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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. In order to manage all the data pipelines
In Part 1: Infrastructure Considerations in this three part revamped series on deploying clusters like a boss, we provided a general explanation for how nodes are classified, disk layout configurations and network topologies to think about when deploying your clusters. In this Part 2: Service and Role Layouts segment of the series, we take a step higher up the stack looking at the various services
One of the most fundamental aspects a data model can convey is how something changes over time. This makes sense when considering that we build data models to capture what is happening in the real world, and the real world is constantly changing. The challenge is that it’s not just that new things are occurring, it’s that existing things are changing too, and if in our data models we overwrite the
Synopsis This article introduces a new Apache Hadoop feature called S3Guard. S3Guard addresses one of the major challenges with running Hadoop on Amazon’s Simple Storage Service (S3), eventual consistency. We outline the problem of S3’s eventual consistency, how it affects Hadoop workloads, and explain how S3Guard works. Problem Although Apache Hadoop has support for using Amazon Simple Storage Se
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
Create conda recipe to use C extended Python library on PySpark cluster with Cloudera Data Science Workbench Cloudera Data Science Workbench provides data scientists with secure access to enterprise data with Python, R, and Scala. In the previous article, we introduced how to use your favorite Python libraries on an Apache Spark cluster with PySpark. In Python world, data scientists often want to
Deep Learning Frameworks on CDH and Cloudera Data Science Workbench The emergence of “Big Data” has made machine learning much easier because the key burden of statistical estimation—generalizing well to new data after observing only a small amount of data—has been considerably lightened. In a typical machine learning task, the goal is to design the features to separate the factors of variation th
Cloudera Data Science Workbench: Self-Service Data Science for the Enterprise We are entering the golden age of machine learning, and it’s all about the data. As the quantity of data grows and the costs of compute and storage continue to drop, the opportunity to solve the world’s biggest problems has never been greater. Our customers already use advanced machine learning to build self-driving cars
We are thrilled to announce the general availability of the Cloudera AI Inference service, powered by NVIDIA NIM microservices, part of the NVIDIA AI Enterprise platform, to accelerate generative AI deployments for enterprises. This service supports a range of optimized AI models, enabling seamless and scalable AI inference. Background The generative AI landscape is evolving […] Read blog post
Analyzing US flight data on Amazon S3 with sparklyr and Apache Spark 2.0 We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. sparklyr, developed by RStudio, is an R interface to Spark that allows users to use Spark as the backend for dplyr, which is the popular data manipulation package for R. If y
How-to: Use the New HDFS Intra-DataNode Disk Balancer in Apache Hadoop HDFS now includes (shipping in CDH 5.8.2 and later) a comprehensive storage capacity-management approach for moving data across nodes. In HDFS, the DataNode spreads the data blocks into local filesystem directories, which can be specified using dfs.datanode.data.dir in hdfs-site.xml. In a typical installation, each directory, c
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 4: Fair Scheduler Queue Basics In this installment, we provide insight into how the Fair Scheduler works, and why it works the way it does. In Part 3 of this series, you got a quick introduction to Fair Scheduler, one of the scheduler choices in Apache Hadoop YARN (and the one recommended by Cloudera). In Part 4, we will cover most of the queue properties, some
Better SLAs via Resource-preemption in YARN’s CapacityScheduler Mayank Bansal, of EBay, is a guest contributing author of this collaborative blog. This is the 4th 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 Apache Hadoop YARN in HDP. Background In Had
Introducing Apache Arrow: A Fast, Interoperable In-Memory Columnar Data Structure Standard Engineers from across the Apache Hadoop community are collaborating to establish Arrow as a de-facto standard for columnar in-memory processing and interchange. Here’s how it works. Apache Arrow is an in-memory data structure specification for use by engineers building data systems. It has several key benefi
Enabling Python development on CDH clusters (for PySpark, for example) is now much easier thanks to new integration with Continuum Analytics’ Python platform (Anaconda). Python has become an increasingly popular tool for data analysis, including data processing, feature engineering, machine learning, and visualization. Data scientists and data engineers enjoy Python’s rich numerical and analytical
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 st
Untangling Apache Hadoop YARN, Part 2: Global Configuration Basics A new installment in the series about the tangled ball of thread that is YARN In Part 1 of this series, we covered the fundamentals of clusters of YARN. In Part 2, you’ll learn about other components than can run on a cluster and how they affect YARN cluster configuration. Ideal YARN Allocation As shown in the previous post, a YARN
The SparkOnHBase project in Cloudera Labs was recently merged into the Apache HBase trunk. In this post, learn the project’s history and what the future looks like for the new HBase-Spark module. SparkOnHBase was first pushed to Github on July 2014, just six months after Spark Summit 2013 and five months after Apache Spark first shipped in CDH. That conference was a big turning point for me, becau
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