Apache Samza At 1.0

Jagadish Venkatraman announces Apache Samza 1.0:

We are pleased to announce today the release of Samza 1.0, a significant milestone in the history of the project. Apache Samza is a distributed stream processing framework that we developed at LinkedIn in 2013. Samza became a top-level Apache project in 2014. Fast-forward to 2018, and we currently have over 3,000 applications in production leveraging Samza at LinkedIn. The use-cases include detecting anomalies, combating fraud, monitoring performance, notifications, real-time analytics, and many more. Today, Samza integrates not only with Apache Kafka, but also with many other systems, including Azure EventHubsAmazon Kinesis, HDFS, ElasticSearch, and Brooklin. Multiple companies like Slack, TripAdvisor, eBay, and Optimizely have adopted Samza.

We view Samza 1.0 as a step towards our vision of making stream processing universally accessible. In this post, we describe our journey in building and scaling a distributed stream processing system. We also present the key features in Samza 1.0: a rich high-level API, event-time-based processing, integration with Apache Beam, Samza SQL, a standalone mode to run Samza without YARN, and a new test framework for Samza applications.

This runs in the same space as Spark Streaming, Flink, and Kafka Streams, so there are plenty of competitors and a lot of innovation.

Related Posts

Performance Tuning Neural Network Training

Sean Owen takes us through a few techniques for speeding up neural network model training: Step #2: Use Early StoppingKeras (and other frameworks) have built-in support for stopping when further training appears to be making the model worse. In Keras, it’s the EarlyStopping callback. Using it means passing the validation data to the training process for evaluation […]

Read More

Machine Learning and Delta Lake

Brenner Heintz and Denny Lee walk us through solving data engineering problems with Delta Lake: As a result, companies tend to have a lot of raw, unstructured data that they’ve collected from various sources sitting stagnant in data lakes. Without a way to reliably combine historical data with real-time streaming data, and add structure to […]

Read More

Categories

November 2018
MTWTFSS
« Oct Dec »
 1234
567891011
12131415161718
19202122232425
2627282930