #Qubole #ADHOCanalytics #AI #Airflow #AmazonRedshift #ApacheSpark #AutomatedCluster #AutoScale #AWS #BigData #BigDataPipeline #BigDataPlatform #Cloud #CloudDataLake #CloudElasticity #CloudPlatform #CloudServices #DataAnalytics #DataArchitecture #DataCompliance #DataEngineering #DataHub #DataIngestion #DataLake #DataPlatform #DataProcessing #DataProcessingPlatform #DataScience #DataSecurity #DataWarehouse

🚀 Exciting Journey: Building a Snowflake Data Pipeline Across Clouds & Kafka with Data Modeling 🌐🔗 👋 Exciting times in the world of data! 🌟 Today, I'm thrilled to share our journey in building a robust Snowflake Data Pipeline spanning across Azure, AWS, GCP, and Kafka, enriched with effective Data Modeling practices. 🛠️💡 🌐 Multi-Cloud Magic: Integrating data from Azure, AWS, and GCP offers unparalleled flexibility and scalability. Leveraging the strengths of each cloud provider, we've woven a seamless fabric to enable data flow across diverse environments. The key? Interoperability and embracing the power of hybrid and multi-cloud architectures! 🚀 🌊 Streaming with Kafka: Real-time data is the heartbeat of modern analytics. Our Snowflake Data Pipeline embraces Kafka for its streaming prowess. The ability to process and analyze data as it flows unlocks new dimensions for decision-making. 🔄💬 🎨 Data Modeling Brilliance: Behind every successful pipeline is a solid foundation of data modeling. Thoughtful design ensures efficiency, accuracy, and adaptability. We've delved into the art and science of data modeling to construct a resilient structure that evolves with our business needs. 🏗️📊 🌪️ Challenges Turned Opportunities: No journey is complete without challenges. Scaling across clouds and integrating with Kafka posed unique hurdles, but they became opportunities for innovation. Collaboration and learning paved the way for breakthroughs, creating a stronger and more adaptable data ecosystem. 💪🔍 🚀 Key Takeaways: Interoperability Wins: Seamlessly integrating data across clouds maximizes flexibility and resource utilization. Real-Time Insights: Kafka's streaming capabilities unlock the door to real-time decision-making. Data Modeling Excellence: A well-designed data model is the bedrock of a robust data pipeline. 🚀 Gratitude & Learning: Shoutout to our amazing team for their dedication and expertise! The learning never stops, and we're excited about the road ahead as we continue to push the boundaries of what's possible in the world of data. Let's connect and share insights on building data pipelines! How has your journey been in integrating diverse data sources and models? 🌐💬 #DataPipeline #Snowflake #Azure #AWS #GCP #Kafka #DataModeling #TechInnovation 🚀

📣 Data heroes! 📣 Ever wondered how to run a custom query in Databricks using Azure Data Factory? I've got you covered! 🚀 In my latest article, I dive deep into the integration of Azure Data Factory and Databricks. While it may seem like a straightforward process, the reality can be quite different. I break down the complexities and guide you through the process of running a custom query on Databricks and retrieving the results using Azure Data Factory. 📊 Stay ahead of the curve and enhance your data movement and transformation skills with my new guide. Don't miss out! 💡 #AzureDataFactory #Databricks #DataEngineering #Azure #CloudComputing" https://lnkd.in/dSatNkB5

🚀 Unlock the Power of Real-Time Analytics with Kafka and Databricks! 🚀 Are you looking to enhance your big data capabilities? Integrate Kafka with Databricks to leverage the full potential of real-time data streaming and analytics. Here’s how you can get started: 1. Setup Kafka Cluster: Deploy your Kafka cluster on-premises or in the cloud. Ensure it’s configured for high availability and scalability. 2. Create Kafka Topics: Define the topics in Kafka that will handle your data streams. Structure them to align with your data sources and business needs. 3. Connect Kafka to Databricks: Utilize the Kafka connector in Databricks to stream data into your Databricks environment. Configure the connector with the necessary security and data transformation settings. 4. Data Processing with Spark: Use Apache Spark within Databricks to process and analyze your data streams. Take advantage of Spark’s powerful processing capabilities to perform real-time analytics, machine learning, and more. 5. Visualize and Share Insights: Employ Databricks’ visualization tools to create dashboards and reports. Share these insights across your organization to drive data-informed decisions. 6. Automate and Scale: Set up automated workflows to handle data ingestion and processing. Scale your infrastructure as your data needs grow to maintain performance and reliability. By integrating Kafka with Databricks, you can transform raw data into actionable insights in real-time. Embrace the future of big data analytics today! 💡 #BigData #RealTimeAnalytics #Kafka #Databricks #ApacheSpark #DataScience #DataEngineering #MachineLearning

Hello, My latest article on "Convert Parquet File to Delta Lake using Azure Data Factory" has been published! Check it out here https://lnkd.in/ghMPd-gc. #DataEngineering #AzureDataFactory #DeltaLake #BigData #TechArticle

What is a Data Lakehouse ??? ✅️ Introduction to Lakehouses: 📍Era of 1990s: Data Warehouses. 📍Era of 2010s: Data Lakes. 📍Lakehouse Era: Combining the best of both worlds. ✅️ Lakehouse Benefits: 📍Unified data storage, reducing the need for multiple data copies. 📍Significant cost savings by leveraging low-cost cloud storage. 📍Simplified ETL processes, reducing opportunities for data quality issues. 📍Faster development cycles and reduced time to value. Big shoutout to Databricks Apache Spark Microsoft Azure Ramesh Retnasamy #DataScience #BigData #DataWarehouse #DataLakes #Lakehouse #DataRevolution #TechInnovation #DataStorage #CloudComputing #Dataengineer #Databricks #ApacheSpark #DeltaLake

Interesting move by Databricks, not sure what their ETL partner community will think about this. Then again, Amazon AppFlow didn't have a huge impact on AWS's ETL partner community, so we'll have to see. https://lnkd.in/eWH2yuHb #databricks