As Carly Fiorina, former CEO of HP, rightly said, “The goal is to turn data into information and information into insight.” Modern data demands force organizations to pick platforms that can scale, support analytics, ML, and integrate well. Two leading contenders are Azure Synapse Analytics and Azure Databricks. While both are powerful in handling big data, analytics, and AI, their core strengths, ideal use cases, and cost models differ.
According to a 2024 Gartner report, over 65% of enterprises are expected to adopt cloud-based data platforms, such as Azure Synapse and Databricks, for analytics and AI-driven insights. This rapid growth highlights the increasing importance of choosing the right platform that not only handles massive datasets but also delivers real-time, actionable intelligence. Below, we explore what each offers, what’s changed recently, and how to decide between them.
Key Takeaways Azure Synapse specializes in structured data warehousing and SQL-based analytics. Databricks focuses on big data processing, machine learning , and real-time pipelines. Synapse offers tight integration with Power BI and the broader Azure ecosystem. Databricks provides greater flexibility and scalability for diverse data types. Synapse pricing is based on serverless or provisioned models; Databricks charges by compute usage. Synapse is easier for SQL and BI teams, while Databricks suits data engineers and ML experts. Many enterprises combine both—Synapse for BI reporting and Databricks for advanced data science.
What is Azure Synapse? Azure Synapse Analytics is Microsoft’s all-in-one cloud platform that merges data warehousing, big data analytics , and data integration. It enables businesses to analyze structured and unstructured data at scale, with the flexibility of serverless on-demand or provisioned compute. Seamless integration with Power BI, Azure Data Lake, and Azure Machine Learning allows organizations to build end-to-end analytics solutions efficiently and securely.
Key Features:
Unified analytics platform for data integration, warehousing, and big data analytics Flexible compute: serverless or provisioned to optimize cost and performance Deep integration with Power BI and Azure Machine Learning for BI and predictive analytics Support for multiple data formats: CSV, Parquet, JSON, ORC Advanced security: column-level security, dynamic data masking , always-on encryption Synapse Studio: a single workspace for data prep, orchestration, management, and AI tasks Distributed query processing for high-speed analytics PolyBase technology to query data across multiple sources ETL/ELT orchestration with Synapse Pipelines for seamless data workflows Enterprise-grade governance and compliance, including integration with Azure Active Directory Struggling to choose between Synapse and Databricks? We simplify the journey. Partner with Kanerika for expert data strategy and implementation.
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What is Databricks? Databricks is a collaborative data and AI platform built on Apache Spark, designed to simplify big data processing , advanced analytics, and machine learning workflows. Its collaborative notebooks support multiple languages, enabling seamless teamwork among data engineers , scientists, and analysts. Delta Lake ensures reliability and consistency in data lakes, while auto-scaling clusters and cloud integrations make large-scale processing efficient and cost-effective.
Key Features:
Built on Apache Spark for high-performance big data processing Collaborative notebooks supporting Python, R, Scala, and SQL for seamless teamwork Delta Lake ensures ACID (Atomicity, Consistency, Isolation, Durability) transactions, data quality, and reliability Auto-scaling compute clusters for optimal performance and cost efficiency Integration with cloud storage: ADLS, AWS S3, Google Cloud Storage Supports real-time streaming analytics for live data processing Tools for end-to-end machine learning: model training, deployment, monitoring Scheduling and orchestration of data pipelines and ETL/ELT workflows Enterprise-grade security and compliance: role-based access, encryption, GDPR/HIPAA compliance Multi-language and multi-framework support for ML and AI experimentation Microsoft Fabric Vs Databricks: A Comparison Guide Explore key differences between Microsoft Fabric and Databricks in pricing, features, and capabilities.
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10 Key Differences: Azure Synapse vs Databricks 1. Core Purpose Azure Synapse: Primarily designed as an enterprise data warehousing and analytics platform, ideal for structured data, reporting, and business intelligence.Databricks: Built around Apache Spark, Databricks is a unified data platform focused on data engineering, data science, and machine learning, suitable for large-scale processing and real-time analytics.
