ELK on Debian

BIG-DATA ANALYSISopensource

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About

ELK stack has been re-branded as Elastic Stack after the addition of Beats to the stack.

  • Elasticsearch is a search and analytics engine
  • Logstash is a data processing pipeline that ingests data from multiple sources concurrently, transforms it, and then sends it to a stash.
  • Kibana enables users to visualize data with charts and graphs in Elasticsearch

Miri Infotech is launching a product which will configure and publish ELK Stack, to produce free implementations of distributed or otherwise scalable machine learning algorithms which is embedded pre-configured tool with Debian and ready-to-launch AMI on Amazon EC2 that contains Elasticsearch, Kibana and Logstash.

Miri configured ELK Stack does not use its fourth attribute Beats. Elasticsearch, LogStash, Kibana and Beats are trademarks of Elasticsearch BV.  Elasticsearch, Logstash, and Kibana are registered in the U.S. and in other countries.

Miri is only configuring the product with its own referencing styles.

In simple words, Logstash collects and analyzes logs, and then Elasticsearch indexes and stores the data. Kibana then presents the information in visualizations that provide actionable insights.

Elastic Stack, comprehensive end-to-end log analysis solution that helps in deep searching, analyzing and visualizing the log generated from different machines. Organizations all over the world use these tools for performing critical business functions. These different tools are most commonly used together for centralized logging in IT environments, security and compliance, business intelligence, and web analytics.

These tools are spread across an extensive collection of languages. Beats are written in “Go” for convenient, efficient distribution of compiled binaries whereas Kibana uses Javascript for combined development of frontend and backend mechanisms.

Logstash

Logstash serves as the pillar for storage, querying, and analysis of your logs. With Logstash, it’s really easy to collect all those logs and store them in one centralized location. The only precondition is a Java 8 runtime, and it takes only two commands to get Logstash running. Since, it has a collection of ready-made inputs, codecs, filters, and outputs, you can grab hold of a dynamic feature-set effortlessly.

Elasticsearch

Elasticsearch is a NoSQL database, based on the Lucene search engine. A single developer can use it to find the high-value information underneath all of your data haystacks, so you can put your team of data scientists to work efficiently. Elasticsearch comes along with these benefits:

  • Real-time data
  • Real-time analytics
  • Document orientation
  • Full-text search

Kibana

Kibana is the log-data dashboard that can be installed on Linux, Windows, and Mac. It runs on node.js, and the installation packages come incorporated with the required binaries. It provides a better grip on large data stores with bar graphs, point-and-click pie charts, maps, trendlines, and scatter plots. Ultimately, each of your business lines can make practical use of data collection, as you help them customize their dashboards.

You can subscribe to ELK Stack, an AWS Marketplace product and launch an instance from the ELK Stack product’s AMI using the Amazon EC2 launch wizard.

To launch an instance from the AWS Marketplace using the launch wizard

  • Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/
  • From the Amazon EC2 dashboard, choose Launch Instance.
    On the Choose an Amazon Machine Image (AMI) page, choose the AWS Marketplace category on the left. Find a suitable AMI by browsing the categories, or using the search functionality. Choose Select to choose your product.
  • A dialog displays an overview of the product you’ve selected. You can view the pricing information, as well as any other information that the vendor has provided. When you’re ready, choose Continue.
  • On the Choose an Instance Type page, select the hardware configuration and size of the instance to launch. When you’re done, choose Next: Configure Instance Details.
  • On the next pages of the wizard, you can configure your instance, add storage, and add tags. For more information about the different options you can configure, see Launching an Instance. Choose Next until you reach the Configure Security Group page.
  • The wizard creates a new security group according to the vendor’s specifications for the product. The security group may include rules that allow all IP addresses (0.0.0.0/0) access on SSH (port 22) on Linux or RDP (port 3389) on Windows. We recommend that you adjust these rules to allow only a specific address or range of addresses to access your instance over those ports.
  • When you are ready, choose Review and Launch.
  • On the Review Instance Launch page, check the details of the AMI from which you’re about to launch the instance, as well as the other configuration details you set up in the wizard. When you’re ready, choose Launch to select or create a key pair, and launch your instance.
  • Depending on the product you’ve subscribed to, the instance may take a few minutes or more to launch. You are first subscribed to the product before your instance can launch. If there are any problems with your credit card details, you will be asked to update your account details. When the launch confirmation page displays

Usage/Deployment Instruction

Step 1: Open Putty for SSH


Step 2: Open Putty and Type <instance public IP> at “Host Name” and Type “ubuntu” as user name Password auto taken from PPK file


Step 3: Use following Linux command to start ELK


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 4: sudo su


Step 5: Now, enter the command with username of your choice:

htpasswd -c /etc/nginx/htpasswd.users <username>

Step 5.1: You will be prompted to enter the password of your choice:


Step 6: vi /etc/nginx/sites-available/default

Change the <server name> with your public <ip>.


Step 6.1: ln -s /etc/nginx/sites-available/default /etc/nginx/sites-enabled/default

Step 6.2: Now enter the following command:

service nginx restart

Step 6.3: Hit the <ip> on the browser

Enter the username and password that you obtained from Step 5 and Step 5.1 respectively.

Step 6.4: You will enter into the Kibana dashboard. Use it as you want.


(Optional)

Step 7: Enter the following commands:

service elasticsearch status


service logstash status


service kibana status


service nginx status


 

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    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

    • icon

      Elasticsearch is a search and analytics engine

    • icon

      Logstash is a data processing pipeline that ingests data from multiple sources concurrently, transforms it, and then sends it to a stash.

    • icon

      Kibana enables users to visualize data with charts and graphs in Elasticsearch

    Application Installed

    • icon ELK on Debian
    • icon java