Metabase
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Please feel free to contact us
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Metabase the classiest, simplest and fastest way to provide business intelligence and analytics to each person working in the company. Pushing data is one of the things in which it has achieved a great level. It is written in Clojure and offers multiple options such as Mac application, Docker image, cloud images and a jar file which are designed specifically for particular use cases.
Miri Infotech 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 16.04 and ready-to-launch AMI on Amazon EC2 that contains Hadoop, Hbase and SQL interface.
Now, while going through the information, the most important question arises is that why we choose metabase?
And to answer this is that, we should understand a concept of Pulses! It’s a feature in metabase that gives you the ability to automatically send regular updates without any intervention to your teammates to keep a full track on the changes that are done to the metrics which matters you the most.
1. A backend written in Clojure which contains a REST API as well as all the relevant code for talking to databases and processing queries.
2. A frontend written as a JavaScript single-page application which provides the web UI.
Supported Databases
You can subscribe to Metabase, an AWS Marketplace product and launch an instance from the Metabase product's AMI using the Amazon EC2 launch wizard.
Step 1: Open Putty for SSH
Step 2: Open Putty and Type <instance public IP> at “Host Name” Type "ubuntu" as user name Password auto taken from PPK file.
Step 3 : Use following Linux command to start Metabase
Step 3.1 : $ sudo vi /etc/hosts
Take the Private Ip address from your machine as per the below screenshot and then replace the second line of your command screen with that Private ip address
Step 3.2: Change ssh ‘ubuntu’ user to ‘root’
>> sudo su
Step 3.3: First remove ‘metabase’
>> docker rm metabase
Step 3.4: Start metabase with docker
>> docker run -d -p 3000:3000 --name metabase metabase/metabase
Step 3.5: Now Metabase start in Browser.
Open the URL: http://<instance ip address>:3000
Example: http://54.165.94.231:3000
Step 4: Register with Application
First Name : <Any Name>
Last Name: <Any Name>
Email Address: <Your Email Address>
Create a password: <InstanceID>
Confirm password: <InstanceID>
Your company or team name: <Team name>
Note: Email address is mandatory but storing data in your running application email address not to send anywhere else.
Step 4.1: Select database.
Step 4.2: Installation Complete
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The Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The software library is designed to scale from single servers to thousands of machines; each server using local computation and storage. Instead of relying on hardware to deliver high-availability, the library itself handles failures at the application layer. As a result, the impact of failures is minimized by delivering a highly-available service on top of a cluster of computers.
Hadoop, as a scalable system for parallel data processing, is useful for analyzing large data sets. Examples are search algorithms, market risk analysis, data mining on online retail data, and analytics on user behavior data.
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:
Quickly installable,usable by everyone
Ability to store and resuse filter definitions Metrics
The ability to store and reuse aggregation definitions.
Metabase
apache
linux
java
hadoop