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Azure Synapse vs Databricks: 2023 Comparison Guide

26th Sep, 2023
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    Azure Synapse vs Databricks: 2023 Comparison Guide

    Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. The Microsoft Azure Synapse and Databricks platforms are two strong competitors in this space. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets. The KnowledgeHut Microsoft Data Architect Certification is a valuable credential for people looking to thrive in the field of data design and management, ideally fitting with the industry's changing requirements. To help data architects, engineers, and analysts choose the platform that best meets their objectives, we'll examine the essential characteristics, advantages, and use cases of Azure Synapse vs. Databricks in this thorough comparison. Let's start with this journey into the dynamic world of data platforms and certification, where innovation and knowledge converge.

    What is Azure?

    Before we dive into the comparison, let's start with a brief overview of Azure. Microsoft Azure, also known as Azure, is a well-known cloud computing service offered by Microsoft. It offers a wide range of services, including computing, storage, databases, machine learning, and analytics, making it a versatile choice for businesses looking to harness the power of the cloud. Azure provides the infrastructure and tools necessary to build, deploy, and manage applications and services efficiently.

    If you want a deep understanding of cloud computing, you can check out Cloud Computing online courses.

    What is Azure Synapse?

    Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

    Key Features of Azure Synapse

    Here are some of the key features of Azure Synapse:

    • Cloud Data Service: Azure Synapse operates as a cloud-native service, residing within the Microsoft Azure cloud ecosystem. This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads. Organizations can harness the power of the cloud, easily scaling resources up or down to meet their evolving data processing demands. This scalability allows for cost optimization, ensuring you only pay for the resources you need when you need them.
    • Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively. This is particularly valuable in today's data landscape, where information comes in various shapes and sizes.
    • Effective Data Storage: Azure Synapse offers robust data storage solutions that cater to the needs of modern data-driven organizations. It provides the infrastructure necessary for efficient data storage and management, enabling you to store and access large volumes of data reliably.
    • Responsive Data Engine: At the heart of Azure Synapse lies a powerful data processing engine. This engine is designed to handle complex analytical queries with remarkable speed and responsiveness. Whether you're running ad-hoc queries or performing complex data transformations, Azure Synapse ensures that your analytics are conducted swiftly, enabling timely decision-making.
    • Language Compatibility: Flexibility is a hallmark of Azure Synapse. It supports a variety of query languages, including the industry-standard SQL, as well as popular data analysis languages like Python and R. This compatibility ensures that data professionals can work with the languages they're most comfortable with, making it easier to leverage existing skills and tools.
    • Query Optimization: Query performance is critical in data analytics. Azure Synapse incorporates advanced query optimization techniques that fine-tune and optimize queries for efficiency. This not only speeds up data retrieval but also reduces the need for manual query optimization, allowing data teams to focus on insights rather than performance tuning.

    What is Databricks?

    Databricks is a unified analytics platform that has rapidly emerged as a powerhouse in the world of data science, engineering, and analytics. Born out of the minds behind Apache Spark, an open-source distributed computing framework, Databricks is designed to simplify and accelerate data processing, data engineering, machine learning, and collaborative analytics tasks. It has gained widespread popularity for its ability to seamlessly bring together data ingestion, exploration, model development, and deployment within a single, collaborative workspace.

    Key Features of Databricks

    1. Language Compatibility: Databricks provides extensive language compatibility, catering to data professionals with diverse skill sets. Some of the prominent languages supported include:

    • Scala: Ideal for developers who want to leverage the full power of Apache Spark.
    • Python: Widely used for data analysis, scripting, and machine learning.
    • R: A favourite among statisticians and data scientists for statistical analysis and visualization.
    • SQL: Enables users to query and manipulate data using standard SQL, making it accessible to a broad audience.

    This language versatility empowers data teams to work with the tools they are most comfortable with, fostering collaboration and efficiency.

    2. Productivity and Collaboration: Databricks excels in enhancing productivity and fostering collaboration within data teams. Here's how:

    • Interactive Notebooks: Databricks provides interactive notebooks (Databricks Notebooks) that allow data scientists, analysts, and engineers to create and share code, visualizations, and documentation in a collaborative environment. These notebooks support multiple languages, including Scala, Python, R, and SQL, making them versatile for various tasks.
    • Version Control: Databricks offers built-in version control, simplifying the process of tracking changes to code and data. This feature enhances collaboration and ensures that teams can work together seamlessly without the risk of code conflicts.
    • Workspace and Libraries: Databricks provides a centralized workspace for managing resources, libraries, and data. It also offers a library system for managing dependencies and sharing code across different notebooks and projects.
    • Collaborative Debugging: Teams can collaboratively debug code within notebooks, making it easier to identify and resolve issues quickly.

