Databricks Workspaces Simplified: The Ultimate Guide for 2023
A colossal amount of data is generated whenever a digital action is performed. As a result, Data Engineering, Data Analysis, and Data Science operations become crucial to store, manage, and deliver insights using the vastly generated data. Today, there are various tools available to help data professionals deliver meaningful insights to enhance business decision-making. Being an industry-leading analytics platform, Databricks Workspaces provides a unified environment for processing large amounts of data to get valuable insights. Databricks provides a Workspace that serves as a location for all data teams to work collaboratively for performing data operations right from Data Injection to Model Deployment.
Table of Contents
The article focuses on the Databricks Workspaces along with features of the Databricks Workspaces such as Clusters, Notebooks, Jobs and more!
Table of Contents
- Prerequisites
- What is Databricks?
- Key Features of Databricks
- What is Databricks Workspaces?
- Databricks Workspace’s Pricing
- Conclusion
Prerequisites
- A Fundamental Understanding of Big Data and Data Workflow.
What is Databricks?
Databricks is a San Francisco headquartered software company that provides numerous solutions for carrying out processes like Data Analytics, Data Engineering, & Data Science. It was founded in 2013 by Ali Ghodsi, who was one of the creators of Apache Spark. Built on top of Apache Spark, Databricks also facilitates the processing of a colossal amount of data while allowing different data teams to collaborate for building data-driven solutions. Being an end-to-end Data Science platform, users can leverage its superior features to simplify Data Science processes right from Data Preparation to Data Visualization and Model Development.
Key Features of Databricks
Databricks has numerous features that assist users in efficiently working on the Machine Learning Lifecycle. Some of these features are as follows:
- End-to-End Machine Learning: Building Machine Learning models comes with its fair share of challenges. It includes tasks like data cleaning, data exploration, feature engineering, model training and testing, and model deployment. To simplify the process of Machine Learning Model Deployment, Model Management, and more, it offers Managed MLflow, which is built on top of MLflow, an Open-source platform for Machine Learning lifecycles. With Managed MLflow, you can experiment with different libraries and frameworks while keeping track of changes made to tune models. Managed MLflow can also automate resource allocation tasks like Cluster Management to not only bring flexibility but also eliminate maintenance jobs.
- Collaboration: It helps developers in collaborating across Data Science, Machine Learning, and Engineering teams by allowing the sharing of notebooks and insights. This helps in real-time commenting and co-authoring notebooks or code to expedite the model development processes. It also allows Data Professionals to create, manage, organize, and develop in their own environment by granting multi-language support, thereby yielding maximum productivity.
- Integrations: To perform ML operations effectively across various IDEs, Databricks integrates with a wide range of developer tools like DataGrip, PyCharm, IntelliJ, Visual Studio Code, etc. The integration further extends to analytics tools like Power BI & Tableau for data visualizations with low- to no-code experience.
- Dashboards: It is a collection of reports, which lets users visually access tabular or CSV data in a graphical representation. The Dashboard also comes with a customizable feature that provides an interactive environment through which users can consume visual insights about data by changing the parameters of queries.
- Access Control: In Databricks, admins can manage the ACL permissions across the organization or teams for granting them access to work with Databricks workspace features like Clusters, Jobs, Notebooks, and Experiments. However, by default, all users will have access to all data and features present in the Workspace unless an admin tweaks ACL permissions.
What are Databricks Workspaces?
Databricks initially launched Workspace in 2014 as a Cloud-hosted environment for developing Data Science applications. The first release of Workspace only had notebooks but didn’t have source files, additional libraries, etc. It did not provide a clear path to productivity, nor production, and collaboration. To overcome these drawbacks, Databricks introduced the next generation workspace named Workspace 2.0 in 2020 to provide all data professionals with a unified development experience.
Today, you can use Databricks Workspace to get access to a wide range of assets like models, clusters, jobs, notebooks, and more. With collaborative notebooks on a scalable & secure platform, developers can handle complex ML problems with ease. It also ensures a best-of-breed developing environment for Git-based deployment and collaboration. The Workspace serves as a one-stop platform for all the ML development lifecycles, right from developing to deploying and updating ML models. Currently, Databricks services are fully integrated into Cloud Services like AWS, Google Cloud, and Azure.
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How to Create a Databricks Account?
