Why choose Azure Databricks Services for unlocking insights?
    • Easy switch between languages
    • Seamless integration with Microsoft Stack
    • No need for separate environments
    • Flexible and easy to get started with

    Azure Databricks being a Apache Spark-based analytics tool provides fast, easy, and collaborative method for businesses to bring insights from their data. This will help organizations realize their full potential by integration of services like never before. The advantage of using this Spark-based platform is that it allows commonly used programming languages like Python, R, and SQL (with slight modifications with APIs) to be used for the purpose of Analytics.

    With Polestar Solutions as your Azure Databricks solution partner you can develop smarter AI solutions, build scalable solutions, and transform data effortlessly by integrating with a variety of data stores such as Azure Synapse, Azure Cosmos DB, Azure Data Lake Storage, Azure Event Hubs and Azure Data Factory.

    Key Features of Azure Databricks Integration
    Apache Spark Environment

    Leverage on the capabilities of Spark and explore data with zero-management

    Analytics for all

    Perform large scale data processing for batch workloads & enable analytics to your data

    Choice of language

    In addition to R, Python, Scala, & SQL there are deep learning libraries like Pytorch & Tensor Flow

    Interactive Workspace

    For seamless collaboration between data scientists & Data engineers with notebooks & dashboards

    Native Integrations

    Integrate with Azure Data Factory, Azure Data Lake Storage, Azure ML, and Power BI


    Get enterprise-grade security with Azure Active Directory Integration, role-based permissions, and clusters

    Azure Databricks
    Azure Databricks
    Azure Databricks for Big Data Analytics & AI

    Our Azure Databricks solutions and services include conduction of Databricks migration and fit-analysis wherein we will work with your organization to demonstrate and implement the capabilities of Azure Databricks. Some of the key services include:

    ETL Stream Processing

    Perform Data ingestion, ETL activities and stream processing pipelines with Azure Databricks by combining with Azure Machine Learning & MLflow. Build and share Machine learning applications & AI solutions in minutes with reliable data engineering

    Data Science & ML

    Create and deploy AI & ML solutions by using Azure Databricks even for real-time streaming data. Manage models, reproduce runs, and track experiments in a collaborative space. Also integrate with Azure Machine Learning for a central registry of your pipelines, models, and projects

    Power BI Integration

    By integrating Azure Databricks with Power BI you can enhance the performance of Databricks beyond data scientists and bring it to business users. We can help your organization connect your Databricks clusters to Power BI for generating more powerful insights

    Modern Analytics Architecture with Azure Databricks
    Azure Databricks

    Azure Databricks offers organizations the latest versions of Apache Spark and provides powerful cluster management capabilities that allow you to create new clusters in seconds, seamlessly connect with open source libraries, dynamically scale up and down, and share them across teams. This is especialy useful for massive jobs without a need for seperate enviroment.

    Azure Data Factory is primarily used for Data Integration and Ingestion whereas Azure Databricks provides a collaborative platform for Data Scientists and Engineers to perform ETL activities and build Machine Learning models. One of the key differences is that ADF uses drag and drop features with a GUI for visualizing building Data pipelines whereas Databricks uses Python, Spark, R, Java, or SQL for performing Data Engineering

    Apache Spark, the platform out of which Databricks is based out of, is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data across computers with distribution tools. Spark can be deployed in a number of ways with Java, Scala, Python, R programming, SQL, Graph processing, Machine Learning, and streaming data.

    Databricks aims to bring analytics to solve the problems of your team. Based on the Apache Spark environment which is faster than the traditional Hadoop MapReduce in analyzing data of large volumes, Azure Databricks brings an interactive workspace for faster collaboration, exploration, and visualization of data.

    Get Started On Your Data Journey with Azure Databricks
    Begin Your Azure Cloud Migration Journey Now!
    Why Polestar Solutions for your Azure Databricks needs?
    • 7+ Years of Professional Experience
    • Certified Microsoft Azure Experts
    • Faster Deployment
    • Top Cloud Solution Providers
    • 24*7 Customer Support
    • Established architectural planning
    • Low migration risk
    • Integrated microservices