Apache Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. Hadoop services provide for data storage, data processing, data access, data governance, security, and operations.
The genesis of Hadoop came from the Google File System paper that was published in October 2003. This paper spawned another research paper from Google – MapReduce: Simplified Data Processing on Large Clusters. Development started in the Apache Nutch project, but was moved to the new Hadoop subproject in January 2006. The first committer added to the Hadoop project was Owen O’Malley in March 2006. Hadoop 0.1.0 was released in April 2006 and continues to be evolved by the many contributors to the Apache Hadoop project. Hadoop was named after one of the founder’s toy elephant.
In 2011, Rob Bearden partnered with Yahoo! to establish Hortonworks with 24 engineers from the original Hadoop team including founders Alan Gates, Arun Murthy, Devaraj Das, Mahadev Konar, Owen O’Malley, Sanjay Radia, and Suresh Srinivas.
Some of the reasons organizations use Hadoop is its’ ability to store, manage and analyze vast amounts of structured and unstructured data quickly, reliably, flexibly and at low-cost.
The Hadoop Distributed File System (HDFS) provides scalable, fault-tolerant, cost-efficient storage for your big data lake. It was designed to span large clusters of commodity servers scaling up to hundreds of petabytes and thousands of servers. By distributing storage across many servers, the combined storage resource can grow linearly with demand while remaining economical at every amount of storage.
MapReduce is the original framework for writing massively parallel applications that process large amounts of structured and unstructured data stored in HDFS. MapReduce can take advantage of the locality of data, processing it near the place it is stored on each node in the cluster in order to reduce the distance over which it must be transmitted.
More recently, Apache Hadoop YARN opened Hadoop to other data processing engines that can now run alongside existing MapReduce jobs to process data in many different ways at the same time, such as Apache Spark. YARN provides the centralized resource management that enables you to process multiple workloads simultaneously. YARN is the foundation of the new generation of Hadoop and is enabling organizations everywhere to realize a modern data architecture.
Apache Tez is an extensible framework for building high performance batch and interactive data processing applications, coordinated by YARN in Apache Hadoop. Tez improves the MapReduce paradigm by dramatically improving its speed, while maintaining MapReduce’s ability to scale to petabytes of data.
Applications can interact with the data in Hadoop using batch or interactive SQL (Apache Hive) or low-latency access with NoSQL (Apache HBase). Hive allows business users and data analysts to use their preferred business analytics, reporting and visualization tools with Hadoop. Data stored in HDFS in Hadoop can be searched using Apache Solr.
The Hadoop ecosystem extends data access and processing with powerful tools for data governance and integration including centralized security administration (Apache Ranger) and data classification tagging (Apache Atlas), which combined enable dynamic data access policies that proactively prevent data access violations from occurring. Hadoop perimeter security is also available to integrate with existing enterprise security systems and control user access to Hadoop (Apache Knox).
Introduction R is a popular tool for statistics and data analysis. It has rich visualization capabilities and a large collection of libraries that have been developed and maintained by the R developer community. One drawback to R is that it’s designed to run on in-memory data, which makes it unsuitable for large datasets. Spark is […]
A very common request from many customers is to be able to index text in image files; for example, text in scanned PNG files. In this tutorial we are going to walkthrough how to do this with SOLR. Prerequisites Download the Hortonworks Sandbox Complete the Learning the Ropes of the HDP Sandbox tutorial. Step-by-step guide […]
Introduction The Azure cloud infrastructure has become a common place for users to deploy virtual machines on the cloud due to its flexibility, ease of deployment, and cost benefits. Microsoft has expanded Azure to include a marketplace with thousands of certified, open source, and community software applications and developer services, pre-configured for Microsoft Azure. This […]
Introduction Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs in Scala, Java, Python, and R that allow developers to execute a variety of data intensive workloads. In this tutorial, we will use an Apache Zeppelin notebook for our development environment to keep things simple and elegant. Zeppelin will […]
In this tutorial we will explore how you can use policies in HDP Advanced Security to protect your enterprise data lake and audit access by users to resources on HDFS, Hive and HBase from a centralized HDP Security Administration Console.
This tutorial will cover the core concepts of Storm and the role it plays in an environment where real-time, low-latency and distributed data processing is important.
Introduction Apache Ranger delivers a comprehensive approach to security for a Hadoop cluster. It provides a central security policy administration across the core enterprise security requirements of authorization, accounting and data protection. Apache Ranger already extends baseline features for coordinated enforcement across Hadoop workloads from batch, interactive SQL and real–time in Hadoop. In this tutorial, […]
The San Jose DataWorks Summit (June 13-15) is just a few weeks away! We’re busy finalizing the lineup of an impressive array of speakers and business use cases. This year our Data Processing & Warehouse Track will feature Daniel Sumners, IT Architect at CenterPoint Energy. CenterPoint Energy is a Fortune 500 electric and gas utility company operating in several […]
Simon Meredith, Chief Technology Officer – CSI, IBM Europe explains the significance of IBM & Hortonworks working together in the era of Big Data What is fuelling IBM’s commitment to Apache Hadoop and Spark? The pressures of day to day business are delaying companies doing more with their data. IBM’s commitment is to initiate, simplify […]
Danske Bank, headquartered in Copenhagen, is the largest bank in Denmark. It’s also one of the major retail banks in the northern European region, with over 5 million retail customers. Data is mission critical to Danske Bank as it provides them with actionable intelligence to help minimize risk and maximize opportunities. In our latest video, […]
Destination Autonomous The march towards autonomous vehicles continues to accelerate. While expert opinion differs on the specific timing and use cases that will emerge first, few deny that self-driving cars are in our future. Not surprisingly, when reviewing Big Data strategies with my automotive clients, discussions on data management strategies for autonomous driving research inevitably […]
Clearsense, based in Jacksonville, Florida, develops cloud-based applications based upon Hortonworks 100% open-source Connected Data Platforms. Its customers are hospitals and health systems, and its mission is to save people’s lives by giving providers and medical practitioners advanced notice of a patient’s deteriorating health. Clearsense’s flagship product, Inception, is “designed specifically for the needs of […]
Thank you for reading our Data Lake 3.0 series! In part 1 of the series, we introduced what a Data Lake 3.0 is. In part 2 of the series, we talked about how a multi-colored YARN will play a critical role in building a successful Data Lake 3.0. In part 3 of the series, […]
Carolinas HealthCare System is one of the leading healthcare organizations in the Southeast and one of the most comprehensive, not-for-profit systems in the country. Our more than 900 care locations include: Academic medical centers Hospitals Freestanding emergency departments Healthcare pavilions Physician practices Outpatient surgical centers Laboratories Rehabilitation centers Home health agencies Nursing homes Hospice and […]
With the San Jose DataWorks Summit (June 13-15) just two months away, we’re busy finalizing the lineup of an impressive array of speakers and business use cases. This year our Enterprise Adoption Track will feature Jay Etchings, Director of Operations for Research Computing at Arizona State University. In February we announced Jay’s new book, “Strategies in Biomedical Data […]
Apache Spark is a powerful framework for data processing and analysis. Spark provides two modes for data exploration: Interactive: provided by spark-shell, pySpark, and SparkR REPLs Batch: using spark-submit to submit a Spark application to cluster without interaction in the middle of run-time. While these two modes look different on the surface, deep down they […]
You have heard about Big Data for a long time, and how companies that use Big Data as part of their business decision making process experience significantly higher profitability than their competition. Now that your company is ready to embark on its first Apache Hadoop® journey there are important lessons to be learned. Read on […]
Hive View 2.0 is New in Apache Ambari 2.5 Ambari’s Hive View gives analysts and DBAs a convenient web interface to Apache Hive which allows SQL analytics, data management and performance diagnostics. Ambari 2.5 introduces Hive View 2.0 with a brand new user experience plus a slew of great new tools to help DBAs run […]
