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Getting Started with Databricks

Databricks is an all-in-one analytics platform for big data. It helps businesses easily create, share, and maintain enterprise-grade data and AI solutions at a large scale.

SP

Snehal Patel

Chief Executive Officer

December 08, 2025
7 min read
Getting Started with Databricks

In the world of massive Data, it is very important to make sure your data is stored securely, easily accessible and have a wide range of integrations with the AI tools. Looking at all of this perspectives, we think that Databricks is a front runner.

Databricks widely known as a Data warehouse among the researchers, engineers in the Data industry. It was founded in 2013 and since then databricks has matured with proven benchmarks.

Well, prepare yourself to sock in the world of Databricks. We’ll be covering a brief, solemnly technical aspects of Databricks.

  • What is Databricks?
  • Databricks Architecture
  • What features Databricks provides?
  • Costing structure of Databricks
  • Alternatives of Databricks

What is databricks?

Databricks is an all-in-one analytics platform for big data. It helps businesses easily create, share, and maintain enterprise-grade data and AI solutions at a large scale. With Databricks, you can connect to your cloud storage, ensure security, and let the platform handle the management and deployment of cloud infrastructure for you.

Below are some of the best sample usecases where Databricks can help you boost productivity of your team.

  • Process and store data of all shapes and sizes: Think terabytes of sensor readings, customer transactions, research datasets, or social media buzz — Databricks can handle it all, seamlessly.
  • Transform data from messy to meaningful: No more wrestling with raw data. Databricks lets you clean, organize, and structure your information, making it ready for analysis.
  • Build powerful machine learning models: Predict customer churn, personalized recommendations, or analyze financial trends — Databricks provides the tools and libraries to create cutting-edge AI solutions.
  • Collaborate and share insights easily: Imagine data scientists, analysts, and business users working together in real-time on the same platform. Databricks fosters seamless collaboration and knowledge sharing.
  • Deploy your models into the real world: Once your AI models are trained, Databricks helps you integrate them into applications, websites, or any other system, putting your insights to work.

Databricks Architecture

Databricks architecture consists of two main components: the control plane and the data plane. These components work together to provide an unified and scalable platform for big data analytics and machine learning. Let’s explore each component and how they coordinate with each other.

Control Plane

  • The control plane is responsible for managing and orchestrating the overall Databricks environment for your Admin account.
  • It includes the Databricks Workspace, which is a collaborative space for users to create and manage notebooks, clusters, and jobs.
  • The Workspace also handles user authentication, access control, and collaboration features.
  • The control plane manages the deployment and configuration of clusters, job scheduling, and overall system monitoring.
  • Control plane data is stored with Databricks only.

Data Plane

  • Resides in your chosen cloud of the customer. Currently supported platforms are AWS, Azure, and GCP.
  • The data plane is responsible for the actual processing and storage of data. (Raw data is accessible at customer’s cloud account)
  • It includes the computing resources (clusters) that execute data processing tasks using Apache Spark or other distributed computing engines.
  • Data storage can be in various data sources, such as data lakes, databases, or streaming platforms.

Coordination between Control Plane and Data Plane

  • Users interact with the control plane through the Databricks Workspace, where they create and run notebooks, schedule jobs, and manage clusters.
  • When a user submits a job or runs a notebook, the control plane determines the resources needed and provisions the appropriate cluster from the data plane.
  • The control plane communicates with the data plane to allocate resources, deploy the required runtime environment (Databricks Runtime), and schedule the job execution.
  • The data plane, represented by the clusters, then executes the job by distributing tasks across its nodes, leveraging distributed computing frameworks like Apache Spark.
  • The results of the computation may be stored back in the data plane or made available for further analysis and visualization in the Databricks Workspace.

What features Databricks provides?

Below features collectively make Databricks a versatile and comprehensive platform for organizations looking to derive insights from big data, build and deploy machine learning models, and foster collaboration across data and analytics teams.

Notebooks

  • Interactive notebooks support multiple languages such as Scala, Python, R, and SQL, allowing users to perform data exploration, analysis, and visualization.

Distributed Computing with Apache Spark

  • Databricks leverages Apache Spark for distributed data processing, enabling the handling of large-scale datasets and parallel computing.

Databricks Runtime

  • Optimized runtime environment with performance improvements and enhancements over the standard Apache Spark distribution.

Data Integration

  • Seamless integration with various data sources and formats, including data lakes, databases, and streaming sources.

