data science platform?
Digazu is a smart end-to-end data science platform that combines a data lake, a data hub, and an MLOps platform that integrates a data science workbench. It ingests data from most data sources, stores it in a data lake, and makes the data available to data consumers in a standardized and fully managed way. It brings holistic data management into the company. It provides user-friendly interfaces while integrating with metadata management, security, and data quality tools.
Reduce implementation and integration costs, save time, and lower your risks thanks to our fully packaged platform. It implements state-of-the-art data architecture. Deploy it, and start building your data pipelines from day 1.
Thanks to the native UI, you don’t need advanced data engineering skills or technical know-how. Save time, reduce the errors and improve your efficiency by visually building your data pipelines directly in the end-to-end data science platform.
The ease of use of the platform lowers the training period and eases the skills transfer. It also gives data scientists the flexibility they need to use the infrastructure and technologies to work efficiently and provide production-ready results quickly.
Save time on the deployments of your data projects to production and increase the numbers of your go-to-market data projects. Thanks to our state-of-the-art technology, you deploy a production-ready data science model in 1 click.
The integration of the end-to-end data science platform to a company’s systems is done as fast as possible in the most secured environment possible. It will always only be a matter of minutes. Thanks to our state-of-the-art architecture and technology integration, you develop a production-ready data pipeline in a few minutes and deploy it in 1 click. To ensure maximum efficiency for the integration of our end-to-end platform, we grant you access to our data lake to store the necessary data, whatever their nature or format, which can be structured or unstructured. You can store the data in the cloud or on-premise, and you can explore both raw and historical data.
Our end-to-end science platform fully automates the creation of your data pipelines, from data collection to transformation and distribution.
Our metadata-driven configuration allows you to create these data pipelines from the platform’s UI or API, without needing any line of code. This also enables an automated promotion of data pipelines from an environment to another, moving from development to acceptance and production in a controlled and efficient way.
To orchestrate our end-to-end data science platform, we use a state-of-the-art architecture and technology to manage all data flows in a smart and robust way. The platform makes sure that data is collected only once from each data source, and then distributed to many applications seamlessly. It runs on a distributed architecture, with built-in scalability and handling of hardware failures. It guarantees high performance, with low latency and high availability.
The end-to-end science platform is meant to help your organization become data-centric. It facilitates the sharing of data, making your developers and data scientists more efficient. It also provides a standard way to share cleaned data as well as insights in real time, to make sure that the whole organization is looking at the latest information whenever it is needed. Search, find, and use the right data easily to enrich your value proposition and create added value. Let business value be a part of your data processes.
MLOps data platform
Empower the end-to-end data engineering platform with an MLOps data platform. It helps optimize the ML lifecycle from development to production. It offers a flexible solution for data scientists, providing accelerators to explore data and build models while enforcing best practices. Data scientists work from their web browser, benefiting from an open environment in a scalable architecture. Once the development is completed, the deployment of the model training and model serving pipelines is easily automated. Enhance your data scientists’ productivity and effectiveness. Set up your customized analytical environments in just a few clicks. Use the sharing and collaborative aspects of the platform to stimulate learning and allow different teams to share their mutual expertise to enhance the effectiveness and robustness of their predictive models to create added value.
To help you drive a successful data lake project you have to discover what are the challenges in a data lake project, and how you can minimise the failure risk.