Building Real-Time AWS Log Analytics Solution

In this AWS Project, you will build an end-to-end log analytics solution to collect, ingest and process data. The processed data can be analysed to monitor the health of production systems on AWS.

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Project Template Outcomes

  • What is the business problem that we are facing and its solution on it.

  • The list services that are going to be used in this project.
  • Understanding what is AWS S3.
  • Understanding how to create S3 bucket in AWS.
  • Understanding what is AWS IAM.
  • Understanding how to create an IAM policy and role.
  • Introduction to EC2 instance, and its use in project
  • Practical implementation how we can create an EC2 instance.
  • The basics of AWS kinesis firehose.
  • The basics of AWS Glue.
  • Understanding how to create Glue database and Glue table in AWS.
  • Understanding to create kinesis firehose delivery stream in AWS.
  • The basics of AWS Cloud watch and its use in project.
  • What is AWS Athena and its use in project.
  • Complete explanation of Project Architecture.
  • Installation process for installing AWS kinesis agent on EC2 instance.
  • What are the configuration used for the AWS kinesis agent.
  • Installation process for kinesis agent configuration on AWS EC2 instance.
  • Analyzing the logs file transferred through kinesis agent on Athena.

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Architecture Diagram

Building Real-Time AWS Log Analytics Solution architecture diagram

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Project Description

Business Problem

The common big data use case that we are going to take is “Log Analytics” where there is a requirement for analysing the log data which comes from various sources such as websites, mobile devices, sensors and applications. The tracking of application availability, fraud detection, and SLA monitoring can be achieved using log analytics. Automated trigger can be setup. The logs from different sources can be transformed to common format for easy query execution.


Project Architecture

 

Solution by using AWS Native Services:

As now a days various applications are running over the cloud so the logs from these applications can be parsed and stored in S3. End to end log analytics solution that collects, ingests, processes and loads both the batch data and streaming data. Processed data will be available to users in near real time. The solution is highly reliable, cost effective, scales automatically to varying data volumes and require almost no IT administration.

 

Services that we are going to use:

Amazon S3 – This is easy to use service with a simple web services interface to store and retrieve any amount of data from anywhere on the web. It is a safe place to store the files. The data is spread across multiple devices and facilities, this is object based service and the file size can be from 0 bytes to 5 TB for uploading. There is unlimited storage and the files are stored in buckets.

AWS IAM – This is nothing but identity and access management which enables us to manage access to AWS services and resources securely. We can create and manage AWS users and groups, use permissions to allow and deny their access to AWS resources. It is a feature of AWS with no additional charge.

AWS EC2 – It is a service which provides resizable compute capacity in cloud and designed to make web-scale cloud computing easier. We can launch instances with a variety of operating systems.

AWS kinesis firehose – In this the delivery stream is the underlying entity of firehose. Use the firehose by creating a delivery stream to a specified destination and send the data to it. The record is the data of interest which is our data producer sends to a delivery stream which can be large as 1000KB. The data producers send records to a delivery stream.

AWS Glue – It is a fully managed ETL service in which we can categorise our data, clean it, enrich it and move it reliably between various data stores. It is simple and cost effective.

AWS Athena – It is an interactive query service for S3 in which there is no need to load data it stays in S3. It is server less and supports many data formats e.g CSV, JSON, ORC, Parquet, AVRO.

AWS Cloud Watch – It monitors our AWS resources and applications that we run on AWS in real time.

 

Project Execution:

  • Go to AWS Console and create a S3 bucket with a unique name.
  • After that create IAM policy and role, remember we are creating the role and policy for EC2.
  • Create an EC2 instance using the free tier storage and Amazon Linux os for it.
  • Create a Glue database and table which is used for logs and also edit the table properties in it.
  • Create a kinesis firehose Delivery stream and configure that stream for S3 and other things.
  • Install the kinesis agent on the EC2 instance using External terminal (e.g Putty) and update the agent.json file into it.
  • Start the kinesis agent by using the command and after that create a app.log file update the log file with logs.
  • Open a duplicate terminal window run the command for reading the logs of kinesis agent.
  • After the logs transformed and sent successfully, go to AWS S3 and see if the bucket is being updated with the “parquet” log file.
  • We can check the log file which is in S3 with Query with S3 and with Athena query editor.

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