The Hortonworks Blog

Posts categorized by : Storm

We are excited to announce that Apache Kafka 0.8.1.1 is now available as a technical preview with Hortonworks Data Platform 2.1. Kafka was originally developed at LinkedIn and incubated as an Apache project in 2011. It graduated to a top-level Apache project in October of 2012.

Many organizations already use Kafka for their data pipelines, including Hortonworks customers like Spotify and Tagged.

What is Apache Kafka?

Apache Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system.…

Chaos Before The Storm … and a Brief History

For its name and the metaphoric image it evokes, Apache Storm lives up to its purpose and promise: to ingest, absorb, and digest an avalanche of real-time data as a stream of unbounded discrete events at scale, speed, and success.

Before Storm, developers used a set of queues and workers to process a stream of real-time events. That is, events were placed on a worker queues, and worker threads plucked events and processed them—transforming, persisting or forwarding them to another queue for further processing.…

Sheetal Dolas is a Principal Architect at Hortonworks. As part of Apache Storm design patterns’ series blog, he explores three options for micro-batching using Apache Storm’s core APIs. This is the first blog in the series.

What is Micro-batching?

Micro-batching is a technique that allows a process or task to treat a stream as a sequence of small batches or chunks of data. For incoming streams, the events can be packaged into small batches and delivered to a batch system for processing [1]

Micro-batching in Apache Storm

In Apache Storm, micro-batching in core Storm topologies makes sense for performance or for integration with external systems (like ElasticSearch, Solr, HBase or a database).…

YARN and Apache Storm: A Powerful Combination

YARN changed the game for all data access engines in Apache Hadoop. As part of Hadoop 2, YARN took the resource management capabilities that were in MapReduce and packaged them for use by new engines. Now Apache Storm is one of those data-processing engines that can run alongside many others, coordinated by YARN.

YARN’s architecture makes it much easier for users to build and run multiple applications in Hadoop, all sharing a common resource manager.…

This summer, Hortonworks presented the Discover HDP 2.1 Webinar series. Our developers and product managers highlighted the latest innovations in Apache Hadoop and related Apache projects.

We’re grateful to the more than 1,000 attendees whose questions added rich interaction to the pre-planned presentations and demos.

For those of you that missed one of the 30-minute webinars (or those that want to review one they joined live), you can find recordings of all sessions on our What’s New in 2.1 page.…

The Apache Storm community recently announced the release of Apache Storm 0.9.2, which includes improvements to Storm’s user interface and an overhaul of its netty-based transport.

We thank all who have contributed to Storm – whether through direct code contributions, documentation, bug reports, or helping other users on the mailing lists. Together, we resolved 112 JIRA issues.

Here are summaries of this version’s important fixes and improvements.

New Feature Highlights Netty Transport Overhaul

Storm’s Netty-based transport has been overhauled to significantly improve performance through better utilization of thread, CPU, and network resources, particularly in cases where message sizes are small.…

We recently hosted the sixth of our seven Discover HDP 2.1 webinars, entitled Apache Storm for Stream Data Processing in Hadoop. Over 200 people attended the webinar and joined in the conversation.

Thanks to our presenters Justin Sears (Hortonworks’ Product Marketing Manager), Himanshu Bari (Hortonworks’ Senior Product Manager for Storm), and Taylor Goetz (Hortonworks’ Software Engineer and Apache Storm Committer) who presented the webinar. The speakers covered:

  • Why use Apache Storm?

The first use of the term BoF session was used at the Digital Equipment Users’ Society (DECUS) conference in the 1960s. Its essence was to bring together like minds and thought leaders—just as birds of the feather flock together— to share and exchange computing ideas, in an informal yet spirited way. Since then, the organizers and sponsors of most computing conferences have been loyal to its essence and spirit.

For ideas and innovation happen in collaboration—not in isolation. …

If you’re excited to get started with the new features in Hortonworks Data Platform 2.1, then we’ve included 4 tutorials for you try out – Sandbox-style.

You can download the HDP 2.1 Technical Preview here, and then get stuck into these great tutorials.

Interactive Query with Apache Hive and Apache Tez

OK, so you’re not going to get huge performance out of a one-node VM, but you can try out Hive on Tez, and see the performance gains versus MapReduce, and also try out features such as Vectorized Query, and the host of new SQL features.…

The pace of innovation within the Apache Hadoop community is truly remarkable, enabling us to announce the availability of Hortonworks Data Platform 2.1, incorporating the very latest innovations from the Hadoop community in an integrated, tested, and completely open enterprise data platform.

Download HDP 2.1 Technical Preview Now

What’s In Hortonworks Data Platform 2.1? Presentation & Applications Enable both existing and new applications to provide value to the organization. Enterprise Management & Security Empower existing operations and security tools to manage Hadoop.…

In February 2014, the Apache Storm community released Storm version 0.9.1. Storm is a distributed, fault-tolerant, and high-performance real-time computation system that provides strong guarantees on the processing of data. Hortonworks is already supporting customers using this important project today.

Many organizations have already used Storm, including our partner Yahoo! This version of Apache Storm (version 0.9.1) is:

  • Highly scalable. Like Hadoop, Storm scales linearly
  • Fault-tolerant. Automatically reassigns tasks if a node fails
  • Reliable. 

I recently sat down with Himanshu Bari to discuss how Apache Ambari will serve as the single point of management for Hadoop 2 clusters integrated with Apache Storm and its real-time, streaming event processing.

Himanshu discusses Apache Storm’s five key benefits and how those will add to the power and stability of a Hadoop 2 stack, providing analysis of huge data flows from the second data is created and then for decades of historical analysis of that data stored in HDFS.…

Last week was a busy week for shipping code, so here’s a quick recap on the new stuff to keep you busy over the holiday season.

In October, we announced our intent to include and support Storm as part of Hortonworks Data Platform. With this commitment, we also outlined and proposed an open roadmap to improve the enterprise readiness of this key project.  We are committed to doing this with a 100% open source approach and your feedback is immensely valuable in this process.

Today, we invite you to take a look at our Storm technical preview.…

Join Hortonworks and Pactera for a Webinar on Unlocking Big Data’s Potential in Financial Services Thursday, November 21st at 12:00 EST.

Have you ever had your debit or credit card declined for seemingly no reason? Turns out, the rejections are not so random. Banks are increasingly turning to analytics to predict and prevent fraud in real-time. That can sometimes be an inconvenience for customers who are traveling or making large purchases, but it’s necessary inconvenience today in order for banks to reduce billions in losses due to fraud.…

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