Systems | Development | Analytics | API | Testing

We launched the Mobile DevOps Assessment, then shared the results. Now it’s time to look at 5 apps, like Reddit and Wise, that reached Mobile DevOps maturity and the metrics that helped them get there.

Data ingestion is an important component of any successful data initiative. It refers to the process of collecting data from multiple sources and loading it into another system. Businesses most commonly use a subtype of data ingestion called ETL (extract, transform, load), which allows the data to be transformed before it's loaded. This extra step provides many benefits. Most importantly, it allows organizations to automatically match and correlate data from a variety of different sources.

Businesses today know that operational efficiency, effectiveness, and productivity are keys to competitive advantage. And process automation tools are being adopted more and more because they help IT and business leaders turn those goals into reality. If you’ve spent time researching ways to boost efficiency by taking advantage of automation opportunities, you’ve likely come across the related terms hyperautomation (which is synonymous with process automation) and intelligent automation.

Accelerate the extraction, loading, storing and transformation of data — including data from SAP systems — for faster, richer manufacturing insights.

Learn why parallel testing is a powerful tool for development teams to have in their automated testing strategy.

Manufacturers today are implementing a range of new technologies to increase operational efficiency and create visibility and flexibility across value chains. These include robotics, automation, data analytics, IoT, and artificial intelligence (AI) and machine learning (ML), according to Deloitte. Company leaders hope these innovations will help them create more productive and resilient supply chains, improve production quality and efficiency, and mitigate risks.

Data observability is a critical aspect of any data-driven business. It refers to the ability to monitor, understand, and troubleshoot data quality issues in real-time. In essence, data observability ensures that data is accurate, reliable, and accessible. It involves collecting and analyzing data from various sources to gain insight into how data is being used, how it is flowing through the system, and whether it is meeting the required quality standards.

Direct database connectors or connections act as a middleperson between an app and a database, allowing the app to read, write, and alter data in the database without an API or other intermediary layer. While these connectors might be simpler to set up than creating and automating an API, making them valuable for database management, they pose several cybersecurity risks that could put your business at risk. Below, learn about these threats and how to mitigate them with an API management platform.

Many organizations are experiencing a digital transformation to keep pace with the rapidly changing technology landscape and the increasing demand for faster time-to-market. By embracing Agile principles, like collaboration, iterative development, and continuous feedback, you can respond more quickly to changing requirements and market conditions. But these transitions don’t happen overnight.

Are you executing data replications and backups? Building ETL data pipelines? Ensuring the data ingestion is running smoothly? Or simply building an ad hoc dataset for your machine learning task? Using a data mapping tool can shave hours off your engineering work in all four use cases. In this article, you will learn.