Cloudera partner in Paris, France
Deploy real-time Big Data analytics on public-private clouds with a privacy-first architecture.
Combine public and private clouds
Cloudera is leading the Hybrid Data Cloud battle in the IT department and building new, easy-to-use cloud solutions.
- Support for the major Cloud provider, AWS, Azure, and GCP.
- Cloud infrastructure agnostic and easily portable applications.
- One platform to serve all data lifecycle use cases with a unified security and governance model
Build a unified Analytics practice.
- For data science, data engineering and analytical use cases
- Accessible to technical and business users
- Collaborate inside a compresensive platform
Innovate with Big Data & AI.
- Simplify the data architecture
- Eliminate the data silos
- Work across teams and innovate faster
Methodology and roadmap for success
Adaltas works with your team to leverage the Databricks platform with a comprehensive Methodology. Our experts are certified with Databricks as well as with the major Cloud providers including Microsoft Azure, Amazon AWS and Google GCP.
Qualify the use case
- What is the business challenge today.
- What is the business outcome and value you are hoping to achieve.
Qualify the data
- Is the data in the cloud?
- Describe the data: type, size, format, speed, ...
- Understand the complexity of the Big Data the client is working with.
Qualify the solution
- Describe the current technology ecosystem and data pipeline architecture.
- Who are the data users? (data scientits, data engineers, business users)
State-of-the-art platform for analytics and AI in the cloud
The extensive Spark ML libraries and integration with popular frameworks such as Tensorflow, PyTorch, etc. make Databricks the market leader among AI platforms. Additionally, the introduction of MLFlow has made managing the machine learning lifecycle easy and productive.
Discover past work and don't recreate the wheel
- Building models is a very iterative process and most gains are incremental
- Almost all Data Scientist teams regularly recreate work and therefore won't get as far as they could by refining past work. It is also a waste of money.
Collaboration between DS
- There is value to also sharing past work or working together on diffrent parts of the problem. Having a system of record for how work is done makes things easier and increase satisfaction.
- Collaborate with business users, data engineers and analyts.
Easy reproducibility of own and other works
- If a model is not reproducible, it is worthless
- It is also a cornertone of collaboration. Two individuals need to be able to reproduce others results.
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