The Hortonworks Blog

Posts categorized by : Innovation from Hortonwoks

VoltDB is a Certified Hortonworks Technology Partner and developers of an in-memory relational DBMS capable of supporting high volume OLTP and real-time analytics with Hortonworks Data Platform. Our guest blogger today is John Piekos, vice president of engineering at VoltDB.

It’s a common phrase here at VoltDB: Streaming Apps are Really Database Apps When You Use a Database that’s Fast Enough.

What does that mean?

We’re seeing a trend: developers are struggling to create interactive, real-time applications on fast streaming data.…

In our series on Data Science and Hadoop, predicting airline delays, we demonstrated how to build predictive models with Apache Hadoop, using existing tools. In part 1, we employed Pig and Python; part 2 explored Spark, ML-Lib and Scala.

Throughout the series, the thesis, theme, topic, and algorithms were similar. That is, we wanted to dismiss the misconception that data scientists – when applying predictive learning algorithms, like Linear Regression, Random Forest or Neural Networks to large datasets – require dramatic changes to the tooling; that they need dedicated clusters; and that existing tools will not suffice.…

Apache HBase is the online database natively integrated with Hadoop, making HBase the obvious choice for applications that rely on Hadoop’s scale and flexible data processing. With the Hortonworks Data Platform 2.2, HBase High Availability has taken a major step forward, allowing apps on HBase to deliver 99.99% uptime guarantees. This blog takes a look at how HBase High Availability has improved over the past 12 months and how it will improve even more in the future.…

The Hadoop Distributed File System (HDFS) is the reliable and scalable data storage core of the Hortonworks Data Platform (HDP). In HDP, HDFS and YARN combine to form the distributed operating system for your data platform, providing resource management for diverse workloads and scalable data storage for the next generation of analytical applications.

In this blog, we’ll describe the key concepts introduced by Heterogeneous Storage in HDFS and how they are utilized to enable key tiered storage scenarios.…

Last year on December 11th, Hortonworks presented the sixth of 8 Discover HDP 2.2 webinars: Apache HBase with YARN & Slider for Fast NoSQL Access. Justin Sears, Carter Shanklin and Enis Soztutar hosted this 6th webinar in the series.

After Justin Sears set the stage for the webinar by explaining the drivers behind Modern Data Architecture (MDA), Carter Shanklin and Enis Soztutar introduced Apache HBase and discussed how to use it with Apace Hadoop YARN and Apache Slider for fast NoSQL access to your data.…

We take pride in producing valuable technical blogs and sharing it with a wider audience. Of all the blogs published in 2014 on our website, the following were most popular:

  • Improving Spark for Data Pipelines with Native YARN Integration.

    Gopal Vijayaraghavan and Oleg Zhurakousky demonstrate improved Apache Spark, which with the help of the pluggable execution context.

  • HDP 2.2 A Major Step Forward for Enterprise Hadoop

    Tim Hall outlines six months of innovation and new features across Apache Hadoop and its related projects.

  • On December 4th, Hortonworks presented the fifth of 8 Discover HDP 2.2 webinars: Apache Kafka and Apache Storm for Stream Data Processing. Taylor Goetz, Rajiv Onat, and Justin Sears hosted this 5th webinar in the series.

    After Justin Sears set the stage for the webinar by explaining the drivers behind Modern Data Architecture (MDA), Rajiv Onat and Taylor Goetz introduced and discussed how to use Apache Kafka and Apache Storm for stream data processing.…

    With Apache Hadoop YARN as its architectural center, Apache Hadoop continues to attract new engines to run within the data platform, as organizations want to efficiently store their data in a single repository and interact with it for batch, interactive and real-time streaming use cases. Apache Storm brings real-time data processing capabilities to help capture new business opportunities by powering low-latency dashboards, security alerts, and operational enhancements integrated with other applications running in the Hadoop cluster.…

    The Stinger.next initiative, with its focus on transactions, sub-second queries and SQL:2011 Analytics evolves Apache Hive to allow it to run most of the analytical workloads that are typical within a data warehouse, but now at petabyte scale. The first phase of Stinger.Next, delivered in Apache Hive 0.14 and in HDP 2.2, delivers transactions with ACID semantics a critical step in the evolution of the Hive as the defacto standard for SQL in Hadoop.…

    Hadoop Operations for provisioning, managing and monitoring a cluster are critical to the success of a Hadoop project and having an intuitive and effective set of tooling has become a foundational element of a Hadoop distribution. Within HDP, we provide completely open source Apache Ambari to help you be successful with Hadoop operations.

    The rate of innovation in the Ambari community is astonishing and this pace continues with the 7th release of the project this year alone, Apache Ambari 1.7.0.…

    Data platforms within Enterprises are in midst of a generational shift. After successful reliance on databases for decades, leading organizations today are complementing their data platforms to create a Modern Data Architecture (MDA) with Apache Hadoop in a Data Lake environment. Hadoop with its scale out and schema free architecture enables organizations to store and analyze all its structured and unstructured data in a single consolidated data environment. A key partner in the Hadoop journey has been the complementary infrastructure of server, storage and networking.…

    The successful Hadoop journey typically starts with new analytic applications, which lead to a Data Lake. As more and more applications are created that derive value from the new types of data, an architectural shift happens in the data center: companies gain deeper insight across a large, broad, diverse set of data at efficient scale. They create a Data Lake.

    Cisco and Hortonworks have partnered to build a highly efficient, highly scalable way to manage all your enterprise data in a data lake.…

    With YARN as its architectural center, Apache Hadoop continues to attract new engines to run within the data platform, as organizations want to efficiently store their data in a single repository and interact with it simultaneously in different ways. Apache Tez supports YARN-based, high performance batch and interactive data processing applications in Hadoop that need to handle datasets scaling to terabytes or petabytes.

    The Apache community just released Apache Pig 0.14.0,and the main feature is Pig on Tez.…

    While YARN has allowed new engines to emerge for Hadoop, the most popular integration point with Hadoop continues to be SQL and Apache Hive is still the defacto standard. Although many SQL engines for Hadoop have emerged, their differentiation is being rendered obsolete as the open source community surrounds and advances this key engine at an accelerated rate.

    Last week, the Apache Hive community released Apache Hive 0.14, which includes the results of the first phase in the Stinger.next initiative and takes Hive beyond its read-only roots and extends it with ACID transactions.…

    Hortonworks is pleased to be part of the “going green” movement and even more pleased to introduce guest bloggers from Actian and Slingshot Power. In this blog, Slingshot Power describes their use case on how Hadoop and analytics can influence and increase the adoption of clean energy use.

    By Ashish Gupta, CMO & SVP Business Development, Actian

    Recently, we announced with Slingshot Power their use of Hortonworks Data Platform (HDP) and the Actian Analytics Platform – Hadoop SQL Edition.…