ELK STACK ON DEBIAN

  • Java
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ELK Powered by Niles Partners is a comprehensive end-to-end log analysis solution that helps in deep searching, analyzing, and visualizing the logs generated from different machines. The product is configured by Niles Partners and is now available on the Azure Cloud Platform. Companies all over the world use this tool to perform critical business functions. This tool is most commonly used together for centralized logging and for web analytics as well.

ELK has the following components:

  • 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

Niles Partners is launching a product that will configure and publish Elasticsearch, Logtash and Kibana, a comprehensive end-to-end log analysis solution which is embedded with Debian10 along with ready-to-launch Machine Image on Azure that contains Elasticsearch, Kibana and Logstash.

Niles Partners 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. 

Niles Partners is only configuring the product with its own referencing styles.

Simply put, Logstash collects and analyzes logs and then Elasticsearch indexes and stores the data. Kibana subsequently presents the information in visualizations that provide actionable insights.

These tools are spread across a compilation of different languages. Beats are written in “Go” for the convenient, efficient distribution of compiled binaries while Kibana uses JavaScript for combined development of frontend and backend mechanisms.

Logstash

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

Elasticsearch

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

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

Kibana

Kibana is a log-data dashboard which can be installed on Linux, Windows, and Mac. It runs on Node.js, along with the installation packages that come incorporated with the required binaries. It provides a better grip on large data stores with point-and-click pie charts, trend lines, bar graphs, maps, and scatter plots. In the long run, users from each business line can make practical use of data collection, as this helps them to customize their dashboards.
  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 Debian 10 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.

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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.

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

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

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