METABASE

  • Big-data analysis
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About

Metabase, a business intelligence and data visualization tool with SQL capabilities is now available on Azure Marketplace and is powered by Niles partners. Metabase offers a simpler, faster way to power in-application analytics. Using a simple graphical interface, anyone from the company can easily create dashboards, set up nightly emails, or ask questions on their own.

Metabase is primarily used for analyzing existing data on a daily basis by swiftly fetching answers to the most common queries without dealing with complex workflows.

Why Metabase?

Power the in-app analytics without writing any SQL
Provide Application-wide Reports and dashboards
Provide Per-User, Per-Account, etc. Reports and Dashboards
Fully secure
Niles partners, one of the IT Solutions provider is launching a product which will configure Metabase to a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques which is embedded pre-configured tool with Ubuntu 18.04 and ready-to-launch AMI on Azure cloud that contains Hadoop, Hbase and SQL interface.
Running Metabase on a server will enable others to log into accounts and share reports & dashboards. It is written in Clojure and offers multiple options such as Docker image, cloud images, Mac application, and a jar file, which are specially designed for particular use cases.

Features

See streamed data plotted in real time
Log data to the onboard flash memory
View device information
Run diagnostics

The Metabase application has two basic components

1. The backend is written in Clojure that contains a REST API as well as all the relevant code for talking to databases and processing queries.

2. The frontend is written as a JavaScript single-page application which provides the web UI.

  1. Type virtual machines in the search.
  2. Under Services, select Virtual machines.
  3. In the Virtual machines page, select Add. The Create a virtual machine page opens.
  4. In the Basics tab, under Project details, make sure the correct subscription is selected and then choose to Create new resource group. Type myResourceGroup for the name.*.
  5. Under Instance details, type myVM for the Virtual machine name, choose East US for your Region, and choose Ubuntu 18.04 LTS for your Image. Leave the other defaults.
  6. Under Administrator account, select SSH public key, type your user name, then paste in your public key. Remove any leading or trailing white space in your public key.
  7. Under Inbound port rules > Public inbound ports, choose Allow selected ports and then select SSH (22) and HTTP (80) from the drop-down.
  8. Leave the remaining defaults and then select the Review + create button at the bottom of the page.
  9. On the Create a virtual machine page, you can see the details about the VM you are about to create. When you are ready, select Create.

It will take a few minutes for your VM to be deployed. When the deployment is finished, move on to the next section.

Connect to virtual machine

Create an SSH connection with the VM.

  1. Select the Connect button on the overview page for your VM.
  2. In the Connect to virtual machine page, keep the default options to connect by IP address over port 22. In Login using VM local account a connection command is shown. Select the button to copy the command. The following example shows what the SSH connection command looks like:

bashCopy

ssh azureuser@10.111.12.123

  1. Using the same bash shell you used to create your SSH key pair (you can reopen the Cloud Shell by selecting >_ again or going to https://shell.azure.com/bash), paste the SSH connection command into the shell to create an SSH session.

 Usage/Deployment Instructions

Step 1: Access the Metabase in Azure Marketplace and click on Get it now button.

Click on continue and the on create;

Step 2: In the Create a virtual machine window, enter or select appropriate values for zone, machine type, and so on. Click the create button.

 

Click on create.

Note: You will get the Instance IP Address as shown in the screenshot below:

Once your Deployment is successful , follow the following Steps;

Step 1: Please open the following Security Ports in the instance:

5601, 9200, 54323, 9093, 2181, 9092, 5902, 5901, 3000, 8091, 54321, 4040, 8787, 8080, 8088

Step 2: Do SSH

Command: sudo su

Command: cd /metabase

Command: java -jar metabase.jar

Note: you will see a url when your metabase in ready to be configured:

Step 3: Hit the browser http://<instance ip>:3000 where <instance ip is the public ip of your running instance. 

Step 4: Select the Preferred Language and hit next;

Step 5: Fill in the admin details of your Choice;

Step 6: Setting up the Database:-

Use the following;

User – root

Host – localhost

Database name – metabase

Port – 3306

Password – Niles@123

Database Type – Mysql

Paste the below data as shown in Image;

 Step 7: Click on Next;

Click on Take me to Metabase & Enjoy your Application.

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Captcha

Add the words “information security” (or “cybersecurity” if you like) before the term “data sets” in the definition above. Security and IT operations tools spit out an avalanche of data like logs, events, packets, flow data, asset data, configuration data, and assortment of other things on a daily basis. Security professionals need to be able to access and analyze this data in real-time in order to mitigate risk, detect incidents, and respond to breaches. These tasks have come to the point where they are “difficult to process using on-hand data management tools or traditional (security) data processing applications.”

The Hadoop JDBC driver can be used to pull data out of Hadoop and then use the DataDirect JDBC Driver to bulk load the data into Oracle, DB2, SQL Server, Sybase, and other relational databases.

Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity.

There are many machine learning algorithms in use today, but the most popular ones are:

  • Decision Trees
  • Naive Bayes Classification
  • Ordinary Least Squares Regression
  • Logistic Regression
  • Support vector machines
  • Ensemble Methods
  • Clustering Algorithms
  • Principal Component Analysis
  • Singular Value Decomposition
  • Independent Component Analysis

 

 

Highlights

  • Quickly installable,usable by everyone
  • Ability to store and resuse filter definitions Metrics
  • The ability to store and reuse aggregation definitions.

Application Installed