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Companies need to rely on connectivity and data to keep up with the competition. Organizations digitize their processes to reduce costs, enhance the customer experience, and improve efficiencies. In fact, 66% of businesses have automated at least one business process. However, as businesses adopt more data, tracking digital processes becomes increasingly complex and challenging. How can you know if these efforts are truly saving your organization time and money?

The Open System Interconnection model (OSI Model) is a foundational concept that shapes how we build digital environments. The OSI Model is a conceptual framework that describes how different computer systems communicate with each other inside network or cloud/internet environments. Today, let’s look at how the OSI Model affects our digital lives, applications and networks.

Today, everyone wants data to drive decision-making. Product analytics has emerged as a crucial tool for businesses to optimize their products and create exceptional customer experiences. In fact, 64% of respondents in one survey have achieved their goals through product analytics. But what exactly is product analytics, and how can it benefit your organization?

There’s a new term on the technology block: Applied Observability. Gartner estimates that 70% of organizations will successfully adopt applied observability capabilities in coming years. The most common use cases of applied observability will include: But exactly what is applied observability? We’ve got the answers and more here for you to get a full understanding. Read on!

Data dictionaries are an invaluable tool for any data-driven organization, but they can often seem like a complex and daunting task to build. Not only do you need to understand the definition of a data dictionary — you also have to know its components, benefits and how to create one. In this article, we'll cover everything about data dictionaries — from beginning to end — so that you'll have a good foundation of what a data dictionary is for. Read on for a detailed guide!

The past few years have seen an incredible rise in cloud computing. Organizations embrace the cloud to address the challenges of an uncertain economy, an increasingly distributed workforce and the pressure to deliver a better employee and customer experience. Gartner experts forecast almost $600 billion in worldwide public cloud spend in 2023. The cloud is no longer an option: nearly 9 out of every 10 IT leaders said it was a “cornerstone” of their digital strategy.

A little over two years ago, Splunk announced a revolutionary feature that would simplify the life of Splunk Cloud administrators by providing APIs that enable self-management and self-administration of Splunk Cloud Stacks. No more waiting for support tickets to be written, emailed, prioritized and then executed.

By 2025, more than 180 zettabytes of data will have been created and captured around the globe. With the influx of data in this digitized world, the process of data aggregation has become an essential tool for businesses. It’s a way to take large amounts of separate data from different sources and combine them into one collective body.

SQL and NoSQL are two database technologies widely adopted by many organizations for different use cases. Both technologies share the common goal of efficiently processing and managing data. Still, there are some significant differences. This article compares SQL and NoSQL, exploring their key differences in terms of language, structure, scalability, properties and support. We’ll also discuss examples, pros and cons and the most suitable application areas for each database type.