Data science and machine learning can be applied to solve many common business scenarios, yet there are many barriers preventing organizations from adopting them. Collaboration between data scientists, data engineers, and business analysts and curating data, structured and unstructured, from disparate sources are two examples of such barriers - and we haven’t even gotten to the complexity involved when trying to do these things with large volumes of data.
Recommendation engines, clickstream analytics, and intrusion detection are common scenarios that many organizations are solving across multiple industries. They require machine learning, streaming analytics, and utilize massive amounts of data processing that can be difficult to scale without the right tools. Companies like Lennox International, E.ON, and renewables.AI are just a few examples of organizations that have deployed Apache Spark™ to solve these challenges using Microsoft Azure Databricks.