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Embedding Databricks Notebooks into a BlogEngine.NET post

Databricks is a buzzword now. This means that each day more and more related content appears on the net. With Databricks Notebooks it’s so easy to share code. If by any chance you need to share a notebook directly in your blog post here are some short guidelines on how to do so. If your favourite blog engine appears to be BlogEngine.NET it’s not so straightforward task. Fear not – following are the steps you need to take: Export your notebook to HTML from the Databricks portal: Make the following text replacements in the exported HTML: replace & with &amp; and “ with &quot; Paste the following code in the blog’s source replacing both the [[HEIGHT]] placeholders with the notebook’s height in pixels (you may need a little trial and error to get to the exact value so that the vertical scrollers disappear) and [[NOTEBOOK_HTML]] placeholder with the resulting HTML from the above point: <iframe height="[[HEIGHT]]px" frameborder="0" scrolling="no" width="100%" style="width: 100%; height: [[HEIGHT]]px;" sandbox="allow-forms allow-pointer-lock allow-popups allow-presentation allow-same-origin allow-scripts allow-top-navigation" srcdoc="[[NOTEBOOK_HTML]]"></iframe> To make everything compatible with Internet Explorer and Edge, at the very end of your blog script place between <script></script> tags the wonderful srcdoc-polyfill script Here is an example 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if (window.mainJsLoadError) { var u = 'https://databricks-prod-cloudfront.cloud.databricks.com/static/d9b6f94329a854d5e4b563a3854b37b2e3a5420ba1b2e8434244d6f80244e6c6/js/notebook-main.js'; var b = document.getElementsByTagName('body')[0]; var c = document.createElement('div'); c.innerHTML = ('Network Error' + 'Please check your network connection and try again.' + 'Could not load a required resource: ' + u + ''); c.style.margin = '30px'; c.style.padding = '20px 50px'; c.style.backgroundColor = '#f5f5f5'; c.style.borderRadius = '5px'; b.appendChild(c); } ">Your browser is not supported. Please use any of the following browsers: Chrome, Firefox, Safari, Opera. Not all notebook features are available within an iframe, but the main ones, such as syntax highlighting are intact and so is the ability to display a table results (with column sort capability!). The proposed way of embedding notebook code in a blog post is just the first idea that came to my mind. Maybe there are more clever ways to achieve this. Please let me know in the comments. 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Connecting Azure Databricks to Data Lake Store

Just a quick post here to help anyone who needs integrate their Azure Databricks cluster with Data Lake Store. This is not hard to do but there are a few steps so its worth recording them here in a quick and easy to follow form.This assumes you have created your Databricks cluster and have created a data lake store you want to integrate with. If you haven’t created your cluster that’s described in a previous blog here which you may find useful.The objective here is to create a mount point, a folder in the lake accessible from Databricks so we can read from and write to ADLS. Here this is done in notebooks in Databricks using Python but if Scala is your thing then its just as easy. To create the mount point you need to run the following command:-configs = {"dfs.adls.oauth2.access.token.provider.type": "ClientCredential",            "dfs.adls.oauth2.client.id": "{YOUR SERVICE CLIENT ID}",            "dfs.adls.oauth2.credential": "{YOUR SERVICE CREDENTIALS}",            "dfs.adls.oauth2.refresh.url": "https://login.microsoftonline.com/{YOUR DIRECTORY ID}/oauth2/token"} dbutils.fs.mount(   source = "adl://{YOUR DATA LAKE STORE ACCOUNT NAME}.azuredatalakestore.net{YOUR DIRECTORY NAME}",   mount_point = "{mountPointPath}",   extra_configs = configs)So to do this we need to collect together the values to use for{YOUR SERVICE CLIENT ID}{YOUR SERVICE CREDENTIALS}{YOUR DIRECTORY ID}{YOUR DATA LAKE STORE ACCOUNT NAME}{YOUR DIRECTORY NAME}{mountPointPath}First the easy ones, my data lake store is called “pythonregression” and I want the folder I am going to use to be ‘/mnt/python’, these are just my choices.I need the service client id and credentials, for this I will create a new Application Registration by going to the Active Directory blade in the Azure portal and clicking on “New Application Registration”Fill in you chosen App name, here I have used the name ‘MyNewApp’, I know not really original. Then press ‘Create’ to create the App registrationThis will only take a few seconds to create and you should then see your App registration in the list of available apps. Click on the App you have created to see the details which will look something like this:Make a note of the ApplicationId GUID (partially deleted here), this is the SERVICE CLIENT ID you will need. Then from this screen click the “Settings” button and then the “Keys” link. We are going to create a key specifically for the purpose. Enter a Key Description, choose a Duration from the drop down and when you hit “Save” a key will be produced. Save this key, its the value you need for YOUR SERVICE CREDENTIALS and as soon as you leave the blade it will disappear. We now have everything we need except the DIRECTORY ID. To get the DIRECTORY ID go back to the Active Directory blade and click on “Properties” as shown below:-From here you can get the DIRECTORY IDOk, one last thing to do. You need to grant access to the “MyNewApp” App to the Data Lake Store, otherwise you will get access forbidden messages when you try to access ADLS from Databricks. This can be done from Data Explorer in ADLS using the link highlighted below.Now we have everything we need to mount the drive. In Databricks launch a workspace then create a new notebook (as described in my previous post). Run the command we put together above in the python notebookYou can then create directories and files in the lake from within your databricks notebookIf you want to you can unmount the drive using the following commandSomething to note. If you Terminate the cluster (terminate meaning shutdown) you can restart the cluster and the mounted folder will still be available to you, it doesn’t need to be remounted.You can access the file system from Python as follows:with open("/dbfs/mnt/python/newdir/iris_labels.txt", "w") as outfile:and then write to the file in ADLS as if it was a local file system.Ok, that was more detailed than I intended when I started, but I hope that was interesting and helpful. Let me know if you have any questions on any of the above and enjoy the power of Databricks and ADLS combined.