Systems | Development | Analytics | API | Testing

Is your small business struggling with inflation? Not sure whether to cut costs or optimize spend? We’ve analyzed 55 small businesses in an attempt to help you solve this dillemma.

In its 2022 Magic Quadrant™ for Data Integration Tools report, Gartner® observes that “organizations are increasingly seeking a comprehensive range of improved data integration capabilities to modernize their data, analytics and application infrastructures.”

In the first blog of this series, I used the metaphor of a bridge connecting two towns to illustrate the importance of establishing purposeful connections between your shift left and shift right testing initiatives. Modern product organizations that fail to engineer a bridge to link the two “towns” (shift left and shift right) will, at a minimum, suboptimally utilize precious human and machine testing resources.

There are a number of things to consider when automating your mobile app tests, but first and foremost is to decide which tests should be and can be automated. This means understanding the business goals that are most important to your organization, as well as the risks you want to manage and mitigate. Mobile app testing is ultimately about managing risk, and the only way to do that is to align testing practices with the most critical business processes and goals you need to cover.

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes.

How long do users stay on your website? 50+ experts spill their secrets on how to improve your average session duration and what numbers to aim for.

The modern enterprise taps into over 400 different data sources to extract the insights that sharpen its competitive edge. The complexity, though, does not stop at the origin, where data is generated. To get valuable insights from raw data enterprises must extract data from its source, transform the data (clean and aggregate it), and finally load the data into a data warehouse or BI tool, where it is served to data scientists for analysis.

Apache Ozone is a distributed, scalable, and high-performance object store, available with Cloudera Data Platform (CDP), that can scale to billions of objects of varying sizes. It was designed as a native object store to provide extreme scale, performance, and reliability to handle multiple analytics workloads using either S3 API or the traditional Hadoop API.

Visualizing data is an important aspect of presenting insights clearly. But it's not always easy to create an effective visualization that people will understand on their first glance, or even second. So how do you create the kinds of graphs and tables that leave key stakeholders thinking, " Wow! I need this information!" In this post, we will discuss the top nine best practices for data visualization.

More data is available than ever, challenging organizations to change how they interact with their data so they can get the most out of it. Ahmed Munir, Lead SAP Functional Technology Architect Manager at Whirlpool Corporation, has 16 years of SAP leadership experience. He joins us to share what he has learned about building great data teams, upcoming trends in data analytics to keep an eye on, and how data teams will evolve over the next 5-10 years.