In the rapidly evolving landscape of big data and analytics, two names frequently emerge as frontrunners: Azure Synapse and Databricks. Both platforms offer powerful capabilities to handle vast amounts of data, support advanced analytics, and enable seamless integration with other tools and services. However, choosing between them can be challenging due to their unique strengths and features. In this blog, we will delve into a detailed comparison of Azure Synapse and Databricks, exploring their core functionalities, use cases, and how they cater to different business needs. Whether you’re a data engineer, data scientist, or business analyst, understanding the nuances of these platforms will help you make an informed decision for your data strategy.

As Carly Fiorina, former CEO of HP, rightly said, “The goal is to turn data into information and information into insight.” 

In this article, we will explore two of the leading analytics solution available for businesses and compare to find out which one is the right technology for you. Let’s take a deep dive into Azure Synapse vs Databricks.

Introducing Azure Synapse

Azure Synapst is a comprehensive analytics service provided by Microsoft that brings together big data and data warehousing. It is designed to give users a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. Here’s a breakdown of what Azure Synapse offers:

1. Integrated Analytics Platform:

Azure Synapse combines data integration, enterprise data warehousing, and big data analytics into a single unified service. This allows users to query both relational and non-relational data at scale using a unified experience.

2. Serverless and Provisioned Options:

Users can choose between serverless on-demand or provisioned resources, giving them flexibility and control over cost and performance. This means you can scale resources as needed, only paying for what you use.

3. Data Integration:

It offers robust data integration capabilities with Azure Synapse Pipelines, which is similar to Azure Data Factory. This allows users to create, schedule, and orchestrate their ETL/ELT workflows.

4. Synapse Studio:

A single workspace for data professionals that provides an integrated environment for data prep, data management, data warehousing, big data, and AI tasks. This includes a SQL-centric workspace for SQL developers and a code-free data orchestration for data engineers.

5. Query Performance:

Azure Synapse Analytics uses distributed query processing to optimize and execute complex queries quickly. It supports PolyBase technology to enable querying of data across multiple data sources.

6. Security and Compliance:

It offers advanced security features like column-level security, dynamic data masking, and always-on encryption to protect sensitive data.

7. Integration with Power BI and Machine Learning:

Azure Synapse seamlessly integrates with Power BI and Azure Machine Learning, allowing users to generate insights and build predictive models directly from their data within Synapse.

8. Support for Multiple Data Formats:

Azure Synapse supports various data formats, including CSV, Parquet, and JSON, making it versatile for different types of data storage and processing needs.

By providing a comprehensive and integrated approach, Azure Synapse simplifies the process of building and managing end-to-end analytics solutions, enabling businesses to gain faster insights and make data-driven decisions.


Introducing Databricks

Databricks is a unified data analytics platform that is built to simplify and accelerate data engineering, data science, and machine learning. It is known for its powerful combination of Apache Spark and a user-friendly collaborative environment. Here’s an overview of Databricks:

1. Unified Data Platform:

Databricks integrates with various data sources and provides a unified platform for data engineering, data science, machine learning, and analytics.

2. Apache Spark:

At its core, Databricks is built on Apache Spark, an open-source unified analytics engine for big data processing with built-in modules for SQL, streaming, machine learning, and graph processing. This allows for large-scale data processing and high-performance analytics.

3. Collaborative Workspace:

Databricks offers collaborative notebooks that support multiple languages like Python, R, Scala, and SQL. These notebooks provide a shared environment where data engineers, data scientists, and analysts can collaborate seamlessly.

4. Delta Lake:

Databricks includes Delta Lake, an open-source storage layer that brings reliability to data lakes. Delta Lake ensures data quality and consistency through ACID transactions and scalable metadata handling.

5. Machine Learning and AI:

Databricks provides tools and environments to streamline the end-to-end machine learning lifecycle. This includes model training, hyperparameter tuning, deployment, and monitoring, all integrated within the platform.

