DataStax, the real-time data company, announced the general availability (GA) of Astra Streaming, an advanced, fully-managed messaging and event streaming service built on Apache Pulsar. Now featuring built-in API-level support for Kafka, RabbitMQ and Java Message Service (JMS), Astra Streaming makes it easy for enterprises to get real-time value from all their data-in-motion. "Because business happens in real time, continuously processing streams of data is imperative for enterprises to optimize decisions, actions and experiences. Streaming data can be a game changer for companies to make predictive business decisions and gain competitive advantages." "Many enterprises are struggling with fragmented and complex streaming architectures, with most of their data-in-motion still siloed in legacy messaging and queuing middleware like JMS and RabbitMQ," said Chris Latimer, vice president of product management at DataStax.

Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark.

This is an article from DZone's 2022 Enterprise AI Trend Report. Today, many large organizations are deploying artificial intelligence (AI) models with an event-driven platform in order to solve two common challenges of leveraging enterprise AI. First, to meet their data needs, enterprises often require a variety of model types that are built on different machine learning (ML), deep learning, and AI languages, frameworks, tools, and systems. These models are tied to various ways of deployment, using tools such as PyTorch, scikit-learn, XGBoost, DJL.AI, spaCy, TensorFlow, ONNX, PMML, Apache MXNet, and H2O. As a result, developers and data engineers need to deploy their models in diverse deployment environments with varying characteristics and restrictions, which makes accessing and managing the models complicated.

For more on Apache Kafka, Apache Pulsar, Apache Spark, and other data technologies, attend the "Data Engineering & Architecture" sessions at the Strata Data Conference in New York City, September 23-26, 2019. With companies producing data from an increasing number of systems and devices, messaging and event streaming solutions--particularly Apache Kafka--have gained widespread adoption. Over the past year, we've been tracking the progress of Apache Pulsar (Pulsar), a less well-known but highly capable open source solution originated by Yahoo. Pulsar is designed to intelligently process, analyze, and deliver data from an expanding array of services and applications, and thus it fits nicely into modern data platforms. Pulsar is also designed to ease the operational burdens normally associated with complex, distributed systems.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some disagreement on whether such a specialized job role/title was needed in the first place. As Eric Colson pointed out in his beautiful keynote at Strata Data San Jose, when done too soon, creating specialized roles can slow down your data team.

Check out the Data Engineering & Architecture and the Data Science & Machine Learning sessions at Strata Data London, May 21-24, 2018. Hurry--early price ends April 6. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon.