Big Data Trendz

Thanks to blog contributors from Cloudera Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compared to replication while maintaining the same durability guarantees. This post explains how it works. HDFS by default replicates each block three times. Replication provides a simple and robust form of redundancy to shield against most failure scenarios. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from. However, replication is expensive: the default 3x replication scheme incurs a 200% overhead in storage space and other resources (e.g., network bandwidth when writing the data). For datasets with relatively low I/O activity, the additional block replicas are rarely accessed during normal operations, but still consume the same amount of storage space. Therefore, a natural improvement is to use erasure coding (EC) in place of replication, which uses far less storage s...

Thanks to Carter Shanklin & Nita Dembla from Hortonworks for valuable post. One of the most exciting new features of HDP 2.6 from Hortonworks was the general availability of Apache Hive with LLAP. If you missed DataWorks Summit you’ll want to look at some of the great LLAP experiences our users shared, including Geisinger who found that  Hive LLAP outperforms their traditional EDW  for most of their queries, and Comcast who found  Hive LLAP is faster than Presto for 75% of benchmark queries . These great results are thanks to performance and stability improvements Hortonworks made to Hive LLAP resulting in 3x faster interactive query in HDP 2.6. This blog dives into the reasons HDP 2.6 is so much faster. We’ll also take a look at the massive step forward Hive has made in SQL compliance with HDP 2.6, enabling Hive to run all 99 TPC-DS queries with only trivial modifications to the original source queries. STARTING OFF: 3X PERFORMANCE GAINS IN HDP 2.6 WITH HIVE ...

Thanks to Hortonworks Team for the valuable post. In this post, we deep dive into something that we are extremely excited about – Running a container cloud on YARN! We have been using this next-generation infrastructure for more than a year in running all of the Hortonworks internal  CI / CD  infrastructure. With this, we can now run Hadoop on Hadoop to certify our releases! Let’s dive right in! CERTIFYING HORTONWORKS PLATFORMS The introductory post on Engineering @ Hortonworks gave the readers an overview of the scale of challenges we see in delivering an Enterprise Ready Data platform. Essentially, for every new release of a platform, we provision Hadoop clusters on demand, with specific configurations like authentication on/off, encryption on/off, DB combinations, and OS environments, run a bunch of tests to validate changes, and shut them down. And we do this over and over, day in and day out, throughout the year.

Thanks to Vinay Shukla( Leading Data Science Product Management at Hortonworks) Our customers increasingly leverage Data Science, and Machine Learning to solve complex predictive analytics problem. A few examples of these problems are churn prediction, predictive maintenance, image classification, and entity matching. While everyone wants to predict the future, truly leveraging Data Science for Predictive Analytics remains the domain of a select few. To expand the reach of Data Science, the Modern Data Architecture (MDA) needs to address the following 4 requirements: Enable Apps to consume predictions and become smarter Bring predictive analytics to the IOT Edge Become easier, more accurate & faster to deploy and manage Fully support  data science life cycle The below diagram represents where Data science fits in the MDA. DATA SMART APPLICATIONS The end-users consumes data, analytics and the results of Data Science analytics via data centric applications (or...

Thanks to Hortonworks Blog and   Yanbo Liang 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, which lets data scientists interpret data efficiently. However, data analysis using R is limited by the amount of memory available on a single machine and further as R is single threaded it is often impractical to use R on large datasets. To address R’s scalability issue, the Spark community developed SparkR package which is based on a distributed data frame that enables structured data processing with a syntax familiar to R users. Spark provides distributed processing engine, data source, off-memory data structures. R provides a dynamic environment, interactivity, packages, visualization. Sp...

Source: Cloudera Blog This is article about Hadoop 3.x version release from Cloudera Blog post The Apache Hadoop project recently  announced its 3.0.0-alpha1 release . Given the scope of a new major release, the Apache Hadoop community decided to release a series of alpha and beta releases leading up to 3.0.0 GA. This gives downstream applications and end users an opportunity to test and provide feedback on the changes, which can be incorporated during the alpha and beta process. The 3.0.0-alpha1 release incorporates thousands of new fixes, improvements, and features since the previous minor release, 2.7.0, which was released over a year ago. The full  changelog  and  release notes  are available on the Hadoop website, but we’d like to drill into the major new changes that landed in 3.0.0-alpha1. Disclaimer: As this release is an alpha, there are no guarantees regarding API stability or quality. The feature set and behavior are subject to change durin...

Thanks to Hortonworks blog resource --  Carter Shanklin && Nita Dembla Apache Hive(™)  is the most complete SQL on Hadoop system, supporting comprehensive SQL, a sophisticated cost-based optimizer, ACID transactions and fine-grained dynamic security. Though Hive has proven itself on multi-petabyte datasets spanning thousands of nodes many interesting use cases demand more interactive performance on smaller datasets, requiring a shift to in-memory. Hive 2 marks the beginning of Hive’s journey from a disk-centric architecture to a memory-centric architecture through  Hive LLAP (Live Long and Process). Since memory costs about 100x as much as disk, memory-centric architectures demand a careful design that makes the most of available resources. In this blog, we’ll update benchmark results from our earlier blog, “ Announcing Apache Hive 2.1: 25x Faster Queries and Much More. ” NOT ALL MEMORY ARCHITECTURES ARE EQUAL