From the Dev Team

Follow the latest developments from our technical team

Haohui Mai is a member of technical staff at Hortonworks in the HDFS group and a core Hadoop committer. In this blog, he explains how to setup HTTPS for HDFS in a Hadoop cluster.

1. Introduction

The HTTP protocol is one of the most widely used protocols in the Internet. Today, Hadoop clusters exchange internal data such as file system images, the quorum journals, and the user data through the HTTP protocol.…

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 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.…

We are in the midst of a data revolution. Hadoop, powered by Apache Hadoop YARN, enables enterprises to store, process, and innovate around data at a scale never seen before making security a critical consideration. Enterprises are looking for a comprehensive approach to security for their data to realize the full potential of the Hadoop platform unleashed by YARN, the architectural center and the data operating system of Hadoop 2.

Hortonworks and the open community continue to work tirelessly to enhance security in Hadoop.…

Introduction

HDP 2.1 ships with Apache Knox 0.4.0. This release of Apache Knox supports WebHDFS, WebHCAT, Oozie, Hive, and HBase REST APIs.

Hive is a popular component used for SQL access to Hadoop, and the Hive Server 2 with Thrift supports JDBC access over HTTP. The following steps show the configuration to enable a JDBC client to talk to Hive Server 2 via Knox (Beeline > JDBC over HTTPS > Knox > HTTP > Hive Server2).…

In May, Hortonworks acquired XA Secure and made a promise to contribute this technology to the Apache Software Foundation.  In June, we made it available for all to download and use from our website and today we are proud to announce this technology officially lives on as Apache Argus, an incubator project within the ASF.

This podling has been formed and now the process of graduating Argus to a top-level project (TLP) has begun.…

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.…

“Data is to information society what fuel was to the industrial economy: the critical resource powering the innovations that people rely on,” write Victor Mayer-Schönberger and Kenneth Cukier, in Big Data. Today, big data fuels and engenders innovation of new products and services, according to Forrester.

Just as countries’ fuel repositories need protection and security because they can come under attack, so do companies’ big data repositories. “Companies, markets, and countries are increasingly under attack from cyber-criminals.…

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.…

Go to page:12345...10...Last »