in Blog

August 18, 2023

Mastering Databricks Deployment: A Step-by-Step Guide


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

In recent years, data-driven decision-making has become an integral part of business strategy in many companies. According to a recent survey[1], 91% of companies confirm that data-driven decision-making plays a crucial role in their business growth, while 57% of companies say they use data to make decisions in their daily business operations. This is mainly because data-driven decision-making helps these companies set measurable goals, improve their business processes, find unresolved questions, and guard against biases.

However, for these organizations to continue benefitting from data-driven decision-making, they need a fast, efficient, reliable, and scalable workspace for their data professionals. This is where Databricks comes in. Successful Databricks deployment is crucial in turning data analytics and machine learning (ML) projects into practical, fully operational solutions that drive business value. This enables organizations to adapt to changing market conditions and leverage the insights provided by available data.

This post will explore what Databricks deployment is, how to deploy Databricks solutions, and how to troubleshoot various issues related to the process.

Grasping the basics

Databricks deployment basically refers to the process of operationalizing various solutions, applications, and workflows within the Databricks platform. It involves taking the developed codes, MLflow models, and notebooks and making them available for analysis and consumption by data professionals.

Effective deployment of Databricks solutions ensures your data pipeline, ML models, and analytics workflows can handle large amounts of data without sacrificing long-term performance.

It also leads to the automation of mundane tasks, which helps save time and allows data scientists, data engineers, data analysts, and other data professionals to focus on advanced analysis and strategic big data initiatives that benefit the company.

Strategizing your Databricks deployment

Here are the steps you should follow to ensure a successful deployment:

databricks deployment step-by-step guide

  • Define Objectives: Ensure you clearly define the respective goals you want to achieve with this service.
  • Choose a Cloud Service Provider: Select your preferred cloud service provider and open an account. You can choose between AWS, Google Cloud Platform (GCP), Microsoft Azure, and many others, depending on your needs.
  • Open a Databricks Workspace: The next step is to create a Databricks workspace within your preferred cloud service provider’s environment.
  • Prepare Data: After setting up your Databricks workspace, the next step is data preparation. Once the right data has been collected, it must be cleaned, labeled, validated, and visualized using a dedicated data pipeline.
  • Develop Notebooks: Developing Databricks notebooks[2] provides great features such as automatic versioning and built-in visualizations. Shared and interactive Databricks notebooks also allow data professionals to collaborate on complex data science projects in real time.
  • Configure Clusters and Install Libraries: Ensure you set up clusters based on your workload and use Databricks Libraries to install the appropriate libraries.
  • Code Testing: Carrying out code testing will help you improve the quality and consistency of your Databricks notebooks’ code.
  • Documentation: Documenting your deployment provides guidance and reference information for data professionals working on various data science projects.
  • Compliance Checks: Verify that your deployment process adheres to data governance and compliance standards.
  • Continuous Improvement: Once you’ve carried out a successful deployment, it’s vital to continuously monitor and update it based on feedback and the changing business environment.

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Carrying out a successful Databricks deployment

To achieve a successful deployment, there are several prerequisites you need to consider and fulfill considering your data pipeline. They include:

Cloud service provider account

You need to have an active account on one of the cloud service providers, such as Google Cloud Platform (GCP), Microsoft Azure, Amazon Web Services (AWS), Alibaba Cloud, Oracle Cloud, IBM Cloud, Salesforce, Rackspace Cloud, VMWare, or others. This is mainly because Databricks operates as a cloud-based service when analyzing and managing large datasets.

Databricks workspace

Your Databricks workspace will serve as the hub where you can access all your Databricks assets, such as notebooks, clusters, experiments, jobs, models, libraries, dashboards, and many others. Notably, Databricks workspace is ideally designed and organized to support efficient and effective collaboration, development, and deployment of data science and data engineering projects.

Read more about Databricks for Business

Data sources

After creating a Databricks workspace, you need to identify and prepare the data sources you’ll use in Databricks. This usually includes structured, semi-structured, and unstructured data from various data storage solutions.

Data understanding

Before you start feeding data to your Databricks workspace, it’s highly recommended that you understand its quality, characteristics, and structure. Understanding your data in the early stages of a project will help you establish baselines, goals, benchmarks, and expectations to keep moving forward. This is vital for designing effective and efficient data processing and analysis workflows.

Data management plan (DMP)

A Data Management Plan (DMP)[3] is basically a document that describes how data will be collected, stored, analyzed, and shared within your Databricks workspace. It will help you plan and organize your data accordingly by answering any questions that may arise as you gather data.

ML objectives

Another crucial prerequisite for a successful Databricks deployment is clearly defining your machine learning (ML) objectives[4]. Doing so will help you determine the specific ML models you need to train depending on the size of the training data available, the training period required, and the accuracy of the required output.

Necessary skills

It’s important to ensure that your team possesses the necessary skills needed in data engineering, machine learning, and data analysis. These skills include solid programming skills, analytical skills, a great understanding of big data technologies, statistics knowledge, knowledge in data warehousing, cloud engineering skills, and problem-solving proficiency.

Cluster configuration

A cluster in Databricks is basically a group of virtual machines configured with Spark/PySpark and possesses a combination of computation resources on which you can run your notebooks, jobs, and applications. In simple terms, these clusters execute all your Databricks code. Before coming up with the appropriate cluster configurations, it’s important to understand the computational requirements of your workloads and the types of users that will be using these clusters.

Data governance and compliance

Understanding data governance and compliance within your respective industry is vital for successful deployment of Databricks. Adhering to these requirements, regulations, and standards helps establish strong protection measures, access controls, and retention policies within your organization. This is important for ensuring data consistency and trustworthiness through the process.

It might be also interesting for you: Data Engineering with Databricks

Budget and resource planning

Planning your budget before Databricks deployment ensures you spend available resources on the right things and that you respond to challenges promptly.

Troubleshooting Databricks deployment issues

During this process, you’ll likely encounter various issues, including the following:

  • Cluster configuration errors
  • Network issues
  • Insufficient permissions
  • Storage configuration errors
  • Credential configuration errors
  • Notebook name conflicts
  • 404 errors
  • Timeout errors
  • Version control conflicts
  • Integration challenges
  • MLflow UI errors

And here are some troubleshooting tips for addressing these issues:

  • Identify the issue
  • Review the Databricks logs and job outputs
  • Verify that the appropriate libraries and packages have been properly installed
  • Inspect your data sources for the correctness
  • Automate your data pipeline as much as possible
  • Verify the accuracy of your data pipeline
  • Check for any network issues that may be affecting your Databricks workspace’s connectivity with other services
  • Search online forums for solutions to common Databricks deployment issues
  • Review recent changes
  • Contact Databricks customer support

Wrapping up

Successful Databricks deployment allows data professionals to collaborate safely and effectively. It allows them to find and bring together siloed data in a useful way so that it can be used by different users, teams, and applications across an organization. In the long run, this process gives companies the ability to track and quantify development cycles and establish standards on how to run workloads.


[1] Data-Driven Decision Making Improve Business Outcomes. URL: Accessed August 13, 2023
[2] Databricks Notebooks. URL: Accessed August 13, 2023
[3] Data Management Plans. URL: Accessed August 13, 2023
[4] ML Objectives. URL: Accessed August 13, 2023


Data Engineering