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In the hopes of creating a "blob" like storage like Amazon S3 , I recently did a Google for open source blob storage.  To my pleasure, I discovered minio .  Minio allows me to expose an S3 compatible service locally on my home network.  I can now work with large datasets in a S3-like fashion locally without having the overhead of dealing with an Internet connection. I can also set up Minio to be a gateway to Amazon S3 or even to my local Hadoop cluster . I also am able to set up the AWS CLI to interact with minio or have the minio client interact with AWS S3. While redundancy can be implemented with minio, I'll save that as a project for later. I picked up a Raspberry Pi 4 8 GB model from Amazon ($150) and a 8 TB external USB drive from Costco ($120).  One can always step down to a lower model / storage space if needed - just couldn't resist the savings on an 8 TB drive from Costco. :) I downloaded the Raspbian Lite image , set up regionalization and my hostname.

Home built Hadoop analytics cluster: Part 4 So yay!  As mentioned for my next goals in my previous post , I finally got the remaining two boxes built out and added into my home network.  I opted to put the Hadoop cluster on it's own subnet with a dedicated unmanaged switch only for the cluster (primary and nodes). I added the agent and metrics to all of the nodes and rebooted the servers. Then I followed the instructions to set up the cluster, naming it "ds730" after the class that I'm currently taking - DS730: Big Data - High Performance Computing. I also made sure I had DNS setup correctly by modifying /etc/systemd/resolved.conf and fixed my name resolution issues. Removed firewall rules. Removed timedatectl by doing: sudo timedatectl set-ntp no Then installed ntp: sudo apt install ntp Now I need to look at installing some database drivers, however I think I'm going to call it a night.  

Home built Hadoop analytics cluster: Part 3 In my previous post I covered Bill of Materials (BOM), hardware assembly, and installing Linux (Ubuntu).  In this post I will cover how I installed Ambari. Installing Ambari Rather than build from source, I opted to use the distribution from Cloudera (formerly HortonWorks). Ambari 2.7.5 requires official support from Cloudera, so I went down to 2.7.3 which doesn't require a support agreement with Cloudera. Install some pre-requisites sudo apt install python-dev sudo apt install gcc Add Cloudera as a distribution sudo wget -O /etc/apt/sources.list.d/ambari.list http://public-repo-1.hortonworks.com/ambari/ubuntu18/2.x/updates/2.7.3.0/ambari.list sudo apt-key adv --recv-keys --keyserver keyserver.ubuntu.com B9733A7A07513CAD sudo apt-get update Verify packages show up apt-cache showpkg ambari-server apt-cache showpkg ambari-agent apt-cache showpkg ambari-metrics-assembly Install and setup the server on primary sudo apt-get install ambari-se

So, for some reason some recent updates to Ubuntu decided to cause the resolution of my monitor to go down. So I tried fixing it doing some updates, and lo and behold, nothing worked. Then on top of that, my virtual machine that I was using for my DS730 class decided to go belly up and didn't come back after 2 reboots. Not having a good day. So, now I'm going to document the installation of my workstation using a blog post so I have some documentation to fall back onto when things go haywire again.  It looks like I'm going to reinstall Ubuntu again. Then I need to hope that rebooting the virtual machine one last time will work.

Home built Hadoop analytics cluster: Part 2 In my previous post , I went through my overall plan that I will be following, along with goals and topics that I will be covering.  In this post, I will cover the initial building out of the cluster. [ Bill of Materials - BOM ] [ Hardware assembly ] [ Installing and configuring Linux ] Bill of Materials - BOM Item Price Link CPU $139.99 Amazon Motherboard $71.99 Amazon Memory (32 GB) $109.99 Amazon Storage (500 GB) $57.99 Amazon Power Supply (600W) $62.99 Amazon Case $68.99 Amazon Total $511.94 *** Total estimated price as of 10/12/2020 Does not include shipping/taxes Obviously, you can swap out components as you see your needs fit.  I did not want to make a high end workstation with GPU, opting to use a CPU that had graphics built in.  I did opt to get 32 GB memory and 500 GB storage - I could have gone down to 16 GB for memory and 250 GB for storage, but I feel that memory and storage is something that I always seem

Hadoop Reading Material I'm starting to really get into my DS730 - Big Data: High Performance Computing class. I wanted to go beyond the instructors material and picked up some additional reading material. Hoping this will help me be successful in the weeks to come.