In today’s digital era, data has become an integral part of every organization. The exponential growth of data continues to accelerate, with projections indicating a compound annual growth rate of 28% for data creation. While this surge in data presents vast opportunities, it also brings substantial challenges in terms of management and value extraction. This is where the concept of a data engine comes in. It serves as the core of your data infrastructure, functioning like a central nervous system.

Software responds to modern, highly dynamic, and distributed systems differently. Grasping this concept presents various challenges for today’s customers. They must understand how it behaves in the field, how it responds to diverse situations and modifications, and they need to keep track of progress made on thousands of products.

When it comes to tackling big data, there are three superior technologies that stand out: Apache Kafka, Apache Spark, and Apache Hadoop. Each of these solutions offers distinct advantages and weaknesses. Understanding their differences is vital in choosing which technology best fits your project’s needs.

We’ve made the case many times that instrumentation is critical for understanding changes in the physical and virtual worlds. During this recent webinar, panelists discussed the challenges and opportunities of integrating IoT sensors into existing infrastructure, ensuring data quality and accuracy, and leveraging sensor data for operational efficiency and productivity.

Calculating your Azure DevOps budget doesn’t need to make your palms clammy. Consider this your 2024 guide to figuring out all you need about Azure DevOps pricing so nothing sneaks up on your monthly budget. But let’s define some terms before we get into the nitty-gritty details.