Apache Beam

FOLLOW https://lnkd.in/gEE_Y5M5 🚀 Mastering Real-Time Data with Apache Beam Window Functions! In this article, I dive into how Apache Beam’s windowing functions can help efficiently process continuous data streams. From fixed-time windows to session-based analytics, these techniques empower data engineers to handle real-time data processing with precision. Learn how window functions work, explore use cases like IoT, fraud detection, and real-time analytics, and discover why Apache Beam stands out for stream processing! 🔗 Read on to unlock the power of windowing in your data pipelines! #ApacheBeam #DataEngineering #StreamProcessing #BigData #RealTimeData #WindowFunctions #TechInnovation

Vijay Kulkarni Harish Nagu Sana and I wrote a Medium blog titled "Stateful Feature Backfill on Apache Beam." We have learned a lot about Apache Beam, including checkpointing, savepointing, state management, and much more during the project. Check it out! #apachebeam #intuit #intuitengineering #featuremanagement #intuitAi https://lnkd.in/gVwxaeby

NEW VIDEO: Transcribe Newscasts in Parallel with Apache Beam and Hugging Face

Dialpad is hiring a software engineer in Vancouver, Canada to work on their data platform using Apache Beam. Evan Galpin, a long time Beam user and a committer, shared this opportunity with the Beam community. Quoting Evan: " I currently have the opportunity to hire for a role[1] where Beam is a key component of the day-to-day work, alongside other OSS technologies, with particular focus on low-latency streaming use cases. This role is a marriage between Data Platform Engineering and Production Engineering where our data-customers are end-users, which brings all kinds of compelling constraints and challenges. As a team, we enjoy being both users and builders of the OSS technologies we employ, and care deeply about honing our craft as Software Engineers. If this sounds like you, and you are based anywhere in *Canada*, I strongly encourage you to apply! " [1] https://lnkd.in/gaUYTApX

Excited to share that I attended a 3-day Apache Beam workshop hosted by Beam College! Apache Beam is a key tool for Data Engineers, providing a unified programming model for batch and stream processing. The workshop was incredibly informative, and I learned a lot of practical and beneficial topics to advance my data engineering expertise

🗞️ Here's your weekly ASF release roundup! 🗞️ 👉 HttpComponents Core 5.3 is now available for download. To download: https://bit.ly/3XxoUK7 To read the release notes: https://bit.ly/3TwkPF2 👉 Apache Groovy 4.0.23 has been released. Apache Groovy is a multi-faceted programming language for the Java virtual machine. To download: https://bit.ly/3qNI7uM 👉 Apache Beam 2.59.0 has been released. This release includes bug fixes, features, and improvements detailed on the Beam blog: https://bit.ly/47CNk9F 👉 Apache Arrow ADBC 14 has been released. Apache Arrow is a columnar in-memory analytics layer designed to accelerate big data. The release is now available from here: https://bit.ly/3BkaOnS 👉 Apache Flink CDC 3.2.0 is now available. Apache Flink CDC is a distributed data integration tool for real time data and batch data. To download: https://bit.ly/3BsFY9E #opensource #data #bigdata #Java

💡 Optimizing Join Operations in Apache Beam for Large Datasets 🔄 Handling large dataset joins in Apache Beam can be tricky, especially when scaling for big data. Efficient join operations help avoid bottlenecks and ensure your pipelines run smoothly. Here are some strategies to handle joins in Apache Beam: Broadcast Joins: Use this strategy when one side of the join is significantly smaller than the other. You can broadcast the smaller dataset as a side input (using beam.pvalue.AsSingleton() or beam.pvalue.AsDict()) to all workers, ensuring that each parallel processing task has access to the data without the need for shuffling. Repartitioning & GroupByKey: For large datasets, repartitioning is key to avoiding skew. You can repartition the larger datasets by a common key to distribute the data more evenly across workers. Follow this with a GroupByKey operation, which ensures that each key is joined with the corresponding values from both datasets efficiently. Sharding Large Datasets: Similar to handling side inputs, sharding larger datasets during join operations distributes the load evenly across multiple workers. By controlling the sharding logic, you prevent data skew and improve parallelism. Join Libraries: Beam provides a CoGroupByKey transform for performing inner and outer joins. This transform groups all elements of both datasets by key, allowing you to join the two datasets in a single operation. Windowing & Triggering: When joining time-sensitive data, make use of windowing and triggering to break the datasets into manageable chunks. This approach helps distribute load over time and ensures timely data processing without overwhelming your workers. By applying these join optimization techniques, you can boost the efficiency of your Beam pipelines and ensure consistent performance at scale. #ApacheBeam #DataJoins #BigData #DataEngineering #PipelineOptimization #TechTips #DataProcessing