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.
This end-to-end data platform focuses on the complexity of a fast data integration in the most secured environment possible thanks to:
- our state-of-the-art architecture and technology integration, (production-ready data pipeline in a few minutes and deploy it in 1 click).
- a multi-structure formats: access to our data lake to store the necessary data, whatever their nature or format, structured or unstructured.
- integrate and store data in the cloud or on-premises.
- explore both raw and historical data.
Our end-to-end data 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 data pipelines from the platform’s UI or API, without needing any line of code.
- handle your data management trough agile technologies and project management.
- automate promotion of data pipelines from one environment to another.
- move data pipelines from development to acceptance and production in a controlled and efficient way.
To orchestrate our end-to-end data science platform :
- uses state-of-the-art architecture and technology to manage all data flows in a smart and robust way.
- automates CI/CD Testing.
- follows the DevOps & DataOps Best practices.
- makes sure that data is collected only once from each data source, and then distributed to many applications seamlessly.
- runs 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 data science platform is meant to help your organisation become data-centric and let business value be a part of your data processes:
- facilitate the sharing of data between data engineers and data consumers (make developers and data scientists more efficient).
- provide a standard way to share cleaned data as well as insights in real time, to make sure data consumers are looking at the latest information when it is needed.
- search, find, and use the right data easily to enrich your value proposition and create added value.
MLOps data platform
Our end-to end data science platform is our end-to-end data engineering platform empowered 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).
- your customized analytical environments is set up in just a few clicks.
- use the sharing and collaborative aspects of the platform to stimulate learning.
- 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.