What does it mean for your company?
Ingestion pipelines ensure your data flow is smooth, continuous, and governed. Code libraries, coding standards, version control, and pipelines that automate testing, ensure that the data team can visualize and create models in a repeatable way. Options for tight user-access control are available at multiple levels, integrated with your enterprise directory. Encryption is optional, and access logging is available as with any data factory. Multiple built-in security features ensure compliance with GDPR requirements.
With our platform — consisting of pre-installed data science libraries, a bunch of coding skeletons, and visualization features — data scientists can perform analytics and data science in the most efficient and effective way. They are not bothered with technical hassle. Instead, they drive the pre-processing, and create and train data-science models in an agile way of working. By doing this, they can smartify data fast, achieving results in as little as a few days – or fail fast and move to the next use case. And, if a use case is proven to drive business value, it can quickly be productionalized to run.
We also assist your data scientists by providing pipelines to handle the workflow, including automated testing of the created models and their versions.
Automation pipelines in the data factory make it very easy to spin up a development environment to test varied machine-learning models. Built-in code libraries and tooling will speed development. And as soon as a model has proven its value, the deployment pipeline enables you to rapidly deploy to production. Version control, automated unit tests, and integration tests ensure that this occurs in a controlled and repeatable manner.
After creating and extensively validating the models, the next step is to embed the results in your business. Together with your domain expert, we will decide what is the best way to do this. Perhaps the output is used to change a working process. Or software engineers turn the outputs into an end-user software application. Or software is used to transform human activities into machine-autonomous behavior.
The data scientist and data engineer can develop using Python, R, or Scala. We visualize with PowerBI, Splunk, Dash, and Shiny – whatever best suits your case. Any library can be used, and frameworks such as Tensorflow, PyTorch, and Spark are readily available and can scale on demand. We can build your data lake factory infrastructure on the technology you prefer: based on Hadoop (main distributions), Azure Data Lake, AWS Data Lake, or Splunk. Other technologies can be added. You can also rely on our standard data factory, instantly available, managed as a service, and based on Azure and Databricks technology.
Turn your data into value
A data factory follows a continuous flow of 3 cycles
Together with the domain expert, you generate visualizations and hypotheses of where the hidden value could be. Together we define, model and verify new value streams. The first intelligent use cases are born and proven.
Your data is flowing in a controlled manner through the data factory. Here, your data is securely ingested, stored, and processed. The factory runs a variety of these data pipelines, used for further learning. In parallel, the pipelines are embedded in your daily processes to offer value in a consistent and uninterrupted way.
The domain experts won’t sit still. The factory offers ways to further improve and tweak the models toward even more added value. True digital transformation is about doing, learning, and adapting.
The infrastructure / data engineer sets up the data lake platform including all security measures and connectivity, and takes care of automated ingestion of data and the storage of raw data.
The data engineer transforms raw data into enriched data (by slicing & dicing, aggregating & filtering, and combining with other data sources), performs data validations and monitors the data flow.
Once pre-processed, the data analyst can take the data to visualize and report on the current and past values.
The data scientist models the data to predict and automate future state via machine learning models, optimization algorithms, and regressions.
Embed via software
To embed the results into an autonomous system, our software engineers work together with your domain experts to translate the model outcomes into an application.
Embed via process
Our business analyst then collaborates with your domain experts to define changes in the business processes in order to embed the results into day-to-day operations.