2. Data Processing Engine Azure Synapse: Uses SQL-based engines (SQL Pools, Spark Pools) and supports serverless or provisioned compute for analytics workloads.Databricks: Built on Apache Spark, optimized for big data processing, machine learning , and real-time analytics with auto-scaling clusters.
3. Integration with Cloud Ecosystem Azure Synapse: Seamlessly integrates with Power BI, Azure Data Lake, and Azure Machine Learning, providing a cohesive analytics environment.Databricks: Integrates with Azure services but also supports multi-cloud setups, enabling flexibility across cloud platforms.
4. Machine Learning & AI Azure Synapse: Integrates with Azure Machine Learning for building and deploying models, mainly for structured ML tasks.Databricks: Provides end-to-end ML support via MLflow, including experimentation, model training, deployment, and monitoring, with collaborative notebooks for real-time teamwork.
5. Data Handling & Formats Azure Synapse: Best for structured data and supports CSV, Parquet, JSON, and other formats; optimized for batch analytics.Databricks: Handles structured, semi-structured, and unstructured data, including logs, streaming data , and Delta Lake for ACID (Atomicity, Consistency, Isolation, Durability) transactions.
6. Real-Time Analytics Azure Synapse: Supports real-time analytics through Azure Stream Analytics but is primarily optimized for batch processing.Databricks: Excels in real-time processing with Structured Streaming, enabling low-latency insights.
7. Collaboration Azure Synapse: Provides Synapse Studio for SQL-centric analytics and workflow orchestration, with limited collaboration capabilities compared to Databricks.Databricks : Offers collaborative notebooks that support Python, R, Scala, and SQL, with version control and real-time co-authoring for teams.
8. Security & Governance Both platforms provide enterprise-grade security and compliance:Azure Synapse: Column-level security, dynamic data masking, always-on encryption, and integration with Azure Active Directory.Databricks: Role-based access, data encryption , and compliance with standards like GDPR and HIPAA.
9. Pricing Model Both platforms follow a pay-as-you-go pricing model:Azure Synapse: Charges separately for storage and compute, with costs depending on the compute model and usage.Databricks: Charges for compute usage and storage, with auto-scaling clusters to optimize costs based on workload.
10. Best Use Cases Azure Synapse: Ideal for enterprises focusing on data warehousing, reporting, and structured analytics, especially within the Azure ecosystem.Databricks: Best for organizations requiring advanced data engineering, real-time analytics, and machine learning capabilities.
Azure Synapse vs Databricks: Comparison Table To make the differences clearer, here’s a quick summary comparison table of Azure Synapse vs Databricks:
Feature / Aspect Azure Synapse Databricks Primary Purpose Data warehousing and analytics platform for structured data and business intelligence Unified data platform for data engineering, machine learning, and big data processing Data Processing Engine SQL-based engines (SQL Pools, Spark Pools), distributed query processing Built on Apache Spark, optimized for large-scale and real-time analytics Compute Model Serverless on-demand or provisioned compute Auto-scaling Spark clusters for variable workloads Data Handling Structured data supports CSV, Parquet, and JSON Structured, semi-structured, unstructured data; Delta Lake with ACID transactions Machine Learning & AI Integrates with Azure Machine Learning for predictive analytics End-to-end ML support with MLflow and collaborative notebooks Real-Time Analytics Limited, primarily batch analytics Excels with Structured Streaming and low-latency insights Collaboration Synapse Studio for SQL-based workflows, limited real-time collaboration Collaborative notebooks supporting Python, R, Scala, and SQL with version control Security & Compliance Column-level security, dynamic data masking, encryption, Azure AD integration Role-based access, data encryption, GDPR/HIPAA compliance Pricing Model Pay-as-you-go, separate charges for storage and compute Pay-as-you-go, compute and storage; auto-scaling optimizes costs Best Use Case Structured analytics, business intelligence , reporting Big data processing, machine learning, and real-time analytics
Azure Synapse vs Databricks: Why the Comparison Matters Selecting the right data analytics platform is crucial for your business, as it’s the key to unlocking 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, selecting the right data analytics platform can be the difference between success and failure for your business, particularly due to the associated costs and potential revenue-generating opportunities .
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.
