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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.…

The Journey

Almost to the date, two years ago the Apache Hadoop community voted to make YARN a sub-project of Apache Hadoop followed by the GA release nearly a year ago last fall.

Since then, it’s becoming plainly obvious that Apache Hadoop 2.x, powered by YARN as its architectural center, is the best platform for workloads such as Apache Hadoop MapReduce, Apache Pig, Apache Hive etc., which were designed to process data on Apache Hadoop HDFS.…

This week we continue our YARN webinar series with detailed introduction and a developer overview of Apache Tez.  Designed to express fit-to-purpose data processing logic, Tez enables batch and interactive data processing applications spanning TB to PB scale datasets.  Tez offers a customizable execution architecture that allows developers to express complex computations as dataflow graphs and allows for dynamic performance optimizations based on real information about the data and the resources required to process it.…

A transformation is occurring in the data center.  Enterprises are turning to a modern data architecture in order to derive maximum value from both big and small data across their organization.  They are building new analytic apps that unlock opportunity and allow them to maintain or create competitive edge. Apache Hadoop is at the center of this architecture and integrates with the technologies that run your business to augment and extend this new value.…

Hortonworks Software Engineers Vinod Kumar Vavilapalli (Apache Hadoop YARN committer) and Jian He (Apache YARN Hadoop committer) discuss Apache Hadoop YARN’s Resource Manager resiliency upon restart in this blog.This is their third blog post in our series on motivations and architecture for improvements to the Apache Hadoop YARN’s Resource Manager (RM) resiliency. Others in the series are:

Introduction Phase II – Preserving work-in-progress of running applications

ResourceManager-restart is a critical feature that allows YARN applications to be able to continue functioning even when the ResourceManager (RM) crash-reboots due to various reasons.…

It’s been a busy year for Apache Ambari. Keeping up with the rapid innovation in the open community certainly is exciting. We’ve already seen six releases this year to maintain a steady drumbeat of new features and usability guardrails. We have also seen some exciting announcements of new folks jumping into the Ambari community.

With all these releases and community activities, let’s take a break to talk about how the broader Hadoop community is affecting Ambari and how this is influencing what you will see from Ambari in the future.…

Apache Hadoop has come along a long way. From its early days as a platform to index the web, it has evolved to its current interactive, real-time, and batch processing capabilities spanning gigabytes to petabytes of content. A key stepping stone in this evolution has been Apache Hadoop YARN. YARN has enabled enterprises to onboard “fit for purpose” processing engines to its Hadoop Data Lake. This has opened the Data Lake to rapid and unbridled innovation by the ISV community and delivered differentiated insight to the enterprise.…

SequenceIQ provides an API and platform to build predictive applications and turn data into tangible assets. In this guest blog, SequenceIQ Co-founder and CTO Janos Matyas (@sequenceiq), explains why his team chose Apache Ambari for provisioning Hadoop clusters and how they contributed to the Ambari project.

At SequenceIQ, we frequently provision Hadoop clusters on different environments. For a long time, we searched for the right provisioning and management tool.…

Apache Hadoop clusters grow and change with use. Maybe you used Apache Ambari to build your initial cluster with a base set of Hadoop services targeting known use cases and now you want to add other services for new use cases. Or you may just need to expand the storage and processing capacity of the cluster.

Ambari can help in both scenarios. In this blog, we’ll cover a few different ways that Ambari can help you expand your cluster.…

Earlier this month, the Apache Ambari community released Apache Ambari 1.6.1, which includes multiple improvements for performance and usability. The momentum in and around the Ambari community is unstoppable. Today we saw the Pivotal team lean in to Ambari, and this is the sixth release of this critical component in 2014, proving again that open source is the fastest path to innovation.

Many thanks to the wealth of contribution from the broad Ambari community that resulted in 585 JIRA issues being resolved in this release.…

There are many projects that have been contributed to the Apache Software Foundation (ASF) by both vendors and users alike that greatly expand Apache Hadoop’s capabilities as an enterprise data platform.

While Hadoop – with YARN at its architectural center – provides the foundational capabilities for managing and accessing data at scale, a broader blueprint for Enterprise Hadoop has emerged that specifies how this array of Apache projects fit across five distinct pillars to form a complete enterprise data platform: data access, data management, security, operations and governance.…

Last week, Apache Tez graduated to become a top level project within the Apache Software Foundation (ASF). This represents a major step forward for the project and is representative of its momentum that has been built by a broad community of developers from not only Hortonworks but Cloudera, Facebook, LinkedIn, Microsoft, NASA JPL, Twitter, and Yahoo as well.

What is Apache Tez and why is it useful?

Apache™ Tez is an extensible framework for building YARN based, high performance batch and interactive data processing applications in Hadoop that need to handle TB to PB scale datasets.…

The Apache Pig community released Pig 0.13. earlier this month. Pig uses a simple scripting language to perform complex transformations on data stored in Apache Hadoop. The Pig community has been working diligently to prepare Pig to take advantage of the DAG processing capabilities in Apache Tez. We also improved usability and performance.

This blog post summarizes the progress we’ve made.

Support for Backends Other Than MapReduce

We made the Pig 0.13 architecture more general to support multiple backends beyond just MapReduce, while maintaining backward compatibility.…

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