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.

Quality assurance has become the lynchpin for business enterprises to improve the overall quality of software products and services. This helps reduce development costs, time and effort, minimizes rework, and improves the reliability and usability of the software. The State of Software Quality Report 2023 – with the exclusive partnership of Cigniti and Deloitte and based on insights from over 3000 QA teams, provides a comprehensive view of the software quality landscape.

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.

BigQuery change data capture lets you replicate, insert, update, and/or delete changes from source systems without DML MERGE-based ETL pipelines.

Fivetran, Talend, and Integrate.io are three of the most popular ETL tools on the market. Each platform offers unique features and capabilities, but deciding which ETL tool best suits your business depends on your needs and requirements.

At first glance, that does not make sense, right? The title suggests you should invest your DevOps/Platform team’s time in introducing a new product that most likely will: Through this article, we’ll show you that even though those initial concerns are justified, the overall benefits that can be realized by adopting a service mesh can outweigh any initial effort and investments.