Introduction

The current IT environment has evolved to a point where old, manual methods were insufficient to keep up with today’s needs. Increasing complexity, the need for quick solutions, and the massive size of data in IT operations require AIOps to function smoothly.

1. What is AIOps?

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT operations and produce results in real time. It is an essential tool for monitoring and managing IT Operations.

If issues in digital services are not quickly detected and resolved, business operations will be negatively affected. Customers will not have a satisfying experience. To avoid this situation, AIOps must be implemented.

AIOps does an algorithmic analysis of all the data and helps the IT Operations and DevOps (Development Operations) teams identify and resolve high-speed issues. AIOps prevents outages, reduces downtime, and provides seamless services. AIOps can give better insights as all the information is centrally stored in one place.

2. Aspects of IT Operations monitoring using AIOps:

a. Data Selection

The modern IT environment generates massive amounts of heterogeneous data. For example, event records, metrics, logs, and other data types from different sources like applications, networks, storage, cloud instances, etc. This data is always high in volume, and the majority of it is redundant. AIOps use entropy algorithms to remove noise and duplication.

b. Pattern discovery

Select meaningful data and group them by correlation and identify the relationship between them using various criteria. These groups of data can be further analyzed to discover a particular pattern.

AI integrations can be used with real-time data streaming events to identify patterns between big data for various business transactions. It is convenient as the process takes place in real-time, thus resulting in quick and accurate results. Businesses can use tools such as Apache Kafka with AI to help distribute data evenly at all destination points. AI, along with Kafka topic partitions, analyze and process data instantly and scale them with the help of a Kafka consumer group to identify data patterns even when there is a high inflow of big data.

c. Inference

Recurring problems are analyzed, and root causes are found. Identifying such issues makes resolving them more comfortable and quicker.

d. Collaboration

AIOps tools help report to required operators for collaboration without any mishaps, even when these operators are in different departments or different geographical locations.

e. Automation

Automation is the heart of AIOps. When the business’s infrastructure continues to grow and multiply, AIOps helps automate all business processes. It helps store data centrally, auto-discovering, and map the infrastructure, update databases (CMDB), automating redundant tasks and processes. Thus, leading to agile and efficient IT and business operations.

3. What is Big Data?

Big Data is a high volume of structured and unstructured data generated by businesses at high speed at varying veracity.

It systematically extracts meaningful insights from this data to make better decisions and strategic business moves. With the advent of digital storage in 2000, data creation increased as digital storage was cheaper than analog storage. DVDs made data sharing easier.

As institutions like universities, hospitals, and businesses started using technology, the amount of data created went through the roof. This resulted in two problems.

a. The rigidity of relational data structures

This was solved by using Data lakes. The data lake is a centralized repository that allows data storage of all the structured and unstructured data (usually files or object blobs) at any scale and makes it available for analysis.

b. Processing queries in the relational database has scaling issues.

When queries were processed in a single queue, it was time-consuming. The use of Massive Parallel Processing (MPP) resolved this technical issue.

Hadoop 1.0 is an open-source software framework. It was implemented using data lakes and MPP. Apache Hadoop facilitated the use of big data in all organizations. Hospitals, Scientists, and businesses used big data to analyze large data sets quickly and derive valuable insights.

Hadoop 1.0 had a few drawbacks. The optimization of data was complicated. The organization had to employ data scientists to get the required insights.

The introduction of Hadoop 2.0 resolved those issues and further commoditized big data. Hadoop 2.0 also enabled the use of AIOps.

4. The necessity of big data for AIOps

AIOps can function only with big data as older datasets are small and inefficient.

Hadoop 2.0 had a YARN feature that supported data streaming. It also enabled interactive query support. It allowed the integration of third-party applications.

This means that analytics could be improved, but only if it was re-architectured. Organizations without data science resources still had difficulty in optimizing and using Hadoop for better data analytics.

The requirement for more purpose-built and easy-use solutions brought companies like Logstash, Elastic, and Kibana to the market. They replaced Hadoop in a few use cases.

5. What does this mean for your business?

This is important for Core IT Operations and Service Management because they rely on interactive query and streaming data technology.

The Digital Transformation of organizations elevated the need for IT solutions. IT had to deal with increasing complexity, massive data size, and speed.

Transition by upgrading or re-architecture method to support Big Data was also tricky due to purpose-built applications, and the data remained in silos.

AIOps makes Artificial Intelligence take over manual analysis. Data from all the silos form the dataset. Interactive solutions are designed from both technical and usability perspectives.

Conclusion

IT operations need to work on diverse data, analyze real-time streaming data, identify and automate workflows, derive meaningful insights, and support historical analysis. All this requires businesses to build a Big Data backend on the AIOps platform.

AIOps initiative must not be built with a traditional, relational database. AIOps improve the functionality of IT operations. Hence, we can say that AIOps need Big Data to function efficiently. Also, businesses and corporations that need to store large amounts of data will need AIOps to function correctly, automate tasks, obtain insights, and work efficiently as per the trending demands of end-users.