    3. Connectivity: Databricks is designed to seamlessly connect to a wide array of data sources and systems, which is essential for organizations dealing with diverse data landscapes. Key connectivity features include:

    • Data Ingestion: Databricks supports data ingestion from a variety of sources, including data lakes, databases, streaming platforms, and cloud storage. This flexibility allows organizations to ingest data from virtually anywhere.
    • Integration with Azure: As part of the Azure ecosystem, Databricks integrates seamlessly with other Azure services, such as Azure Data Lake Storage and Azure Blob Storage. This integration simplifies data access and management within the Azure cloud environment.
    • Third-Party Integrations: Databricks offers connectors and integrations with popular third-party tools and services, including business intelligence (BI) platforms, data visualization tools, and machine learning frameworks. This ensures that data professionals can leverage their preferred tools in conjunction with Databricks.
    • Streaming Data: Databricks supports real-time data processing by integrating with streaming platforms like Apache Kafka, Apache Flink, and Azure Stream Analytics. This capability is essential for organizations that require up-to-the-minute insights from streaming data sources.

    Now that we have an understanding of both Azure Synapse and Databricks at a high level, let's explore the differences and comparative aspects of these two data platforms in greater detail.

    Azure Synapse vs Databricks

    Let's summarize the key differences and comparative aspects of Azure Synapse and Databricks in a tabular format:

    AspectAzure SynapseDatabricks
    Primary UseData Warehousing and AnalyticsData Engineering, Data Science, ML, and Analytics
    Notebook CapabilitiesBasic supportAdvanced interactive notebooks
    Developer ExperiencePowerful but may require more setupStreamlined with extensive libraries
    ArchitectureMPP architecture for data warehousingBuilt on Apache Spark for versatile data processing
    Data Lake IntegrationSupportedNative integration with Azure Data Lake
    Machine LearningSupportedStronger focus with built-in ML tools

    Difference Between Azure Databricks and Azure Synapses

    Here are some of the important differences between Databricks and Synapse:

    1. Azure Databricks vs. Azure Synapse: Data Processing

    Both Azure Synapse and Databricks excel in data processing, but they have different primary use cases and focuses.

    Azure Synapse is designed primarily for data warehousing and analytics. It leverages a Massively Parallel Processing (MPP) architecture, which is optimized for executing complex analytical queries on large datasets efficiently. This makes it an excellent choice for organizations that need to analyze large volumes of structured and semi-structured data quickly and effectively.

    Databricks, on the other hand, offer a broader spectrum of data processing capabilities. While it excels in data engineering, it is equally adept at data science, machine learning, and real-time analytics. Databricks leverages the power of Apache Spark, a distributed data processing framework, making it suitable for handling both batch and real-time data processing workloads. This versatility is especially valuable when you have diverse data processing requirements within your organization.

    2. Azure Databricks vs Synapse: Smart Notebooks

    Both Azure Synapse and Databricks provide support for interactive notebooks, but there are differences in their capabilities and use cases.

    Databricks offers advanced and feature-rich notebooks that are well-suited for data science and collaborative data analysis. These notebooks provide an interactive environment for data scientists and engineers to write and execute code, visualize data, and share insights with team members. They support multiple programming languages, making it convenient for data professionals with diverse skill sets.

    Azure Synapse also supports notebooks, but its notebook capabilities are more basic compared to Databricks. Azure Synapse notebooks are primarily focused on SQL-based data analysis and query development. They are suitable for users who primarily work with SQL and require a straightforward environment for query execution and data exploration.

    If your organization's primary focus is data science and collaborative data analysis, Databricks' notebook capabilities are likely to be a better fit. However, if your workloads revolve mainly around SQL-based data warehousing and analytics, Azure Synapse notebooks may suffice.

    3. Azure Synapse vs. Databricks: Developer Experience

    The developer experience can significantly impact productivity and the ease with which data professionals can work with the platform.

    Databricks is renowned for its streamlined developer experience. It provides a unified workspace that includes an interactive notebook environment, version control, collaborative features, and access to a wide range of pre-installed libraries and frameworks. Data engineers and data scientists can seamlessly transition from data exploration to model development and deployment, all within the same platform. This cohesive experience promotes productivity and accelerates the development of data solutions.

    Azure Synapse, while powerful, may require more initial setup and configuration, especially for users who are new to the platform. The development experience is more SQL-centric, making it well-suited for traditional data warehousing tasks. While it offers integrated support for big data analytics, users may need to adapt to the platform's conventions and may require additional configuration for specific tasks.

    If your organization values a rapid and streamlined development process, Databricks is likely to provide a more satisfying experience. It offers a modern, collaborative workspace that caters to the needs of data professionals across the spectrum.

    4. Azure Synapse vs Databricks: Architecture

    The architectural differences between the two platforms are noteworthy and influence their capabilities and use cases.

    Azure Synapse is built on a Massively Parallel Processing (MPP) architecture, which is optimized for data warehousing workloads. In an MPP architecture, data is distributed and processed across multiple nodes in parallel, allowing for rapid execution of complex queries on large datasets. Azure Synapse's MPP architecture makes it ideal for scenarios where query performance and scalability are paramount, such as traditional data warehousing and structured data analytics.

    Databricks, on the other hand, is built on top of Apache Spark, a distributed data processing framework. Spark is designed to handle a wide range of data processing tasks, including batch processing, real-time stream processing, machine learning, and graph processing. This architecture makes Databricks highly versatile and suitable for diverse data processing needs. Spark's ability to distribute computations across a cluster of nodes enables it to process large volumes of data efficiently.