- Open the Databricks’ homepage.
- Select the option “Try Databricks” in the left panel.
- Fill in the required information. You will need a valid email address to verify your account.
- Click on Sign up.
- It displays two options: Databricks platform Free Trial and Community Edition. While Databricks platform free trial plan is for businesses, Community edition plan is for students and educational institutions for practising and understanding purposes.
- For community plans, you need your Email Id. But for Databricks platform free trial plan, you should have any one of the Cloud platform accounts like AWS, Azure or Google Cloud.
- Choose the plan according to your needs.
- Click Get Started. Now, check your email for activating the account and creating the password.
- Using the credentials that you received in your mail, you can log in to the Databricks platform.
Databricks Workspace Assets
Databricks Workspace is a runtime environment for performing various use cases like running ETL Pipelines, Data Analytics, deploying Machine Learning models, and more. The Databricks Workspace comprises various assets that help developers perform different tasks according to their requirements. Some of the key assets are:
- Databricks Workspaces: Clusters
- Databricks Workspaces: Notebooks
- Databricks Workspaces: Jobs
- Databricks Workspaces: Libraries
- Databricks Workspaces: Repos
- Databricks Workspaces: Models
Databricks Workspaces: Clusters
It is a set of computation resources where a developer can run Data Analytics, Data Science, or Data Engineering workloads. The workloads can be executed in the form of a set of commands written in a notebook.
To Create a Cluster:
- On the starting page of the Workspace, click on the Create (plus symbol) in the sidebar.
- From the displayed menu, select the Clusters option.
- Once the Cluster page appears, name and configure the cluster.
- Click on Create Cluster.
Note: Clusters can also be created using the Cluster UI button on the sidebar.
Databricks Workspaces: Notebooks
It is a Web Interface where a developer can write and execute codes. Notebook contains a sequence of runnable cells that helps a developer to work with files, manipulate tables, create visualizations, and add narrative texts. It can also be used as an interactive document that can be accessed and updated by any co-developer of an organization.
To Create a Notebook:
- Click on the Create (plus symbol) in the sidebar.
- From the displayed menu, select the Notebook option and provide a relevant name to the notebook.
- Then choose the language of preference like Python, SQL, R, etc.
- Finally, select the Clusters where the created Notebook is to be attached.
Databricks Workspaces: Jobs
Jobs allow a user to run notebooks on a scheduled basis. It is a method of executing or automating specific tasks like ETL, Model Building, and more. The pipeline of the ML workflow can be organized into jobs so that it sequentially runs the series of steps one after another.
To Create a Job:
- Click on the Jobs UI button in the sidebar.
- Select Create job.
Note: Another way is to create a Job is by clicking on the Create (plus symbol) in the sidebar. From the displayed menu, select the Jobs option. Give a suitable name for the job and fill in the remaining configurations and click Create.
Databricks Workspaces: Libraries
To use custom code, third-party libraries, and predefined functions in the Notebook, developers can install required libraries. In Databricks Workspace, libraries can be installed in three ways. They are Workspace libraries, Cluster Libraries, and Notebook Scoped Libraries.
Databricks Workspaces: Repos
To empower the process of ML application development, repo’s provide repository-level integration with Git-based hosting providers such as GitHub, GitLab, bitBucket, and Azure DevOps. Developers can write code in a Notebook and Sync it with the hosting provider, allowing developers to clone, manage branches, push changes, pull changes, etc.
Databricks Workspaces: Models
It refers to a Developer’s ML Workflow Model registered in the MLflow Model Registry, a centralized model store that manages the entire life cycle of MLflow models. MLflow Model Registry provides all the information about modern lineage, model versioning, present condition, workflow, and stage transition (whether promoted to production or archived).
Databricks Workspace’s Pricing
Databricks Cloud pricing differs according to the Cloud Service platform (AWS or Azure or GCP) that users select. For more pricing details, check the link.
Conclusion
Databricks Workspaces provides a wholesome experience for all data professionals to solve any task related to data. Since it grants a unified platform for Data and AI tasks, it simplifies the process of Data Engineers, Data Analysts, and Data Scientists.
In this article, you have learned some of the vital constituents of the Databricks Workspace. These features of Databricks can be collectively used to perform many advanced operations like Deep Learning and end-to-end application development.
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