6. Data Engineering:

It simplifies data engineering tasks such as ETL (extract, transform, load) with high-performance pipelines that can handle large volumes of data. Databricks also supports scheduling and monitoring of data workflows.

7. Performance and Scalability:

Databricks automatically scales compute resources up or down based on workload requirements, ensuring optimal performance and cost-efficiency.

8. Integrations:

Databricks integrates with various data storage systems like AWS S3, Azure Data Lake Storage, and Google Cloud Storage, as well as BI tools like Tableau and Power BI, enabling a smooth data flow across the analytics ecosystem.

9. Security and Compliance:

The platform provides enterprise-grade security features, including role-based access control, data encryption, and compliance with industry standards such as GDPR and HIPAA.

10. Managed Service:

Databricks is offered as a managed service on major cloud providers, which means that users do not have to worry about infrastructure management. This allows them to focus on building and deploying analytics solutions.

By combining the power of Apache Spark with an intuitive and collaborative environment, Databricks helps organizations to accelerate their data-driven initiatives, from data preparation and analysis to advanced analytics and machine learning.

Data Governance

Azure Synapse vs Databricks: Why the Comparison Matters

Selecting the right data analytics platform is crucial for your business because it’s the key to unleashing your data’s full potential. Here’s why discussing Azure Synapse vs Databricks matters:

  1. Efficiency: The right platform saves time and resources, making data analysis faster and less labor-intensive.
  2. Accuracy: It ensures your data is reliable, preventing costly errors.
  3. Informed Decisions: The platform provides deeper insights and recommendations, helping you make data-driven choices.
  4. Cost Savings: The right platform can reduce unnecessary expenses by eliminating the need for multiple tools.
  5. Scalability: It can grow with your business as data complexity increases.

In a nutshell, choosing the right data analytics platform can be the difference between success and failure for your business, especially due to the costs and potential revenue generating opportunities associated with it.


Azure Synapse vs Databricks: Key Features

An Integrated Approach to Data Analytics – Azure Synapse

Azure Synapse Analytics, formerly known as Azure SQL Data Warehouse, is an integrated analytics service provided by Microsoft Azure. It brings together big data and data warehousing into a single platform.



Azure Synapse: An Integrated Approach to Data Analytics

Source: Microsoft

Read More: Data Analytics in Telecom Industry: A Comprehensive Guide

Here are some key features of Azure Synapse Analytics:

  1. Integrated Environment: Azure Synapse offers a platform for data preparation, management, and exploration.
  2. Resource Flexibility: Choose between on-demand or provisioned resources for cost and performance.
  3. Big Data Integration: Azure Synapse works with storage solutions like Azure Data Lake for data querying.
  4. Serverless Exploration: Azure Synapse Studio allows data exploration without managing infrastructure.
  5. Real-time Analytics: Azure Synapse provides real-time data insights.
  6. Machine Learning: Integrate with Azure Machine Learning for model building, training, and deployment.
  7. Security: Azure Synapse has enterprise security, including firewall rules and data encryption.
  8. Scalability: Azure Synapse adjusts to data volume needs, ensuring performance and cost flexibility.
  9. Development Tools: It integrates with tools like Power BI and Azure Data Factory.
  10. Data Warehousing: Azure Synapse is a cloud data warehouse with massively parallel processing capabilities.

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Databricks: Flexible and Open Source

Databricks is a cloud-based platform designed for big data analytics and artificial intelligence (AI). It was founded by the original creators of Apache Spark, a powerful open-source, distributed computing system.