    The choice between Azure Synapse and Databricks from an architectural perspective depends on your organization's primary use cases. If your primary focus is on traditional data warehousing and structured data analytics, Azure Synapse's MPP architecture excels in delivering high query performance. However, if you require a platform that can handle a broader spectrum of data processing tasks, including real-time analytics and machine learning, Databricks' Apache Spark-based architecture is more appropriate. When considering the architectural distinctions between Azure Synapse and Databricks, it's essential to recognize how these differences can impact your data architecture choices. This evaluation becomes even more critical when seeking the most suitable solution for your organization's needs, and it's where the Microsoft Data Architect Certification can provide valuable guidance in making informed decisions. 

    5. Azure Synapse vs. Databricks: Leveraging Data Lake

    Leveraging data lakes for storing and processing data is a common practice in modern data architectures. Both Azure Synapse and Databricks offer integration with Azure Data Lake Storage, but there are differences in their capabilities and approaches.

    Databricks provides native integration with Azure Data Lake Storage, making it seamless to access and process data stored in Data Lake. This tight integration enables data engineers and data scientists to work directly with data in Data Lake, perform transformations, and build data pipelines with ease. Databricks also provides optimized connectors for other popular data storage solutions like AWS S3 and Hadoop Distributed File System (HDFS).

    Azure Synapse also supports integration with Azure Data Lake Storage and other Azure data services. However, its primary focus is on data warehousing and analytics. While you can ingest and analyze data from Data Lake within Azure Synapse, the platform may be more oriented toward structured data and SQL-based analytics.

    If your organization heavily relies on Data Lake as a central data repository and requires extensive data preparation and transformation capabilities, Databricks' native integration and data engineering capabilities are likely to be more beneficial. Azure Synapse, on the other hand, suits scenarios where you need the power of data warehousing alongside limited Data Lake integration.

    6. Azure Synapse vs Databricks: Machine Learning Development

    Machine learning has become an integral part of modern data platforms. Both Azure Synapse and Databricks offer machine learning capabilities, but there are differences in their approach and focus.

    Databricks places a strong emphasis on data science and machine learning tasks. It provides built-in libraries and tools for machine learning model development, training, and deployment. Data scientists and machine learning engineers can leverage Databricks for tasks such as data exploration, feature engineering, model training, hyperparameter tuning, and model deployment. Databricks also integrates with popular machine learning frameworks like TensorFlow and PyTorch.

    Azure Synapse also supports machine learning capabilities, but it may require additional setup and integration with Azure Machine Learning services for advanced machine learning workflows. While you can perform machine learning tasks within Azure Synapse, its primary focus is on data warehousing and analytics. If machine learning is a central part of your data strategy, Databricks offers a more comprehensive and integrated solution for your needs.


    Both Azure Synapse and Databricks are robust data platforms, each with its unique strengths and ideal use cases. The choice between these platforms should align with your organization's specific data ecosystem, team expertise, and project requirements.

    • Azure Synapse is an excellent choice for organizations heavily invested in data warehousing and structured data analytics. Its MPP architecture, responsive data engine, and SQL compatibility make it a powerhouse for large-scale analytics tasks. 
    • Databricks, on the other hand, is the go-to platform for organizations seeking a more comprehensive data solution. It covers data engineering, data science, machine learning, and analytics in a unified environment. Its strong emphasis on collaboration and productivity, coupled with its native integration with Azure Data Lake, makes it a preferred choice for teams looking to extract maximum value from their data lake.

    Ultimately, the right choice will empower your organization to unlock the full potential of your data and derive valuable insights for informed decision-making. Whether you choose Azure Synapse or Databricks, you can count on Microsoft Azure's robust and scalable cloud environment to host these platforms, ensuring that your data workloads thrive in the cloud.

    Frequently Asked Questions (FAQs)

    1When should I use Azure Synapse over Azure Databricks?

    Use Azure Synapse when your primary focus is on structured data warehousing and analytics. It excels in handling large volumes of structured and semi-structured data efficiently, making it ideal for organizations with well-defined data schemas and analytical requirements.

    2Can Azure Synapse and Azure Databricks be used together?

    Yes, Azure Synapse and Azure Databricks can be used together seamlessly. They complement each other in scenarios where you need both data warehousing capabilities (Azure Synapse) and advanced data processing, machine learning, or data science tasks (Azure Databricks). You can integrate these services to create a comprehensive data platform.

    3Are there any specific cost considerations when choosing between Azure Synapse and Azure Databricks?

    Cost considerations depend on your specific use case and data processing needs. Azure Synapse's pricing is typically based on the resources you provision, while Azure Databricks pricing factors in processing units and storage. Analyze your data volume, processing requirements, and expected workloads to determine which platform aligns better with your budget and performance expectations.


    Geetika Mathur


    Geetika Mathur is a recent Graduate with specialization in Computer Science Engineering having a keen interest in exploring entirety around. She have a strong passion for reading novels, writing and building web apps. She has published one review and one research paper in International Journal. She has also been declared as a topper in NPTEL examination by IIT – Kharagpur.

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