Delta Lake

  • Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It enables reliable data management and versioning.

Job Scheduling and Automation

  • Users can schedule and automate the execution of jobs for data processing, machine learning model training, and other tasks.

Machine Learning Library

  • MLlib is a scalable machine learning library integrated with Databricks, making it easy to develop and deploy machine learning models at scale.

MLflow

  • Integrated with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment.

Collaboration and Sharing

  • Version control for notebooks, sharing capabilities, and collaborative features facilitate teamwork and knowledge sharing.

Security and Access Control

  • Role-based access control (RBAC) ensures secure access to data and resources, and Databricks integrates with cloud provider security features.

Libraries and Dependencies

  • Users can install and manage libraries and dependencies, making it easy to use external libraries for analytics or machine learning.

Data Visualization

  • Databricks supports various data visualization tools and libraries, allowing users to create informative and interactive visualizations directly within the platform.

Streaming Analytics

  • Real-time data processing capabilities for streaming analytics using technologies such as Apache Kafka and Spark Streaming.

Monitoring and Logging

  • Comprehensive monitoring and logging features help users track and analyze the performance of their clusters, jobs, and applications.

Costing Structure of Databricks

Databricks pricing can be a bit complex as it depends on several factors, but here’s a general idea to start scratching your head around.

Billing Model: Databricks primarily uses a pay-as-you-go model

  • DBUs (Databricks Units): This is the main unit of billing, representing processing power. Your cluster charges are based on the DBU size and runtime. DBU depends on the usage of your cluster and scheduled Job.
  • Storage: Separate charges for storing data in object storage like S3 or Azure Blob Storage.
  • Additional services: Costs for features like Databricks SQL, MLflow, or serverless compute options might apply.

Cost Variables

  • Workload type: Different workloads (data engineering, data warehousing, ML) require different amounts of processing power and therefore have different costs.
  • Cluster size and type: Larger clusters have higher DBU costs. Cluster type (standard, optimized) also affects pricing.
  • Running time: The longer your clusters run, the more you’ll be charged.
  • Cloud provider: Prices may vary slightly between AWS, Azure, and GCP Databricks.

Cost-Saving Options

  • Free Community Edition: For learning and small workloads, the free Databricks Community Edition offers limited functionality.
  • Reserved Instances: Commit to using specific cluster configurations for a period for discounted rates.
  • Auto-scaling: Set policies to automatically scale clusters up and down based on workload demands, reducing wasted DBU usage.
  • Serverless compute: Utilize ephemeral clusters for smaller tasks for cost-effective processing.

It is very crucial to make sure you don’t pay too much for your workload. Team at Aptologics can help you design best cost saving strategy while keeping your workload scalable.

Pricing Resources

  • Pricing Calculator: Databricks provides a helpful pricing calculator to estimate your potential costs based on your specific workload and chosen configuration: Pricing Calculator Page | Databricks 👀
  • Documentation: Refer to the Databricks pricing documentation for detailed information on cost components and billing: Databricks pricing | Databricks

Opensource Alternatives of Databricks

Apache Spark

  • The beating heart of Databricks itself, Spark is a robust and widely used open-source engine for distributed data processing. While requiring more setup and management compared to Databricks’ managed platform, it offers flexibility and deep customization opportunities.

Apache Hadoop

  • Though not as trendy as Spark, Hadoop remains a reliable open-source platform for large-scale data processing. It excels in batch processing and offers mature tools like Hive and Pig for data warehousing and analytics.

Qubole

  • Think of Qubole as a user-friendly platform built on top of Spark. It offers cloud-based deployments, interactive notebooks, and prebuilt data pipelines, simplifying data engineering and analytics tasks.

Domino Data Science Platform

  • This open-source platform focuses on collaborative data science. It provides a secure environment for team work on notebooks, datasets, and models, making it ideal for data science teams.

StarRocks

  • Gaining traction, StarRocks is an open-source, distributed OLAP database designed for fast ad-hoc queries on data lakes. It supports Apache Parquet, Iceberg, and other data formats, making it a flexible option for data lakehouse analytics.

ClickHouse

  • Another open-source OLAP database, ClickHouse excels in real-time analytical processing and offers columnar storage for fast queries on large datasets. Its focus on simplicity and efficiency makes it attractive for some use cases.

Team at Aptologics have extensive experience managing enterprise grade data warehouse on top of Databricks. Please feel free to reach us on info@aptologics.com to learn more on how we can help you build your scalable data warehouse.


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