ITOA is the practice of gathering data from various resources to make a decision and predict any potential issue that you may encounter. It's essential to analyze all events and operational activities in order to improve. Analyzing failures will help to predict any future event and keep the team ready to provide the appropriate response. Implement a mechanism to collect the logs of operations events, various activities across workloads, and infrastructure changes. You should create a detailed activity trail and maintain an activity history for audit purposes.
A large organization could have hundreds of systems generating a massive amount of data. You need a mechanism to ingest and store all logs and event data for a length of time, such as 90 or 180 days, to get insight. ITOA uses big data architecture to store and analyze multiple terabytes of data from all over the place. ITOA helps to discover any issue that you could not find by looking at individual tools and helps to determine dependencies between various systems, providing a holistic view.
As shown in the following diagram, each system has its own monitoring tool that helps to get insights and maintains individual system components. For operation analytics, you need to ingest this data in a centralized place. All operation data collection in one place gives a single source of truth, where you can query required data and run analytics to get a meaningful insight:
To create an operation analytics system, you can use scalable big data storage such as Amazon Simple Storage Service (S3). You can also store data in an on-premises Hadoop cluster. For data extraction, the agent can be installed in each server, which can send all monitoring data to a centralized storage system. You can use the Amazon CloudWatch agent to collect data from each server and store it in S3. Third-party tools such as ExtraHop and Splunk can help to extract data from various systems.
Once data is collected in centralized storage, you can perform a transformation to make data ready for search and analysis. Data transformation and cleaning can be achieved using a big data application such as Spark, MapReduce, AWS Glue, and so on. To visualize the data, you can use any business intelligence tool such as Tableau, MicroStrategy, Amazon QuickSight, and so on. Here, we are talking to about building an extract, transform, and load (ETL) pipeline. You will learn more details in Chapter 13, Data Engineering and Machine Learning. You can further perform machine learning to do predictive analysis on a future event.