Mage Blog

Join us for our first ever in-person data engineering meetup on Tuesday, June 27, 2023 from 6pm to 8pm (PST) in San Francisco, Bay Area! Don't miss out on this fantastic opportunity to learn about the latest technologies and best practices in the data engineering field and network with data professionals!

This guide introduces Apache Flink and stream processing, explaining how to set up a Flink environment and create simple applications. Key Flink concepts are covered along with basic troubleshooting and monitoring techniques. It ends with resources for further learning and community support.

Combine powerful database features with the flexibility of an object storage system by using the Delta Lake framework.

Dive into a comprehensive comparison of Apache Flink and Apache Spark, exploring their differences and strengths in data processing, to help you decide which framework best suits your data processing needs.

Join us for our first ever in-person data engineering meetup on Saturday, May 20, 2023 from 11am to 2pm (IST) in Gurugram, India! Don't miss this fantastic opportunity to connect, learn, and celebrate with your fellow data aficionados.

Discover the Hidden Benefits and Drawbacks of dbt.

Apache Flink is a powerful open-source stream processing framework for big data, offering real-time and batch processing capabilities. With its flexibility and scalability, Flink is ideal for use cases like fraud detection, log analysis, IoT (Internet of Things), anomaly detection, and machine learning, making it a go-to solution for organizations needing real-time analytics and insights.

⚔️ 130 teams across 15 different countries geared up for the first ever Mage Battlegrounds 24-hour virtual hackathon. Only a few emerged victorious. See who came out on top! 🏆

Join us for our first ever data engineering community competition! This 24-hour virtual hackathon will begin on April 15, 2023 with chances to win prizes totaling INR 82,500!

AWS S3 is a widely used option for data lake, let’s see how Singer helps Data Engineers to sync data from AWS S3 (source) to Postgres Database (destination).