About

Metabase, a business intelligence and data visualization tool with SQL capabilities is now available on Google Cloud Marketplace and is powered by Miri Infotech. 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

Miri Infotech, 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 16.04 and ready-to-launch AMI on Alibaba Cloud Network 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.

Usage/Deployment Instructions

Step 1: Access the Metabase in Google Marketplace and click the LAUNCH ON COMPUTE ENGINE button.

Step 2: In the Configure & Deploy window, enter or select appropriate values for zone, machine type, and so on. Click the Deploy button.

Step 3: The Google Developers Console confirms that Metabase was deployed

Use the browser to access the application at http://<instance ip address> replace <instance ip address> with the actual IP address of the running instance.

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

Note: Please open the following Security Ports in the instance:

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

For detailed procedure to add ports: click here

Step 1: Do SSH

Command: java -jar metabase.jar

Step 4: hit the browser http://<instance ip>:3000 where <instance ip is the public ip of your running instance. It can be found as show bellow:

Step 5: enjoy metabase and configure as you like.

All your queries are important to us. Please feel free to connect.

24X7 support provided for all the customers.

We are happy to help you.

Submit your Query: http://www.miritech.com/ContactUs.aspx

Contact Numbers:

(510) 298-5936
(510) 870-8668

Contact E-mail:

support@miritech.com

Other Useful Links:

Product Demo: #

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You can subscribe to this product from GCP Marketplace and launch an instance from the product's image using the GCP compute service.

Go to the VM instances page.
Select your project and click Continue.
Click the Create instance button.
Specify a Name for your instance.
Optionally, change the Zone for this instance.

Note: The list of zones is randomized within each region to encourage use across multiple zones.

Select a Machine configuration for your instance.
In the Boot disk section, click Change to configure your boot disk.

Create a boot disk no larger than 2 TB to account for the limitations of MBR partitions.

Select the Custom Images tab.
Select the image project from the dropdown.
Choose the image you want and click the Select button.
To permit HTTP or HTTPS traffic to the VM instance, select Allow HTTP traffic or Allow HTTPS traffic.

The GCP Console adds a network tag to your instance and creates the corresponding ingress firewall rule that allows all incoming traffic on tcp:80 (HTTP) or tcp:443 (HTTPS). The network tag associates the firewall rule with the instance. For more information, see Firewall Rules Overview in the Virtual Private Cloud documentation.

To add secondary non-boot disks to your VM instance:
1. Click Management, security, disks, networking, sole tenancy.
2. Select the Disks tab.
3. Under Additional disks click Add new disk.
4. Specify a disk Name, Type, Source type, Mode, and Deletion rule.
5. Click Done.
6. Add additional disks as needed.
Click the Create button to create and start the instance.

1What is Hadoop?

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.
2Where does Hadoop find applicability in business?

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.
3What is big data security analytics?

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.”
4Is there an easy way to migrate data from Hadoop into a relational database?

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.
5Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

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.
6What are the popular machine learning algorithms in use today?
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

Metabase

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1What is Hadoop?

2Where does Hadoop find applicability in business?

3What is big data security analytics?

4Is there an easy way to migrate data from Hadoop into a relational database?

5Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

6What are the popular machine learning algorithms in use today?

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