Analytics

This year's Gartner Hype Cycle for Data Management report mentions self-service data management. It’s a game-changer that gives business users the power to work with data without constantly relying on IT, boosting data quality and making data available for analytics and decision-making. But what is it, really? How do you achieve self-service? Let’s take a closer look.

What’s one thing all your favorite legal shows have in common? Whether it’s Suits or The Lincoln Lawyer, they rarely show the amount of paperwork lawyers have to handle on a daily basis. Understandably so, paperwork isn’t the most glamorous part of the job but that doesn’t mean it’s not crucial. In fact, lawyers deal with tens, if not hundreds, of documents on a daily basis during most parts of their job, such as discovery, research, or drafting.

In the fast-paced world of software development, the efficiency of build processes plays a crucial role in maintaining productivity and code quality. At ThoughtSpot, while Gradle has been effective, the growing complexity of our projects demanded a more sophisticated approach to understanding and optimizing our builds. This requirement prompted us to explore Build Analytics—harnessing data from our build processes to gain actionable insights.

In the world of data management, clearinghouses often serve as intermediaries, enabling secure data transactions across different platforms and ensuring compliance with industry regulations. However, as data management needs evolve, businesses are increasingly looking for alternatives to clearinghouses that offer more flexibility, control, and advanced features.

Fivetran Managed Data Lake Service brings automation, governance and reliability to data lakes.

Support for pipe syntax in GoogleSQL brings the elegance of piped data flow to SQL queries in BigQuery and Cloud Logging.

Epicor’s ability to provide industry-focused, scalable, and customizable ERP solutions has made it a popular choice for organizations across the globe. Epicor’s built-in reporting capabilities are useful for standard reports but can be limiting for organizations that require more advanced analytics. Without deep technical knowledge of Epicor’s data structures, attempting to manually create custom reports can create serious roadblocks to data trust within your organization.

The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.

The Director of Product Management at AWS sat down with Fivetran to unpack how Iceberg and metadata are reshaping the future of data lakes, data warehouses and AI.