Various factors can impede an organization's ability to leverage Confluent Cloud, ranging from data locality considerations to stringent internal prerequisites. For instance, specific mandates might dictate that data be confined within a customer's Virtual Private Cloud (VPC), or necessitate operation within an air-gapped VPC. However, a silver lining exists even in such circumstances, as viable alternatives remain available to address these specific scenarios.

At Cloudera, we’re known for making innovative technological solutions that drive change and impact the world. Our mission is to make data and analytics easy and accessible to everyone. And that doesn’t end with our customer base. We also aim to provide equitable access to career opportunities within data and analytics to the workforce of tomorrow.

Today, we are pleased to announce the availability of Tricentis Test Automation (TTA) for SAP integrated with SAP Cloud ALM. This release is the result of three years of close collaboration between Tricentis and SAP, and the result brings the best of application lifecycle management and test automation to SAP customers.

Google BigQuery is a robust and scalable cloud-based data warehouse that allows storing and analyzing vast amounts of data. BigQuery is a natural choice if your data already exists on the Google Cloud Platform (GCP). But before you leverage the platform, you need to extract the source data, carry out transformations, and load the data into your data lake or warehouse. This is where the ETL process and the ETL tools play a significant role.

Today, we are excited to announce the support of Dockerfile based deployments in general availability. You can now deploy any GitHub repository that contains a Dockerfile across all our locations worldwide. It can be used to deploy APIs, full-stack applications as well as workers with no extra cost. Building and deploying using Dockerfiles offers more flexibility: you can deploy any kind of application, framework, and runtime, including with custom system dependencies.

In the world of software-as-a-service, product-led growth (PLG) is an easy way to enable users to experience the value of a product firsthand, without restrictions. PLG can drive adoption, retention, and sustainable business growth with its robust and open framework. But what drives PLG best, a freemium model or a free trial? With freemium, your top of funnel growth can skyrocket, while free trials see a 2-3x higher conversion rate thanks to urgency (ie, users lose access immediately on trial end).

We’ve got something truly special in store for you. We reached out to our expansive testing community, consisting of 40,000 testers, and posed a question about leveraging GPT prompts for various software testing scenarios and tips for effective prompting. The response was nothing short of astounding, and today, we’re thrilled to bring you the incredible insights we gathered. Prepare to be amazed as we unveil 15+ best ChatGPT prompts for software testing enthusiasts like you.

There is no doubt about it: Artificial Intelligence (AI) and Machine Learning (ML) has changed the way we think about software testing. Ever since the introduction of the disruptive AI-powered language model ChatGPT, a wide range of AI-augmented technologies have also emerged, and the benefits they brought surely can’t be ignored. In this article, we will guide you to leverage AI/ML in software testing to bring your QA game to the next level.

Database testing is the process of evaluating the accuracy, reliability, and performance of a database system. Its purpose is to ensure that the data stored there is consistent, valid, and can be correctly manipulated for business needs. The components to be tested are usually database schema, tables, and database triggers. Testers leverage SQL queries, data comparison tools, automation frameworks, or load testing tools to examine the data integrity, validity, security, performance, and structure.

Embedded analytics solutions have become increasingly popular in recent years, as more organizations across multiple sectors recognize the value of integrating advanced business intelligence (BI) and analytical capabilities into their existing software applications. Such solutions allow for deeper insights from data to make more informed decisions - this is well established.