Compare INQDATA vs. Streamlit

A cloud-based data science platform provides meticulously curated and refined data, primed for immediate use. Companies encounter considerable hurdles, limited resources, and elevated expenses when handling their data before they can extract any meaningful insights. The data undergoes a process of ingestion, cleansing, storage, and access, culminating in analysis, which is where true value is derived. Our solution empowers clients to concentrate on their primary business functions instead of the costly, resource-intensive data lifecycle, as we manage those complexities for them. Additionally, our cloud-native platform supports real-time streaming analytics, capitalizing on the advantages of cloud architecture, allowing INQDATA to deliver swift and scalable access to both historical and real-time data while eliminating infrastructure complexities. This approach not only enhances efficiency but also ensures that businesses can adapt quickly to their evolving data needs.

Streamlit is the quickest way to create and distribute data applications. It allows you to transform your data scripts into shareable web applications within minutes, all using Python and at no cost, eliminating the need for any front-end development skills. The platform is built on three core principles: first, it encourages the use of Python scripting; second, it enables you to construct an application with just a few lines of code through an intuitively simple API, which automatically updates when the source file is saved; and third, it simplifies interaction by making the addition of widgets as straightforward as declaring a variable, without the necessity to write a backend, define routes, or manage HTTP requests. Additionally, you can deploy your applications immediately by utilizing Streamlit’s sharing platform, which facilitates easy sharing, management, and collaboration on your projects. This minimalistic framework empowers you to create robust applications, such as the Face-GAN explorer, which employs Shaobo Guan’s TL-GAN project along with TensorFlow and NVIDIA’s PG-GAN to generate attributes-based facial images. Another example is a real-time object detection app that serves as an image browser for the Udacity self-driving car dataset, showcasing advanced capabilities in processing and recognizing objects in real-time. Through these diverse applications, Streamlit proves to be an invaluable tool for developers and data enthusiasts alike.