As the reliance on app-based services grows, digital transformation has become pervasive across all industries. Customers and end users expect increasingly efficient, safer, faster and better quality digital services from organizations. Multicloud environments, which are based on five different platforms on average, are at the heart of this transformation. They improve the agility of organizations so DevOps teams can accelerate their innovation and drive real value to the business.
But these multicloud environments have also introduced new challenges, given their complexity and scalability. Applications span multiple technologies, contain millions of lines of code, and generate even more dependencies. So much so that for DevOps teams, manually monitoring these environments, reconstructing and analyzing logs to get the information they need to deliver quality digital experiences, today exceeds their human capabilities.
AIOps to the rescue
Artificial Intelligence Platforms for Operations (AIOps) are gaining popularity among enterprises looking to master the complexity of multicloud and overcome its challenges. AIOps combines big data and machine learning techniques to automate IT operations, enabling organizations to innovate faster while freeing up valuable developer time to focus on more strategic tasks.
But AIOps intelligence depends on the quality and quantity of logs and data that teams feed into it, so observability is essential. Organizations need to capture detailed metrics, logs and traces from multi-cloud infrastructure and applications and integrate them with AIOps platforms.
This is what enables AI to provide DevOps teams with the insights they need to optimize applications, deliver better customer experiences, and drive more positive business outcomes. With higher quality observability data, AIOps solutions can provide better context and teams can operate more agile and informed.
Data, data, data
The problem is that in this frantic rush to gather more and more user data, metadata and insights into business outcomes, organizations are finding themselves overwhelmed. They simply cannot handle the sheer volume of data from the thousands of microservices and containers in their multicloud environments, not to mention the clicks and other user interactions with a digital service. Organizations are finding it increasingly difficult to continue using traditional log monitoring and analysis solutions, which were not designed for such an ongoing explosion of observability data.
Organizations therefore have the greatest difficulty in ingesting, storing, indexing and analyzing observability data at the right scale. The financial and time implications are too unprofitable. And organizations that have chosen to multiply monitoring and analytics solutions to meet their diverse needs are now faced with data silos that are hard to exploit. By making it more difficult to analyze log data in context, this fragmented approach limits the value that AIOps can bring to organizations.
Additionally, organizations are often forced to migrate their historical log data to cold storage (or storage repositories for data at rest) or to purge or purge the data entirely depending on the cost of the initial storage. While this makes log analysis more convenient, it also reduces its value and impact on modern AIOps approaches. When data is stored in cold storage, organizations cannot use AIOps platforms to run on-demand queries for real-time insights or to identify the context of the cause of a potential problem. This data must first be rehydrated and re-indexed before teams can query and gain insights, which can take hours or even days. The information generated may therefore prove to be already outdated and therefore of little use in preventing problems before they impact the customer experience.
Unlimited observability in the cloud
The reliance on multicloud environments and AIOps-driven automation shows no signs of slowing down, as appetite for digital services continues to grow everywhere. Therefore, organizations need to find a new approach to ingest, import, index, store and exploit observability data, fit for the cloud world.
It’s about having log analytics models designed to scale with the complexity of multicloud environments and the limitless evolution of the volumes of metrics, logs, and traces they create. A data lakehouse is a particularly efficient solution, as it combines the structure, management and querying characteristics of a data warehouse, but with the economic advantages of a data lake. Teams no longer have to manage multiple data sources, manually bring them together, and move them between hot and cold storage, improving the speed and accuracy of AIOps insights.
In this way, organizations can achieve fully contextualized analysis of data and logs, on a gigantic scale, enabling faster queries that provide more accurate AIOps responses. Armed with these capabilities, organizations can implement smarter automation to create better digital interactions for their customers and end users and gain an invaluable competitive advantage in an increasingly connected world.