Apache Pulsar 2.0 Brings Enterprise-Class Scale, Speed and Functionality to Streaming Data Processing

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Streamlio, the intelligent platform for fast data, announced availability of Apache Pulsar 2.0, a significant new release of the streaming messaging solution at the core of the Streamlio platform. Building on the proven Apache Pulsar foundation, this release adds new capabilities to Apache Pulsar that support easy development and deployment of modern data-driven applications and demonstrate the maturity and enterprise-class capabilities of Pulsar while delivering significantly better performance, scalability and durability than older messaging platforms such as Apache Kafka, as verified in real-world OpenMessaging benchmark tests.

Messaging solutions for streaming data are a critical and central component of software infrastructure for modern digital companies, providing the glue that connects diverse data with users and applications. Applications in financial services, IoT, advertising, retail, security and fraud prevention, just-in-time transformation, real-time analytics, machine learning and other fast-data scenarios have demonstrated the value of processing data as it arrives rather than waiting on batch processing pipelines. Today’s fast evolving and massively scalable data-driven applications require performance and durability at scale without operational burdens. Apache Pulsar 2.0 is the first platform to meet these twin enterprise performance and functionality demands.

This is an important milestone demonstrating the maturity and continued innovation of the Apache Pulsar solution,” notes Matteo Merli, co-founder of Streamlio and the architect and lead developer of Pulsar while at Yahoo. “Designed from day one to deliver unmatched performance, scalability, and operational simplicity, Pulsar provides the enterprise-class technology needed to enable companies to move beyond the limits of traditional batch-centric approaches to the data-driven future where they can immediately process and act on fast-moving data as quickly as it arrives.”

The latest enhancements to Pulsar dramatically decrease the cost and complexity involved in analyzing, transforming and acting on data as it arrives. This new functionality builds on Pulsar’s performance and durability advantages with features to improve developer and application efficiency, including:

  • Pulsar Functions, a stream-native processing capability first announced as a preview feature earlier this year. Pulsar Functions is now generally available for developers looking for a simple way to apply transformations and analytics directly to data as it flows through Pulsar, without requiring external systems or add-ons.
  • Schema Registry, a new feature that simplifies development of data-driven applications by providing developers the ability to define and validate the structure and integrity of data flowing through Pulsar.  This easier and more consistent method of tracking information in data streams is critical at the scale Pulsar operates in most enterprises.
  • Topic Compaction, a new enhancement to Pulsar storage that improves performance for applications consuming data from Pulsar in coordination with the Apache Bookkeeper solution for streaming data storage.

This release also delivers significant performance enhancements that further raise the bar for enterprise-class streaming data processing. These enhancements, which take full advantage of scaling optimizations in recent releases of the Apache BookKeeper stream storage solution used within Pulsar, extend Pulsar’s performance lead, demonstrating 7x greater throughput than Apache Kafka.

At STICorp we’re using Apache Pulsar as the messaging solution that processes data for our production applications,” said Daniel Ferreira Jorge, Director of Operations at STICorp. “We switched from Apache Kafka to Pulsar because Pulsar has proven to have higher performance, be more reliable and is easier to operate than other solutions. We’re excited about the release of Pulsar 2.0 because features like Pulsar Functions and the many other enhancements it has will make it even easier for us to build the data pipelines that connect, process and transform data for our applications.”


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