In today’s fast-paced world, businesses face the challenge of managing the ever-expanding volume of data at their disposal, an issue that often hinders their ability to derive the critical and accurate insights they need to thrive and stay competitive. Driven by the monumental rise of generative AI, cloud services, remote work, and IoT devices, most organizations expect their data to double by 2025.

The term 'data pipeline' is everywhere in data engineering and analytics, yet its complexity is often understated. As businesses gain large volumes of data, understanding, processing, and leveraging this data has never been more critical. A data pipeline is the architectural backbone that makes data usable, actionable, and valuable. It's the engineering marvel that transforms raw data into insights, driving decisions and strategies that shape the future of enterprises.

For customers using Microsoft’s Data Lake offerings, they can securely land data in Delta Lake format from any one of Fivetran’s 400+ source connectors.

With ever increasing data complexity, sources, and targets, enterprises are rightly trying to simplify their data stacks and workflows. Organizations want to build out their data fabrics with elements that work together, rather than spend time integrating various solutions.

You can’t rank well in Google if its crawlers are unable to access your website. Sure, 404 errors and internal links have a part to play in how Google spiders find their way around your site. But we wanted to find out how big of an impact your site structure on SEO. And, if so, how you can optimize your website to make it easier for Google (and your users!) to navigate. So in this guide, we cover.

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.