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.
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.
bashCopy
ssh azureuser@10.111.12.123
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.
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: