Cloudera News

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  Cloudera Fast Forward Labs Quarterly Updates – July 2019
  • sorangutan  

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  Cloudera Fast Forward Labs is an applied machine learning research and consulting services group within Cloudera, which helps enterprises accelerate data value creation through the adoption of emerging ML techniques, cutting-edge technical architectures and industry leading ML best practices. We focus on expert knowledge transfer and skills development, empowering organizations to continually evolve, differentiate themselves and ultimately own the future of their business by leveraging open technologies and data. Enabling ethical and responsible ML outcomes to our customers,

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  The post Cloudera Fast Forward Labs Quarterly Updates – July 2019 appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/07/cloudera-fast-forward-labs-quarterly-updates-july-2019/
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  Cloudera at ACM SIGMOD/PODS 2019
  • sorangutan  

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  The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. This year ACM SIGMOD/PODS will be held in Amsterdam, The Netherlands on June 30th – July 5th, 2019, and Cloudera will be present in the conference, contributing to and learning from the broader research community.

  Last year,

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  The post Cloudera at ACM SIGMOD/PODS 2019 appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/06/cloudera-at-acm-sigmod-pods-2019/
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  Putting Machine Learning Models into Production
  • sorangutan  

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  Once the data science is done (and you know where your data comes from, what it looks like, and what it can predict) comes the next big step: you now have to put your model into production and make it useful for the rest of the business. This is the start of the model operations life cycle. The key focus areas (detailed in the diagram below) are usually managed by machine learning engineers after the data scientists have done their work.

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  The post Putting Machine Learning Models into Production appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/06/putting-machine-learning-models-into-production/
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  HDFS Erasure Coding in Production
  • sorangutan  

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  HDFS erasure coding (EC), a major feature delivered in Apache Hadoop 3.0, is also available in CDH 6.1 for use in certain applications like Spark, Hive, and MapReduce. The development of EC has been a long collaborative effort across the wider Hadoop community. Including EC with CDH 6.1 helps customers adopt this new feature by adding Cloudera’s first-class enterprise support.

  While previous versions of HDFS achieved fault tolerance by replicating multiple copies of data (similar to RAID1 on traditional storage arrays),

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  The post HDFS Erasure Coding in Production appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/06/hdfs-erasure-coding-in-production/
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  Visual Model Interpretability for Telco Churn in Cloudera Data Science Workbench
  • sorangutan  

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  Disclaimer: the scenario below is hypothetical.   Any similarity to any specific telecommunications company is purely coincidental.  

  Although we use the example of a telecommunications company the following applies to every organization with customers or voluntary stakeholders.  

  Introduction

  Imagine that you are a Chief Data Officer at a major telecommunications provider and the CEO has asked you to overhaul the existing customer churn analytics.  The current process relies on manual export of data from dozens of data sources including ERP,

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  The post Visual Model Interpretability for Telco Churn in Cloudera Data Science Workbench appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/06/visual-model-interpretability-for-telco-churn-in-cloudera-data-science-workbench/
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  CDH 6.2 Release: What’s new in HBase
  • sorangutan  

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  Cloudera recently launched CDH 6.2 which includes two new key features in Apache HBase:

  1. Serial replication
  2. Bucket cache now supports Intel’s Optane memory

  Serial replication

  HBase has a sophisticated asynchronous replication mechanism that supports complex topologies today that include global round-robin, two way, span-in and span-out topologies.

  

  This replication capability, to date, provides eventual consistency — meaning that the order in which updates are replicated is not necessarily the same as the order in which they were applied to the database.  

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  The post CDH 6.2 Release: What’s new in HBase appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/06/cdh-6-2-release-whats-new-in-hbase/
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  CDH6.2 – Cloudera Search Attribute Based Access Control Part 2
  • sorangutan  

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  Cloudera Search is a highly scalable and flexible search solution based on Apache Solr which enables exploration, discovery and analytics over massive, unstructured and semi-structured datasets (for example logs, emails, dna-strings, claims forms, jpegs, xls sheets, etc). It has been adopted by a large number of Cloudera customers across a wide range of industries for high ROI and SLA-bound workloads, with many of those having strict requirements around security and compliance.

  In CDH6.2 we introduce two new features to Cloudera Search relating to document-level security.