Here are some key features of Databricks: 

  1. Unified Analytics: Databricks offers a space for data engineers, scientists, and analysts to collaborate.
  2. Spark Integration: Developed by Apache Spark creators, Databricks provides an optimized Spark version for large-scale tasks.
  3. Interactive Workspaces: Databricks has notebooks supporting Python, Scala, SQL, and R for data collaboration.
  4. Managed MLflow: Databricks integrates MLflow for managing the machine learning lifecycle.
  5. Delta Lake: Introduced by Databricks, Delta Lake ensures data reliability in Spark and big data tasks.
  6. Scalability: Databricks adjusts resources based on workload for optimal performance and cost.
  7. Security: Databricks has enterprise security, including encryption and role-based access control.
  8. Integration: Databricks works with AWS S3, Azure Blob Storage, and BI tools like Tableau.
  9. Optimized Runtime: Databricks Runtime enhances Apache Spark’s performance and usability.
  10. Cloud Integration: Databricks is available on Azure and AWS platforms.

data governance

Azure Synapse vs Databricks: Architectural Differences


Azure Synapse: MPP Architecture 

Azure Synapse: MPP Architecture

 Source: Microsoft

Azure Synapse Analytics is built on a massively parallel processing (MPP) architecture. It is designed to handle large-scale data warehousing workloads and can scale up to petabytes of data. 

The MPP architecture of Azure Synapse Analytics is based on a shared-nothing architecture, where each node in the cluster has its own CPU, memory, and storage. This allows for parallel processing of queries across the nodes, which results in faster query performance.

Read More: Databricks Vs Snowflake: Choosing Your Cloud Data Partner

Databricks: Lake House Architecture 

Databricks, on the other hand, uses a Lake House architecture. Azure Databricks architecture combines the best features of data lakes and data warehouses into a single platform. 

The Lake House architecture of Databricks is based on the Delta Lake technology, which provides ACID transactions, schema enforcement, and indexing capabilities on top of data lakes. Azure Databricks architecture allows for faster query performance and better data governance compared to traditional data lakes.

Data analytics


Synapse vs Databricks: Machine Learning Capabilities


Azure Synapse: Limited Git Support 


Databricks: Streamlined ML Workflows

Databricks: Streamlined ML Workflows

Source: Databricks

  • Provides a unified platform for end-to-end machine learning
  • Integrates seamlessly with MLflow for ML lifecycle management
  • Supports GPU-enabled clusters for faster model training
  • Robust Git integration ensures smooth version control
  • Supports libraries like TensorFlow, PyTorch, and Scikit-learn


Azure Synapse vs Databricks: Pricing Models


Azure Synapse: Storage and Processing Driven Pricing

The pricing of Azure Synapse Analytics is based on two factors: data storage and data processing

Azure Synapse Analytics offers various pricing editions, ranging from $4,700 to $259,200. The specific features and benefits of each edition is on the official Azure website.

The first 1 million operations per month are free. After this threshold, there are charges associated with the number of operations. For instance, after the first 1 million operations, there might be a charge of $0.25 per 50,000 operations.

Since Azure Synapse Analytics charges separately for storage and compute, it is difficult to obtain an estimate since it will vary on a case to case basis.

MS Fabric vs Tableau

Databricks: Simplified and Transparent Pricing

Azure Databricks pricing is based on the number of compute resources consumed. Azure Databricks costs do not include storage. You have to buy storage separately from Azure or AWS. 

Here are some examples of Azure Databricks pricing for different tasks – 

  • “Workflows & Streaming – Jobs” starts at $0.07 / DBU for data engineering and building data lakes.
  • “Workflows & Streaming – Delta Live Tables” is priced at $0.20 / DBU for streaming or batch ETL using Python or SQL.
  • Data Warehousing – Databricks SQL” starts at $0.22 / DBU for SQL queries, BI reporting, and data lake visualization.
  • “All Purpose Compute” begins at $0.40 / DBU for interactive data science and machine learning.
  • “Serverless Real-time Inference” is priced at $0.07 / DBU for live predictions in apps and websites.


Read More: Data Transformation – Benefits, Challenges and Solutions in 2023


Azure Synapse vs Databricks: Data Security 


Azure Synapse Analytics: Comprehensive Security 

It offers comprehensive security features to safeguard your data and applications. It includes network security and threat protection to detect SQL injection attacks, unusual access locations, and authentication attacks.

