Category Archives: Spark

Reading data securely from Apache Kafka to Apache Spark

Categories: CDH Kafka Platform Security & Cybersecurity Sentry Spark

Introduction

With an ever-increasing number of IoT use cases on the CDH platform, security for such workloads is of paramount importance. This blog post describes how one can consume data from Kafka in Spark, two critical components for IoT use cases, in a secure manner.

The Cloudera Distribution of Apache Kafka 2.0.0 (based on Apache Kafka 0.9.0) introduced a new Kafka consumer API that allowed consumers to read data from a secure Kafka cluster.

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Create conda recipe to use C extended Python library on PySpark cluster with Cloudera Data Science Workbench

Categories: CDH Data Science How-to Spark

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 use Python libraries, such as XGBoost, which includes C/C++ extension. This post shows how to solve this problem creating a conda recipe with C extension.

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The Benefits of Migrating HPC Workloads To Apache Spark

Categories: CDH Data Science Hadoop Spark

Overview

Recently we worked with a customer that needed to run a very significant amount of models in a given day to satisfy internal and government regulated risk requirements.  Several thousand model executions would need to be supported per hour.  Total execution time was very important to this client.  In the past the customer used thousands of servers to meet the demand.  They need to run many derivations of this model with different economic factors to satisfy their requirements.   

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Hail: Scalable Genomics Analysis with Apache Spark

Categories: CDH Data Science Spark

Technology-focused discussions about genomics usually highlight the huge growth in DNA sequencing since the beginning of the century, growth that has outpaced Moore’s law and resulted in the $1000 genome. However, future growth is projected to be even more dramatic. In the paper “Big Data: Astronomical or Genomical?”, the authors say it is estimated that “between 100 million and as many as 2 billion human genomes could be sequenced by 2025”,

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BigDL on CDH and Cloudera Data Science Workbench

Categories: CDH How-to Spark

Introduction

As companies strive to implement modern solutions based on deep learning frameworks, there is a need to deploy it on existing hardware infrastructure in a scalable and distributed manner comes to the fore. Recognizing this need, Cloudera’s and Intel’s Big Data Technologies engineering teams jointly detail Intel’s BigDL Apache Spark deep learning library on the latest release of Cloudera’s Data Science Workbench. This collaborative effort allows customers to build new deep learning applications with BigDL Spark Library by leveraging their existing homogeneous compute capacity of Xeon servers running Cloudera’s Enterprise without having to invest in expensive GPU farms and bringing up parallel frameworks such as TensorFlow or Caffe.

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