AA - Advanced Analytics

DevOps is a set of practices which automate the processes between software development (Dev) and IT teams (Ops). DevOps isn't a software nor a framework; it's a mindset or way-of-working.

Continuous Integration/Continuous Deployment brings continuity where quality checks and thus bugs are checked frequently. At element61, we cheer for Azure DevOps which is a general DevOps tool providing CI/CD directly to Microsoft Azure Cloud Platform.

What's the maturity of the market Big Data technology is now truly mature. Additionally, the use of Big Data and Data Science is becoming truly visible all around us: in the news, as we read about smart Messenger service bots, the rise of IoT and connected devices, Artificial Intelligence, and digital marketing but also closer to us as we shop online and see personal product recommendations, receive customized newsletters and listen to recommended playlists.

element61 is a certified Databricks partner and offers end-to-end support in the set-up, use and training of Databricks.

element61 is a certified Databricks partner and has extensive experience in Delta Lake and Databricks Delta.

What are special features when running Databricks on Azure? (Azure Databricks)

element61 is a certified Databricks partner with extensive experience in development of Machine Learning algorithms and set-ups. On this page we give a rough sketch of how to leverage Machine Learning in Databricks. Feel free to contact us if you want to know more

With over 12 years of experience in BI and being a certified Databricks partner, element61 beliefs Databricks has a powerful role to play in a Modern BI Data Platform. On this page we give a rough sketch of why and how to run BI through Databricks and tips to get started. Feel free to contact us if you want to know more

In this fundamentals training we aim to share and show what a Data professional should know about building BI and Analytics platforms in the Azure Cloud.

If you are a Data Scientist, you might have experienced the struggle to properly manage all the different services (e.g. storage accounts, compute environments) that are needed to support your full machine learning (ML) development cycle. Similarly, it’s tough to manage your machine learning versions, your runs and a smooth, efficient ML model deployment. This is where Azure Machine Learning kicks in: an Azure service which allows you to manage your end-to-end Machine Learning process.