Make the most of Synapse and Databricks with seamless integration. Partner with Kanerika to build scalable, future-ready data solutions.
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FAQs 1. Is Azure Synapse outdated? No, Azure Synapse Analytics is not outdated. It’s constantly evolving, integrating new technologies like serverless SQL pools and Spark capabilities. Instead of being outdated, it’s adapting and expanding to meet modern data warehousing and analytics needs. Think of it as a continuously updated platform rather than a static product.
2. Is Azure Synapse an ETL tool? No, Azure Synapse Analytics is more than just an ETL tool; it’s a comprehensive data integration and analytics service. While it *includes* powerful ETL/ELT capabilities through features like pipelines, it also encompasses data warehousing, data lake capabilities, and serverless SQL pools. Think of it as a complete data management ecosystem, of which ETL is a significant, but not defining, part.
3. What is the difference between Azure and Synapse? Azure is Microsoft’s vast cloud platform offering a wide array of services, from compute and storage to AI and databases. Synapse, in contrast, is *a specific service within Azure*, designed for big data analytics and the integration of data from diverse sources. Think of Azure as the entire city, and Synapse as a specialized high-speed data processing highway within that city. Synapse leverages Azure’s resources but focuses on efficient data warehousing and analytics.
4. Is Synapse better than Databricks? The “Synapse vs. Databricks” question depends entirely on your needs. Synapse excels as a fully integrated Azure service, simplifying management but potentially limiting customization. Databricks offers more flexibility and open-source integration but requires more hands-on management. Ultimately, the best choice hinges on your existing Azure commitment and desired level of control.
5. What is AWS equivalent of Azure Synapse? AWS doesn’t have a single, direct equivalent to Azure Synapse Analytics, which is a unified analytics service. Instead, AWS offers a suite of services like Amazon Redshift, EMR, Glue, and S3 that collectively provide similar functionalities. The best AWS equivalent depends heavily on your specific use case and needs within the data warehousing and analytics space. You’ll need to choose the right combination of services.
6. Which companies use Azure Synapse? Many companies use Azure Synapse, but it’s not typically publicized which *specific* ones. The range is vast, encompassing enterprises of all sizes and industries. Think of it this way: if a company needs powerful, scalable data analytics and warehousing, Synapse is a strong contender, so its user base is extremely diverse. You’ll find them across sectors, from finance and retail to healthcare and manufacturing .
7. Which is better Azure Synapse or Snowflake? The “better” platform between Azure Synapse and Snowflake depends entirely on your specific needs. Synapse integrates tightly with the Azure ecosystem, offering cost advantages if you’re already heavily invested in Microsoft’s cloud. Snowflake excels in its multi-cloud flexibility and powerful, inherently scalable architecture, making it a strong choice for complex, rapidly growing data workloads. Ultimately, a thorough comparison of your data volume, processing needs, and existing infrastructure is crucial for making the right decision.
8. Is Azure Synapse a PaaS or SAAS? Azure Synapse Analytics isn’t strictly PaaS or SaaS; it’s a hybrid. It offers both PaaS capabilities (like serverless SQL pools where you manage the data but not the infrastructure) and SaaS-like features (managed services integrated into the workspace). Think of it as a platform that blends the best of both worlds, giving you flexibility in how much you manage.
Is Azure Synapse being discontinued? Azure Synapse Analytics is not being discontinued. Microsoft continues to actively develop and evolve the platform, adding capabilities like serverless SQL pools, Spark integration, and enhanced security features. Rather than phasing it out, Microsoft is positioning Synapse as a core component of its broader data ecosystem, including Microsoft Fabric. That said, Microsoft has been increasingly directing new investments toward Microsoft Fabric, its next-generation unified analytics platform. Some Synapse features are being absorbed into Fabric, which may give the impression of discontinuation—but existing Synapse workloads remain fully supported. For enterprises already using Azure Synapse for structured analytics, business intelligence, and SQL-based reporting, the platform remains a solid, future-ready choice. Kanerika, a data analytics expert with over two decades of experience, helps organizations navigate these platform decisions and build scalable, future-proof data strategies using both Synapse and Databricks effectively.
