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end to end data science platform
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Features

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.

The platform also supplies a data governance mechanism which can incorporate existing governance rules and protocols and is flexible enough to be modified according to needs.

01
Integration
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.
02
Automation
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.
03
Orchestration
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.
04
Collaboration
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.
05
MLops data platform
Empower your 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.
30  x
Faster deployment
50  %
Cost reduction
500  %
Increase efficiency
400  %
Decrease financial risk

Advantages of using Digazu's platform

01
Be productive from day 1 thanks to your ready-to-use platform.
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.
02
Benefit from an easy to use, UI native solution.
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.
03
Secure the transition from POC to PROD.
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.
04
Use a robust and performant platform.
Benefit from our platform's high robustness, performance, availability, and scalability thanks to state-of-the-art technologies, carefully tested and selected by our private research center.
05
Improve transparency and compliance.
Benefit from built-in end-to-end traceability, giving full transparency of data usage for each of your use cases. Implement data anonymisation and minimisation principles. Manage data to facilitate regulatory compliance.
06
Improve the work methodology and be future-proof.
By using our standard way of working, you reduce maintenance and dependence on your developers and apply DevOps principles on your ML projects. All your models are handled the same way, making it easy for newcomers to adapt and improve them.
07
Improve the efficiency by automation & standardization
Standardise best practices and methodology with our meta-data driven data pipelines. Automate data collection, storage, transformation, and distribution with our low to no-code approach. Minimize time spent on environment setup and preparation. Focus on the added value.
08
Exchange data and collaborate in real time.
Benefit from data and insights shared in real time across the company and make sure that everyone is always looking at the same data. Become data-centric and improve your business decisions.

Read more about our data platforms

FAQ

01
I have a cloud data platform. Do I need an end-to-end data engineering platform?
Digazu is one level of abstraction above the Google Cloud AI platform. While Google-like platforms provide a suite of tools that can be used to implement AI projects, specific skills and substantial effort are needed to integrate and orchestrate them properly. Digazu is an end-to-end platform ready to use from day 1, making sure you can go from data exploration to running in production in a smooth and optimized way.
02
Why buy Digazu and not build the solution myself?
At Digazu, we have helped many companies become data-centric. We also saw many companies try to implement a solution by themselves, and most of them failed. The main reasons were that they didn’t have the experience and skills for such a transformation. We are data architects, engineers, and scientists, and we invested more than two years of work on a state-of-the-art platform. Enjoy Digazu; it will be faster than building a platform by yourselves, and with a much lower TCO!
03
How long is the setup?
Depending on your project, it can take from a few hours to 1 or 2 days. Our support team ensures you have 0 effort to make to get your platform ready.
04
What kind of project is it for?
We suggest you read our typical use cases. Digazu is perfect for any project involving data pipelines. Some of our customers use Digazu mainly to gather data in a governed solution, to build 360° views, or to enable real-time reporting.
05
Am I locked in with Digazu forever?
Digazu is fully transparent on how you collect and process your data. Plus, you choose where to store it, how to distribute it, etc. You can extract your data or change cloud providers anytime. You stay in control of your data.