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