As I have been doing some work on Databricks, I thought that it would make sense that I start writing about it. Databricks is a scalable environment used to run R, Python and Scala code in the cloud. It currently can be run in either AWS or Microsoft’s Azure Cloud. For those of you who are budget-minded when it comes to learning new tools, there is also a free tier, which is available here Community.cloud.databricks.com . It has somewhat limited compute capacity, but if you are just starting out you might find it helpful.
Databricks is an implementation of Apache Spark, part of the Hadoop collective and was a replacement for Map Reduce. Many of the people who worked on that open source project were students at Berkeley, where Apache Spark was created and added to Apache in 2013. Like many development projects, after it was completed, they had some ideas on how to improve the code. This time they decided to not make it open source but make it a commercial product so they could make some money for their development efforts. In April of 2017 Databricks was released on AWS and in March 2018 it was released in Azure.
Creating an Azure Databricks Service
Creating a Databricks Service is very straight-forward. There are only a few things that you need to complete when creating a new Databricks instance. The location becomes very important if you are looking at higher level performing instances which may not be available in all locations. Additionally, security considerations are also important for you if there is plenty of sensitive information being stored and accessed. You can either take up the exams yourself to learn about them (check out SC 900 dumps dumps here) or if that seems like a stretch, then hire someone with the know-how. Well, all of this only comes into the picture if you are working on a company project and not just for the sake of learning. If you are just getting started don’t worry about high level hosting services as you most likely will not need them and most of the compute options are available in most data centers. As always in Azure you want to make sure that you are hosting your Databricks service in the same location as your data so you will not need to pay to transfer data between data centers.
The Pricing Tier contains three options: Standard, Premium and Trial(Premium 14 Days). The trial is pretty self-explanatory and is a great way to get started using Databricks. They are of course a few differences between Standard and Premium. Premium has extra features needed for teams including Role-Based rights for the components of Databricks. And if you want ODBC authentication and Audit logs you will need to use Premium. For more information on the cost of Databricks pricing tiers, check out Microsoft’s pricing link for more information.
Once you have an instance created, you can start using Databricks. The application is contained within a managed instance, so once you launch Databricks you will be in their environment, which looks the same as the free edition.
Clusters, Notebooks and Data
These three components are the most important parts of Databricks as they include the compute power, where you write code and the information you work with respectively. These components are all separated in Databricks to improve scaling and provide a familiar environment to create and run code.
The most important Databrick element, as it contains the compute. This is also the part of Databricks which will greatly increase your bill as the more resources you use to run code the more money you need to run it. One nice thing is clusters by default will terminate in 120 minutes of inactivity. I generally drop this to 20 minutes. If I am using it naturally it will not terminate, but if I am not using it, I want the charges to stop. You can also automatically spin up clusters to run jobs, so that they will only be in use when the job needs them. More about that in another post.
There are 3 supported languages in Databricks, R, Scala and Python, and within Databricks all of these languages are written in Notebooks. You don’t have to write your code in the environment. You can write it locally and then import it. However, if you want to export your Notebook then run it locally, it gets trickier. Natively all of the Notebooks in Databricks are saved as .dbc files. You can’t read them from anywhere else. Fortunately there is a workaround to format the Notebook files as .ipynb files which can be read by any notebook. Dave Wentzel from Microsoft has an elegant solution to convert .dbc to .ipynb which he includes in his blog here.
You have a lot of options with data. You can import a dataset into your environment to play with or you can connect to just about anything you can think of. When you start doing data connections is when you stop using the community edition as you will want to use the Azure version to this to connect to various data resources like Azure SQL and blob storage. More on how to that later in an upcoming post.
If you are interested in hearing more about Databricks and are in Chicago, I am teaching an all day class as part of SQL Saturday Chicago and would love to have you attend. More information on that class is here.
Data aficionado et SQL Raconteur