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  The post CDH6.2 – Cloudera Search Attribute Based Access Control Part 2 appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/06/cdh6-2-cloudera-search-attribute-based-access-control-part-2/
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  CDH6.2 – Cloudera Search Attribute Based Access Control Part 1
  • sorangutan  

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  Cloudera Search is a highly scalable and flexible search solution based on Apache Solr which enables exploration, discovery and analytics over massive, unstructured and semi-structured datasets (for example logs, emails, dna-strings, claims forms, jpegs, xls sheets, etc). It has been adopted by a large number of Cloudera customers across a wide range of industries for high ROI and SLA-bound workloads, with many of those having strict requirements around security and compliance.

  In CDH 5.1.0 we introduced document-level security for Cloudera Search using Apache Sentry.

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  The post CDH6.2 – Cloudera Search Attribute Based Access Control Part 1 appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/05/cdh6-2-cloudera-search-attribute-based-access-control-part-1/
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  The Case for MirrorMaker 2
  • sorangutan  

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  Apache Kafka has become an essential component of enterprise data pipelines and is used for tracking clickstream event data, collecting logs, gathering metrics, and being the enterprise data bus in a microservices based architectures. Kafka is essentially a highly available and highly scalable distributed log of all the messages flowing in an enterprise data pipeline. Kafka supports internal replication to support data availability within a cluster. However, enterprises require that the data availability and durability guarantees span entire cluster and site failures.

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  The post The Case for MirrorMaker 2 appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/05/the-case-for-mirrormaker-2/
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  Small Files, Big Foils: Addressing the Associated Metadata and Application Challenges
  • sorangutan  

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  Small files are a common challenge in the Apache Hadoop world and when not handled with care, they can lead to a number of complications. The Apache Hadoop Distributed File System (HDFS) was developed to store and process large data sets over the range of terabytes and petabytes. However, HDFS stores small files inefficiently, leading to inefficient Namenode memory utilization and RPC calls, block scanning throughput degradation, and reduced application layer performance. In this blog post,

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  The post Small Files, Big Foils: Addressing the Associated Metadata and Application Challenges appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/05/small-files-big-foils-addressing-the-associated-metadata-and-application-challenges/
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  Partition Management in Hadoop
  • sorangutan  

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  Guest blog post written by Adir Mashiach

  In this post I’ll talk about the problem of Hive tables with a lot of small partitions and files and describe my solution in details.

  

  A little background

  In my organization,  we keep a lot of our data in HDFS. Most of it is the raw data but a significant amount is the final product of many data enrichment processes.

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  The post Partition Management in Hadoop appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/05/partition-management-in-hadoop/
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  Fine-Grained Authorization with Apache Kudu and Impala
  • sorangutan  

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  Apache Impala supports fine-grained authorization via Apache Sentry on all of the tables it manages including Apache Kudu tables. Given Impala is a very common way to access the data stored in Kudu, this capability allows users deploying Impala and Kudu to fully secure the Kudu data in multi-tenant clusters even though Kudu does not yet have native fine-grained authorization of its own. This solution works because Kudu natively supports coarse-grained (all or nothing) authorization which enables blocking all access to Kudu directly except for the impala user and an optional whitelist of other trusted users.

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  The post Fine-Grained Authorization with Apache Kudu and Impala appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/04/fine-grained-authorization-with-apache-kudu-and-impala/
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  Demystifying Spark Jobs to Optimize for Cost and Performance
  • sorangutan  

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  Apache Spark is one of the most popular engines for distributed data processing on Big Data clusters. Spark jobs come in all shapes, sizes and cluster form factors. Ranging from 10’s to 1000’s of nodes and executors, seconds to hours or even days for job duration, megabytes to petabytes of data and simple data scans to complicated analytical workloads. Throw in a growing number of streaming workloads to huge body of batch and machine learning jobs —

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  The post Demystifying Spark Jobs to Optimize for Cost and Performance appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/04/demystifying-spark-jobs-to-optimize-for-cost-and-performance/
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  A Guide to Learning with Limited Labeled Data
  • sorangutan  

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  This was originally published on the Fast Forward Labs blog

  We are excited to release Learning with Limited Labeled Data, the latest report and prototype from Cloudera Fast Forward Labs.

  Being able to learn with limited labeled data relaxes the stringent labeled data requirement for supervised machine learning. Our report focuses on active learning, a technique that relies on collaboration between machines and humans to label smartly.

