Not only does Spark handle data analytics tasks, but it also handles machine learning. In 2013, the creators of Spark started a company called Databricks. The name of their product is also Databricks. It’s a cloud-based implementation of Spark with a user-friendly interface for running code on clusters interactively. Microsoft has partnered with Databricks to bring their product to the Azure platform. The result is a service called Azure Databricks. One of the biggest advantages of using the Azure version of Databricks is that it’s integrated with other Azure services. For example, you can train a machine learning model on a Databricks cluster and then deploy it using Azure Machine Learning Services. In this course, we will start by showing you how to set up a Databricks workspace and a cluster. Next, we’ll go through the basics of how to use a notebook to run interactive queries on a dataset. Then you’ll see how to run a Spark job on a schedule. AZURE NOTEBOOKS HOW TOĪfter that, we’ll show you how to train a machine learning model. Prior experience with Azure and at least one programming language.People who want to use Azure Databricks to run Apache Spark for either analytics or machine learning workloads.Deploy a Databricks-trained machine learning model as a prediction service.Train a machine learning model using Databricks.Run code in a Databricks notebook either interactively or as a job.Create a Databricks workspace, cluster, and notebook.Finally, we’ll go through several ways to deploy a trained model as a prediction service. The GitHub repository for this course is at. Remember how we configured it to shut down if it’s inactive for 120 minutes? Well, even if you hadn’t used this cluster for over 2 hours, its configuration would still exist, so you could start it up again.ĭatabricks saves the configuration of a terminated cluster for 30 days if you don’t delete the cluster. If you want it to save the configuration for more than 30 days, then all you have to do is click this pin. OK, now that you have a cluster running, you can execute code on it. If you’ve ever used a Jupyter notebook before, then a Databricks notebook will look very familiar. Let’s create one so you can see what I mean. The notebook will reside in a workspace, so click “Workspace”, open the dropdown menu, go into the Create menu, and select “Notebook”. We’re going to run some simple queries, so select “SQL”.Ī notebook is a document where you can enter some code, run it, and the results will be shown in the notebook.įor the language, you can choose Python, Scala, SQL, or R. It’s perfect for data exploration and experimentation because you can go back and see all of the things you tried and what the results were in each case. It’s essentially an interactive document that contains live code. You can even run some of the code again if you want.Īlright, let’s run a query. Since we haven’t uploaded any data, you might be wondering what we’re going to run a query on. Well, there’s actually lots of data we can query even without uploading any of it. Azure Databricks is integrated with many other Azure services, including SQL Database, Data Lake Storage, Blob Storage, Cosmos DB, Event Hubs, and SQL Data Warehouse, so you can access data in any of those using the appropriate connector. However, we don’t even need to do that because Databricks also includes some sample datasets. To see which datasets are available, you can run a command in this command box. When we created this notebook, we selected SQL as the language, so whatever we type in this command box will be interpreted as SQL. The exception is if you start the command with a percent sign and the name of another language. For example, if you wanted to run some Python code in a SQL notebook, you would start it with “%python”, and it would be interpreted properly.
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