In the world of software development, there is an ever-growing need for agility and speed. With competitive markets and constantly evolving user needs, organizations can’t afford to waste time on the development cycle. This is where Continuous Integration (CI) comes in as an essential tool for modern software development practices.

Looking to take the load off your data engineers and IT pros? And help business users create and analyze datasets on their own? You’re in the right place. In this article, we’ll review the best self-service analytics solutions on the market today: We'll look at the main features of each tool, its pros and cons, its best use cases, and user reviews to help you make the right choice. Before we delve into each one, let’s set the tone of what to expect from a good service analytics tool.

Struggling to keep up with your organization’s hunger for data? That’s an obstacle that many data teams face when their data stack grows and they don’t have complete control over their complex data pipelines. If that’s a challenge you’re looking to solve, you’re in the right place. Below, we’ve curated our list of the best data observability tools every data team should know about.

However, the entire change in the development practices has only been triggered considering the extensive need for improving or meeting the end user’s perspective. And the process involves the best of Agile, DevOps testing services, QAOPs, and DevSecOps practices. Talking specifically for DevOps, it not only streamlines development and operations, but helps foster collaboration through enhanced software delivery for added efficiency, quality, and access to automation.

Lately, AI and Machine Learning have been the most significant breakthroughs in the software testing space, and they will remain a hot topic for the foreseeable future.

Of the many things one might take for granted, access to banking and financial services may not immediately come to mind. But as a thought experiment, imagine trying to buy a home or a car without the ability to take out a loan. Try depending on cash payments from your employer, or relying on alternative banking solutions like short-term payday loans, check-cashing services, and prepaid debit cards.

Navigating the complex world of data, businesses often grapple with raw, unstructured information; this is where data wrangling steps in, turning chaos into clarity. Seamlessly intertwined with ETL processes, data wrangling meticulously refines and prepares data, ensuring it's not just ready but optimized for insightful analysis and decision-making.

Snowflake’s data cloud has expanded to become a top choice among organizations looking to leverage data and AI—including large language models (LLMs) and other types of generative AI—to deliver innovative new products to end users and customers. However, the democratization of AI often leads to inefficient usage that results in a cost explosion and decreases the business value of Snowflake. The inefficient usage of Snowflake can occur at various levels.

With updates to our browser user interface (UI), Stories, report navigation and more, Yellowfin 9.10 is an update that further enhances your users' analytics experience. 9.10 brings significant updates. Browse Page UI has received a sleek makeover, simplifying navigation for reports, dashboards, and presentations, catering to both new and experienced users.

Websites and apps are slowly starting to integrate more collaborative features as a means of increasing interactivity and the functionality of various use-cases. Be it a collaborative document or a chat room, one common feature is to show who else is in the space with you. Typically this’ll be done with an Avatar Stack, a collection of icons, each representing a client connected to the space.