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  The post A Guide to Learning with Limited Labeled Data appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/04/a-guide-to-learning-with-limited-labeled-data/
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  What’s New in Cloudera Altus Director 6.2?
  • sorangutan  

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  Cloudera Altus Director helps you deploy, scale, and manage Cloudera clusters on AWS, Microsoft Azure, or Google Cloud Platform. Altus Director both enables and enforces the best practices of big data deployments and cloud infrastructure. Altus Director’s enterprise-grade features deliver a mechanism for establishing production-ready clusters in the cloud for big data workloads and applications in a simple, reliable, automated fashion. In this post, you will learn about new functionality and changes in release 6.2.

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  The post What’s New in Cloudera Altus Director 6.2? appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/04/whats-new-in-cloudera-altus-director-6-2/
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  What’s new in the Hue Data Warehouse Editor in Cloudera 6.2
  • sorangutan  

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  Self-service exploratory analytics is one of the most common use cases we see by our customers running on Cloudera’s Data Warehouse solution.

  With the recent release of Cloudera 6.2, we continue to improve the end user query experience with Hue, focusing on easier SQL query troubleshooting and increased compatibility with Hive. Read on to learn more and try it out in one-click at demo.gethue.com.

  Easier SelfService Query Troubleshooting

  Hue has great assistance for finding tables in the Data Catalog and getting recommendations on how to write (better) queries with the smart autocomplete,

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  The post What’s new in the Hue Data Warehouse Editor in Cloudera 6.2 appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/04/whats-new-in-the-hue-data-warehouse-editor-in-cloudera-6-2/
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  Testing Apache Kudu Applications on the JVM
  • sorangutan  

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  Although the Kudu server is written in C++ for performance and efficiency, developers can write client applications in C++, Java, or Python. To make it easier for Java developers to create reliable client applications, we’ve added new utilities in Kudu 1.9.0 that allow you to write tests using a Kudu cluster without needing to build Kudu yourself, without any knowledge of C++, and without any complicated coordination around starting and stopping Kudu clusters for each test.

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  The post Testing Apache Kudu Applications on the JVM appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/03/testing-apache-kudu-applications-on-the-jvm/
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  Transparent Hierarchical Storage Management with Apache Kudu and Impala
  • sorangutan  

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  When picking a storage option for an application it is common to pick a single storage option which has the most applicable features to your use case. For mutability and real-time analytics workloads you may want to use Apache Kudu, but for massive scalability at a low cost you may want to use HDFS. For that reason, there is a need for a solution that allows you to leverage the best features of multiple storage options.

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  The post Transparent Hierarchical Storage Management with Apache Kudu and Impala appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/03/transparent-hierarchical-storage-management-with-apache-kudu-and-impala/
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  SMM 1.2 Released with Powerful New Alerting and Topic Lifecycle Management Features with Schema Registry Integration
  • sorangutan  

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  [Editor’s note: Now that the recent merger is complete, the Cloudera Engineering blog will expand to cover products, such as this, originally developed for the Hortonworks platform. Please stay tuned for future product announcements regarding availability of these products on the Cloudera platform.]

  Since the release of Streams Messaging Manager (SMM) at the end of last summer, our customers have started to cure the Kafka Blindness within their organizations by using SMM to monitor their Kafka clusters and streaming microservices applications.

  Read more

  The post SMM 1.2 Released with Powerful New Alerting and Topic Lifecycle Management Features with Schema Registry Integration appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/02/new-kafka-alert-management-features-in-smm-12/
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  Enable MKL Library for Spark ML Applications: Methods and Benchmarks
  • sorangutan  

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  Spark ML is one of the dominant frameworks for many major machine learning algorithms, such as the Alternating Least Squares (ALS) algorithm for recommendation systems, the Principal Component Analysis algorithm, and the Random Forest algorithm. However, the complexity of configuring it optimally  means that frequently, Spark ML is underutilized.. Using native math libraries for Spark ML can help unlock the full potential of Spark ML.

  This article discusses how to accelerate model training speed by using native libraries for Spark ML.

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  The post Enable MKL Library for Spark ML Applications: Methods and Benchmarks appeared first on Cloudera Engineering Blog.

  
  
  https://blog.cloudera.com/blog/2019/02/enable-mkl-library-for-spark-ml-applications-methods-and-benchmarks/