Is Azure Synapse used for ETL? Azure Synapse Analytics supports ETL and ELT workflows, but it is not purely an ETL tool it is a comprehensive analytics platform. Synapse Pipelines enables end-to-end ETL/ELT orchestration, allowing teams to move, transform, and load data across sources seamlessly. It also combines data warehousing, big data analytics, and data integration in a single workspace via Synapse Studio. For organizations needing structured data workflows, Power BI reporting, and scalable data pipelines, Synapse provides a unified environment without requiring separate ETL tools. Kanerika helps businesses leverage Azure Synapse’s full capabilities including pipeline orchestration and analytics to build efficient, future-ready data solutions tailored to specific business needs.
Is Databricks part of Synapse? Databricks is not part of Azure Synapse they are separate platforms that serve different purposes. Azure Synapse is Microsoft’s unified analytics service focused on data warehousing and SQL-based analytics, while Databricks is an independent platform built on Apache Spark, specializing in big data processing, machine learning, and real-time pipelines. Although both operate within the Azure ecosystem and can integrate with Azure services like ADLS and Azure Machine Learning, they have distinct architectures and pricing models. Many enterprises actually use both together Synapse for structured BI reporting and Databricks for advanced data science and ML workflows. Organizations like those working with Kanerika often implement this combined approach to maximize analytics capabilities. Choosing between them depends on your specific data needs, existing infrastructure, and team expertise.
What is the difference between Databricks and Azure? Databricks and Azure are fundamentally different in scope. Azure is Microsoft’s comprehensive cloud platform offering hundreds of services across compute, storage, networking, AI, and databases. Databricks is a specialized data and AI platform built on Apache Spark that runs within Azure (and other clouds). Key differences include: Purpose: Azure is a broad cloud infrastructure; Databricks focuses specifically on big data processing, machine learning, and analytics Scope: Azure encompasses everything from virtual machines to IoT; Databricks is purpose-built for data engineering and data science workflows Multi-cloud: Databricks operates across Azure, AWS, and Google Cloud, while Azure is Microsoft-specific Data processing: Databricks uses Apache Spark for high-performance analytics; Azure offers multiple data services including Synapse, HDInsight, and others Think of Azure as the entire technology city and Databricks as a specialized high-performance data lab operating within it. Organizations like those working with Kanerika typically use Databricks on Azure, combining Azure’s infrastructure with Databricks’ advanced analytics capabilities for maximum impact.
What is replacing Azure Synapse? Microsoft Fabric is replacing Azure Synapse Analytics as Microsoft’s next-generation unified data platform. Microsoft announced that Azure Synapse Analytics will be retired, with Microsoft Fabric serving as its successor by consolidating data engineering, data warehousing, real-time analytics, and Power BI into a single SaaS platform. Fabric integrates all the core capabilities Synapse offered—SQL-based warehousing, Spark processing, pipelines—while adding enhanced AI features and a more streamlined experience. Organizations currently using Synapse are encouraged to migrate to Microsoft Fabric or, depending on workloads, consider Azure Databricks for advanced ML and data engineering needs. Kanerika helps enterprises navigate this transition, evaluating whether Microsoft Fabric, Databricks, or a hybrid approach best fits their data strategy before committing to migration.
Why did Synapse fail? Azure Synapse didn’t completely fail, but it faced significant challenges that limited its adoption. Microsoft announced the retirement of certain Synapse features, primarily because Databricks proved more powerful for big data processing, machine learning, and real-time analytics areas where Synapse struggled to compete. Key reasons Synapse underperformed include: Complexity: Its all-in-one approach made it harder to configure and manage compared to specialized tools Performance gaps: Databricks’ Apache Spark engine outperformed Synapse for large-scale data engineering and ML workloads Limited flexibility: Synapse excels at structured SQL analytics but lacks Databricks’ multi-language, multi-framework versatility Market competition: Microsoft Fabric emerged as Microsoft’s next-generation unified analytics platform, essentially replacing core Synapse capabilities Microsoft is now steering customers toward Microsoft Fabric, which absorbs many Synapse features while offering broader capabilities. Organizations like those partnering with Kanerika are navigating this transition strategically, leveraging both Databricks and newer Microsoft tools to build future-ready data solutions.
