Operations | Monitoring | ITSM | DevOps | Cloud

Multi-cloud has become a hot-button topic these days, with a lot of cloud experts taking a strong stance against the practice. There are exceptions and legitimate reasons why you might choose a multi-cloud strategy (e.g., you sell to customers who restrict you from hosting their data in AWS). Extenuating circumstances aside, there are far more reasons why multi-cloud isn’t a good idea.

A cloud-first strategy can feel like a big jump from traditional setups. One of the benefits of a hybrid or on-premises strategy is you feel like you’re in control. You and your team know where your critical servers live. You can touch them. Your team understands your security processes, and you can easily verify security personnel follow them. Those are all significant benefits. However, a growing number of software teams are choosing to move to cloud-first strategies.

Serverless has been gaining more and more traction over the last few years. The global serverless architecture market was estimated at $3.01 billion in 2017 and is expected to hit $21.99 billion by 2025. The number is reflected in the increasing amount of enterprises starting to look for ways of decoupling their current monolithic architectures and migrating their stack to serverless. Read more about the popular enterprise use cases for AWS Lambda.

At Illuminate, Genesys Cloud principal architect Kal Patel shared how they use Sumo Logic, how they manage it at scale, and some of the lessons they’ve learned in the last year.

Data sources ingest data in different sizes and shapes across on-premises and in the cloud, including product data, historical customer behaviour data, and user data. Enterprise could store these data in data storage services like Azure Blob store, an on-premises SQL Server, Azure SQL Database, and many more. This blog will highlight how users can define pipelines to migrate the unstructured data from different data stores to structured data using the Azure ETL tool, Azure Data Factory.

Monitoring vs observability – is there even a difference and is your monitoring system observable? Observability has gained a lot of popularity in recent years. Modern DevOps paradigms encourage building robust applications by incorporating automation, Infrastructure as Code, and agile development. To assess the health and “robustness” of IT systems, engineering teams typically use logs, metrics, and traces, which are used by various developer tools to facilitate observability.