Lei Feng 的 Blog

For the things we have to learn before we can do them, we learn by doing them. --Aristotle

Install, Setup and Manage Google Cloud SDK to Use Python From Anaconda

07 Jun 2020 | Google Cloud Platform | Cloud SDK

As a power user of Google Cloud Platform, you definately need to use gcloud, gsutil and bq commands to work with GCP, which means you need to install Google Cloud SDK on your local computer. You can install the Cloud SDK through many options, including versioned archives, installer, apt-get/yum for Linux distro, and even Docker image. This post describes the process of installing the Cloud SDK through versioned archive on operating systems that have already installed Python through Anaconda. The process has been tested on both Windows 10 and Ubuntu 18.04. Read More...

Setup Kubeflow Cluster in a Shared VPC on Google Cloud Platform

14 Mar 2020 | Google Cloud Platform | Kubeflow

Kubeflow is an open-source project which aims to make running ML workloads on Kubernetes simple, portable and scalable. However, setting up a Kubeflow cluster in a shared VPC on Google Cloud Platform can not be done through the web console yet. This post tries to describe the steps you need to follow to set up a Kubeflow using a Shared VPC through command line. Read More...

Run Dataflow Jobs in a Shared VPC without Regional Endpoints on GCP

14 Jan 2020 | Google Cloud Platform | Dataflow

Google Cloud Dataflow is a fully managed platform running Apache Beam for unified stream and batch data processing services. With its fully managed approach on resource provisioning and horizontal autoscaling, you have access to virtually unlimited capacity and optimized price-to-performance to solve your data pipeline processing challenges. Read More...

Install Hortonworks HDP 3.1.0 on A Cluster of VMWare Virtual Machines

Hortonworks Tutorials

19 Mar 2019 | Tutorial | Hadoop • Big Data

This post describes the process to install Hortontworks HDP 3.1.0 on a cluster of three VMWare virtual machines. The process includes four major steps: 1) set up the cluster environemnt; 2) set up a local repository for both Ambari and HDP stacks; 3) Install Ambari server and agent; 4) install, configure and deploy the cluster. This installation process might work for other versions too. Please check the product versions through Hortonworks support matrix: https://supportmatrix.hortonworks.com/ Read More...

Set Up Scala Development Environment for Apache Spark in Standalone Mode

Apache Spark Tutorials

17 Apr 2018 | Tutorial | Apache Spark

With the Apache Spark installed through the steps described in last post, this post will introduce you the steps to set up a Scala development environment for Spark and build a WordCount application through Maven and SBT. Althrough Spark can be programmed with either Java, Scala, or Python, this post will focus on Scala. There are a couple of reasons: 1) Spark itself is written in Scala; 2) Scala’s functional programming model is a good fit for distributed processing, thus has less code and boilerplate stuff than Java; 3) Scala compiles to Java bytecode, which gives faster performance than Python. Read More...