Analytics

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.

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.

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.

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.

Organizations are transforming their industries through the power of data analytics and AI. A recent McKinsey survey finds that 75% expect generative AI (GenAI) to “cause significant or disruptive change in the nature of their industry’s competition in the next three years.” AI enables businesses to launch innovative new products, gain insights into their business, and boost profitability through technologies that help them outperform competitors.

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.