Danske Bank, headquartered in Copenhagen, is the largest bank in Denmark. It’s also one of the major retail banks in the northern European region, with over 5 million retail customers. Danske Bank is leveraging Hortonworks for actionable intelligence to help minimize risk and maximize opportunities. Three weeks ago, at the DataWorks Summit in Munich, we announced […]
Three weeks ago, at the DataWorks Summit in Munich, we announced the Data Hero winners for the EMEA region. The winner in the Data Visionary category was Daljit Rehal, Global Director, Digital & Data Services at Centrica. You can read the announcement here. Centrica supplies energy and energy-related services to around 28 million customer accounts in […]
Andrew Ng, the renowned data scientist, has said that artificial intelligence (AI) needs to be a company-wide strategic decision. Companies that don’t strategically invest in AI will slowly lose market share to companies whose core businesses are built around AI. AI enables the prediction, planning and automation of a variety of tasks, and for enterprises, […]
R is one of the primary programming languages for data science with more than 10,000 packages. R is an open source software that is widely taught in colleges and universities as part of statistics and computer science curriculum. R uses data frame as the API which makes data manipulation convenient. R has powerful visualization infrastructure, […]
Apache Hadoop has always been associated with storing & processing vasts amount of data. But did you know it’s also an awesome engine to power interactive data exploration and visualization? With the development of Apache Hive LLAP (a recent innovation included in the Hortonworks Data Platform), you can use Hadoop with Business Intelligence tools (like […]
OPEN SOURCE HADOOP NOW RUNS ON AN OPEN COMPUTE PLATFORM The software market is undergoing a major transition, moving away from proprietary software that leads to customer lock-in. Open source software offers freedom, more flexibility, and faster innovation – all at a lower cost. With the release of HDP 2.6 now available on IBM Power Systems, […]
HDP 2.6 takes a huge step forward toward true data management by introducing SQL-standard ACID Merge to Apache Hive. As scalable as Apache Hadoop is, many workloads don’t work well in the Hadoop environment because they need frequent or unpredictable updates. Updates using hand-written Apache Hive or Apache Spark jobs are extremely complex. Not only […]
We are thrilled to announce that Hortonworks Data Platform (HDP) version 2.6 is now available – both on pre-premise and in the cloud. For the first time, we are also making this available on IBM Power System in addition to the x86 chipset. During 2016, we have seen many of Hortonworks’ customers deploy more and […]
Human Assisted AI Another common trend is pairing humans to evaluate results from Artificial Intelligence (AI). As great and sensational AI has been made out to be recently, it is still long way from having human-like abilities of comprehension, reasoning and intuition. For instance, in radiology, given lymph node cells, AI alone had 7.5 percent […]
Large-scale Machine Learning The ability to learn without being explicitly programmed, Machine Learning, has been around for a long time and is well understood. What is different is the relatively recent emergence of general purpose tools, such as Apache Spark, that enable processing of very large datasets. Additionally, data scientists can now collaborate and rapidly […]
One of the best parts about my job is learning how Big Data drives the world around us. I’m continually awed by the plethora of transformative customer stories and Big Data use cases across every industry. Take for instance Soleo Communications. Soleo bridges the space between the world of telephony and the world of Modern […]
Hortonworks continues to expand its list of customers in the Asia Pacific region, as well as in the housing and building industry. We recently completed a case study to showcase how LIXIL Corporation uses HDP to be first in manufacturing for the Japanese Smart Home Market. READ THE FULL LIXIL CASE STUDY HERE LIXIL is a […]
Did you know every Hortonworks HDP support subscription comes with SmartSense? Advanced Analytics of Diagnostic Data Prevents Issues SmartSense uses advanced analytics to make suggestions and recommendations based on the deep knowledge of our Hortonworks engineers and committers to prevent issues and improve performance of your HDP cluster. Based on the diagnostic data collected from […]
Thank you for reading our Data Lake 3.0 series! In part 1 of the series, we introduced what a Data Lake 3.0 is. In part 2 of the series, we talked about how a multi-colored YARN will play a critical role in building a successful Data Lake 3.0. In part 3 of the series, we […]
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