  • Offers firewall rules and virtual network service endpoints.
  • Provides managed private endpoints for secure access.
  • Integrates with Azure Active Directory for authentication.
  • Encrypts data at rest and in transit.
  • Supports advanced threat protection and monitoring.


Databricks: Role-Based Access Control

Databricks: Role-Based Access Control

Source: Databricks

Databricks provides role-based access control (RBAC) for managing user access to resources. RBAC allows you to assign roles to users or groups, determining their level of access to resources.

  • Implements role-based access control for granular permissions.
  • Uses encryption for data at rest and in transit.
  • Integrates with enterprise identity providers for authentication.
  • Provides audit logs for monitoring and compliance.
  • Supports virtual private cloud (VPC) peering for secure connections.


Azure Synapse vs Databricks: Comparison Table

This table shows, in a nutshell, our entire discussion about Azure Synapse versus Databricks.  


Feature CategoryAzure SynapseDatabricks
OverviewIntegrated analytics service combining data warehousing and big data analytics.Cloud-based platform emphasizing unified analytics and AI.
Azure Databricks vs Synapse Analytics ArchitectureUses a blend of data warehousing and big data analytics with Synapse SQL and Apache Spark.LakeHouse architecture combining data lakes and data warehouses.
Azure Databricks vs Synapse Analytics Machine LearningIntegrated with Azure Machine Learning; limited Git support; no native GPU clusters.Unified ML platform with MLflow; robust Git integration; supports GPU clusters.
Azure Databricks vs Synapse Analytics Data SecurityFirewall rules, virtual network endpoints, Azure AD integration, encryption at rest and in transit.Role-based access control, encryption, enterprise identity provider integration, VPC peering.
Azure Databricks vs Synapse Analytics ScalabilityProvides Massive Parallel Processing (MPP) for analytical workloads.Auto-scaling and optimized runtime for efficient data processing.
Azure Databricks vs Synapse Analytics IntegrationIntegrates with various Azure services and supports multiple programming languages.Supports a wide range of ML libraries and integrates with various data storage solutions.
Azure Databricks vs Synapse Analytics Development ToolsSynapse Studio for collaborative analytics.Databricks UI and Databricks Connect for enhanced developer experience.
Azure Databricks vs Synapse CostPay-as-you-go with options for committed-use discounts.Flexible pricing based on DBU usage; offers committed-use discounts.
Azure Databricks vs Synapse Analytics Cloud IntegrationPrimarily integrated with Microsoft Azure services.Available on major cloud platforms including Azure and AWS.



Which One is Right for You?

Choosing between Azure Synapse and Databricks hinges on your business’s specific needs and the intricacies of your sector.

If you’re in the market for a comprehensive analytics service that merges data warehousing and big data analytics, Azure Synapse is your prime candidate. As an integrated offering from Microsoft, it boasts features like real-time analytics, machine learning integration, and a robust security framework. Its design caters to businesses aiming for a harmonized platform that bridges the gap between traditional data warehousing and modern big data analytics.

Conversely, if your priority lies in harnessing the power of a platform rooted in unified analytics and artificial intelligence, Databricks stands out. Founded by the original creators of Apache Spark, Databricks delivers an optimized Spark experience, making it a powerhouse for large-scale data tasks. With its cloud flexibility, available on platforms like Azure and AWS, and unique features such as Delta Lake and MLflow, Databricks is tailored for those who seek a cutting-edge solution for big data and machine learning endeavors.

Read More- Choosing Your Azure Ally: Databricks vs Data Factory

The Value of Partnering with a Trusted Analytics Consultancy Firm

Today’s data-driven landscape requires businesses to increasingly recognize the significance of harnessing the power of data analytics. However, most analytics solutions require customization and business clarity to truly maximize their output.

