Using Databricks to develop scalable data solutions

Worried about performance when processing a Data Lake or other large data store? Need to develop in a collaborative environment and visualize the results? Databricks allows users to connect to data sources stream the data using Apache Spark for processing data to create a machine learning [ML] solution and providing the data for visualization. Databricks can provide a very quick way of processing data by adding nodes increase performance for tasks, such as analyzing data for a ML solution . Databricks also includes a collaborative workspace so teams of people can create code in a notebook in R or Python and implement the notebook as an Databrick job. The step-by-step demos will include all you need to know to implement Databricks.