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In an era where artificial intelligence (AI) is reshaping enterprises across the globe—be it in healthcare, finance, or manufacturing—it’s hard to overstate the transformation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to […] Read blog post
Enterprises see embracing AI as a strategic imperative that will enable them to stay relevant in increasingly competitive markets. However, it remains difficult to quickly build these capabilities given the challenges with finding readily available talent and resources to get started rapidly on the AI journey. Cloudera recently signed a strategic collaboration agreement with Amazon […] Read blog p
Untangling Apache Hadoop YARN, Part 3: Scheduler Concepts In Parts 1 and 2, we covered the basics of YARN resource allocation. In this installment, we’ll provide an overview of cluster scheduling and introduce the Fair Scheduler, one of the scheduler choices available in YARN. A standalone computer can have several CPU cores, each running a single process, but there can be as many as a few hundred
HDFS is core part of any Hadoop deployment and in order to ensure that data is protected in Hadoop platform, security needs to be baked into the HDFS layer. HDFS is protected using Kerberos authentication, and authorization using POSIX style permissions/HDFS ACLs or using Apache Ranger. Apache Ranger (https://hwxjojo.staging.wpengine.com/hadoop/ranger/) is a centralized security administration sol
This is the fourth post in a series that explores the theme of enabling diverse workloads in YARN. See the introductory post to understand the context around all the new features for diverse workloads as part of YARN in HDP 2.2. Introduction When it comes to managing resources in YARN, there are two aspects that we, the YARN platform developers, are primarily concerned with: Resource allocation: A
Untangling Apache Hadoop YARN, Part 1: Cluster and YARN Basics In this multipart series, fully explore the tangled ball of thread that is YARN. YARN (Yet Another Resource Negotiator) is the resource management layer for the Apache Hadoop ecosystem. YARN has been available for several releases, but many users still have fundamental questions about what YARN is, what it’s for, and how it works. This
This post contains answers to common questions about deploying and configuring Apache Kafka as part of a Cloudera-powered enterprise data hub. Cloudera added support for Apache Kafka, the open standard for streaming data, in February 2015 after its brief incubation period in Cloudera Labs. Apache Kafka now is an integrated part of CDH, manageable via Cloudera Manager, and we are witnessing rapid a
Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop Evaluating which streaming architectural pattern is the best match to your use case is a precondition for a successful production deployment. The Apache Hadoop ecosystem has become a preferred platform for enterprises seeking to process and understand large-scale data in real time. Technologies like Apache Kafka, Apache F
How resource tuning, parallelism, and data representation affect Spark 1.3 job performance. Editor’s Note, January 2021: This blog post remains for historical interest only. It covers Spark 1.3, a version that has become obsolete since the article was published in 2015. For a modern take on the subject, be sure to read our recent post on Apache Spark 3.0 performance. You can also gain practical, h
Editor’s Note, January 2021: This blog post remains for historical interest only. It covers Spark 1.3, a version that has become obsolete since the article was published in 2015. For a modern take on the subject, be sure to read our recent post on Apache Spark 3.0 performance. You can also gain practical, hands-on experience by signing up for Cloudera’s Apache Spark Application Performance Tuning
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop. In the conclusion to this two-part post, pipeline recovery is explained. An important design requirement of HDFS is to ensure continuous and correct operations that support production deployments. For that reason, it’s important for operators to understand how HDFS recovery pro
How-to: Do Real-Time Log Analytics with Apache Kafka, Cloudera Search, and Hue Cloudera recently announced formal support for Apache Kafka. This simple use case illustrates how to make web log analysis, powered in part by Kafka, one of your first steps in a pervasive analytics journey. If you are not looking at your company’s operational logs, then you are at a competitive disadvantage in your ind
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop. An important design requirement of HDFS is to ensure continuous and correct operations to support production deployments. One particularly complex area is ensuring correctness of writes to HDFS in the presence of network and node failures, where the lease recovery, block recove
The new integration between Flume and Kafka offers sub-second-latency event processing without the need for dedicated infrastructure. In this previous post you learned some Apache Kafka basics and explored a scenario for using Kafka in an online application. This post takes you a step further and highlights the integration of Kafka with Apache Hadoop, demonstrating both a basic ingestion capabilit
The ongoing progress in Artificial Intelligence is constantly expanding the realms of possibility, revolutionizing industries and societies on a global scale. The release of LLMs surged by 136% in 2023 compared to 2022, and this upward trend is projected to continue in 2024. Today, 44% of organizations are experimenting with generative AI, with 10% having […] Read blog post
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