The long and complex process of technology selection, system integration, data security, and regulatory adherence can often be daunting. This is where the right data analytics partner can make a world of difference for businesses. Let’s delve into the advantages of such strategic collaborations:


Read More: 10 Best Data Transformation Tools in 2023


1. Partnership Guided by Success

A seasoned analytics partner offers a well-charted roadmap, honed through numerous successful ventures. Their expertise not only accelerates deployment but also safeguards against potential pitfalls and risks.


2. Tailored Expertise with Ethical Foundations

A reputable consultancy boasts in-depth knowledge of cutting-edge analytics technologies, coupled with a deep understanding of your industry’s nuances. This dual expertise ensures solutions that are both tailored to your needs and ethically compliant, a crucial aspect for sectors like healthcare and insurance.


3. State-of-the-Art Tools and Frameworks

Collaborating with a consultancy equipped with a rich arsenal of tools and frameworks can revolutionize your analytics journey. These tools streamline everything from data gathering and processing to continuous monitoring and upkeep.

Partner with the Leading Data Implementation Firm in the US! (1)

Kanerika – Your Partner in Growth with Data Analytics

The biggest asset to a business is partnerships with credible agencies that can understand business requirements and customize technologies to achieve results. Enter Kanerika, a distinguished leader with over two decades of proven expertise in data management, AI/ML, generative AI, and data analytics. 

Kanerika’s team of over 100 seasoned professionals is proficient in all the leading data analytics technologies, ensuring you remain at the cutting edge of technological innovation. As a proud partner of leading data companies, Kanerika’s access to Azure Synapse and Azure Databricks amplifies your existing infrastructure, keeping you perpetually ahead of the curve.

With a track record of successful, scalable, and future-proof data analytics projects, Kanerika offers a robust, end-to-end solution that is technologically sound and compliant with emerging regulations. 

Choose Kanerika and embark on an accelerated journey to innovation and success.

Kanerika - Your Partner in Growth with Data Analytics



1. Is Databricks better than Snowflake?

A. Databricks and Snowflake serve different primary purposes. Databricks is primarily an analytics platform designed for big data processing and machine learning, leveraging Apache Spark. Snowflake, on the other hand, is a cloud data platform focused on data warehousing. The choice between the two depends on your specific needs: if you're looking for advanced analytics and machine learning capabilities, Databricks might be more suitable. If your primary need is data warehousing with seamless scalability, Snowflake could be the better choice.

2. Can you use Databricks with Snowflake?

A. Yes, Databricks can be integrated with Snowflake. You can use Databricks for data processing and analytics while storing and retrieving data from Snowflake. This combination allows businesses to leverage the strengths of both platforms.

3. How much cheaper is Databricks than Snowflake?

A. Pricing for both Databricks and Snowflake varies based on usage, features, and the specific cloud platform. It's essential to consider the total cost of ownership, including storage, compute, and additional services. Directly comparing costs might not be straightforward without specific details about usage patterns, but both platforms offer competitive pricing models.

4. Why choose Snowflake over Databricks?

A. Snowflake is a dedicated cloud data platform designed for data warehousing, making it an excellent choice for businesses that need a scalable, serverless, and fully managed solution for their data storage and querying needs. Its unique architecture allows for seamless scalability and data sharing. If your primary requirement is data warehousing with the ability to scale without managing infrastructure, Snowflake might be the preferred choice.

5. Which cloud platform is best for Snowflake?

A. Snowflake is a multi-cloud platform and can run on AWS, Azure, and Google Cloud. The "best" cloud platform for Snowflake depends on your existing infrastructure, preferences, and specific requirements. Each cloud provider offers unique features and integrations, so the optimal choice will vary based on individual business needs.

6. Does Snowflake run on Azure?

A. Yes, Snowflake is available on Microsoft Azure. This allows businesses that already use Azure services to integrate Snowflake seamlessly into their existing cloud infrastructure.