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.

According to research from Matillon and IDG, data volumes increase by 63 percent per month on average in an organization. Examining such substantial volumes of data without the right tools makes it impossible to make informed decisions, even in small businesses. The key to deriving useful and profit-driving insights from data is data visualization - which turns complex raw figures into meaningful visual representations of the data. Google Charts is a free data visualization library provided by Google.

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.

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.

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.

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.

In today's cloud-centric world, ensuring data quality and reliability on AWS is more crucial than ever. Dive into the best practices for data observability to harness the full potential of your data assets.

Companies want to train and use large language models (LLMs) with their own proprietary data. Open source generative models such as Meta’s Llama 2 are pivotal in making that possible. The next hurdle is finding a platform to harness the power of LLMs. Snowflake lets you apply near-magical generative AI transformations to your data all in Python, with the protection of its out-of-the-box governance and security features.

The rapid advancement of communication technologies has paved the way for the deployment of 5G networks, promising unprecedented speeds, low latency, and enhanced reliability. As 5G technology becomes increasingly integrated into our daily lives, ensuring its robustness and security has never been more critical. Digital assurance, a comprehensive testing and verification approach, plays a vital role in safeguarding the performance, stability, and overall quality of 5G networks.