Is Azure Databricks going away? Azure Databricks is not going away. Microsoft and Databricks have a long-standing strategic partnership, and Databricks continues to be a core part of the Azure ecosystem for big data processing, machine learning, and real-time analytics. In fact, Microsoft has deepened its integration with Databricks rather than retiring it. While Microsoft launched Microsoft Fabric as its own unified analytics platform, Databricks remains a separate, fully supported service on Azure. Many enterprises continue to use both platforms together—Synapse or Fabric for structured BI reporting and Databricks for advanced data engineering and ML workloads. Databricks consistently ranks among the top data and AI platforms globally, with growing enterprise adoption. Its open-source foundation (Apache Spark, Delta Lake, MLflow) and multi-cloud flexibility make it difficult to replace. Organizations evaluating their data stack should treat Databricks as a long-term, viable investment rather than a transitional tool.
Did Microsoft buy Synapse? Microsoft did not buy Synapse Microsoft built Azure Synapse Analytics itself as a native Azure service. It evolved from Azure SQL Data Warehouse, which Microsoft developed internally and rebranded as Azure Synapse Analytics in 2019. It is a first-party Microsoft product, not an acquisition. Azure Synapse is fully integrated into the Microsoft Azure ecosystem, connecting natively with Power BI, Azure Data Lake, and Azure Machine Learning. Because it is a Microsoft-owned platform, enterprises already invested in Azure benefit from seamless compatibility, unified governance, and consolidated billing without managing third-party vendor relationships. This is a key differentiator when comparing Azure Synapse vs Databricks Synapse is a Microsoft-native solution, while Databricks is an independent platform that integrates with Azure but operates as a separate company. Organizations evaluating both platforms should factor in this ownership distinction when planning long-term data strategy and vendor relationships.
What are the limitations of Azure Synapse? Azure Synapse has several notable limitations organizations should consider before adopting it. Key limitations include: Limited ML flexibility – Synapse integrates with Azure ML but lacks the deep, end-to-end machine learning capabilities that Databricks offers via MLflow Weaker multi-cloud support – Synapse is tightly coupled to the Azure ecosystem, making multi-cloud deployments complex Less suited for unstructured data – It’s optimized for structured data and SQL-based analytics, struggling with highly complex, unstructured workloads Performance gaps in real-time processing – Databricks outperforms Synapse for large-scale real-time streaming analytics Customization constraints – Being a fully managed Azure service limits flexibility compared to open-source alternatives Cost at scale – Provisioned SQL pools can become expensive for unpredictable or highly variable workloads Spark performance – Synapse’s Spark pools are less optimized than Databricks’ native Apache Spark engine Organizations needing advanced ML pipelines, multi-cloud flexibility, or heavy real-time processing may find Synapse insufficient alone. Kanerika helps businesses evaluate these trade-offs and architect hybrid solutions combining Synapse and Databricks for maximum efficiency.
Is Azure Synapse like Snowflake? Azure Synapse and Snowflake share similarities but serve different purposes. Both are cloud-based data warehousing platforms offering scalable analytics, but they differ significantly in architecture and ecosystem fit. Synapse integrates deeply within the Microsoft Azure ecosystem, making it cost-effective for organizations already invested in Microsoft tools. Snowflake, however, offers superior multi-cloud flexibility, running across Azure, AWS, and Google Cloud, with a more inherently scalable architecture suited for complex, rapidly growing data workloads. Synapse provides a broader unified analytics experience, combining data warehousing, data lakes, and ETL capabilities, while Snowflake focuses purely on data warehousing excellence. Choosing between them depends on your existing infrastructure, multi-cloud requirements, and data complexity. Partners like Kanerika can help evaluate which platform best aligns with your specific business needs and analytics goals.
What is the old name of Azure Synapse? Azure Synapse Analytics was previously known as Azure SQL Data Warehouse. Microsoft rebranded it to Azure Synapse Analytics in 2019, significantly expanding its capabilities beyond traditional SQL-based data warehousing to include big data analytics, data integration, and unified analytics features. The platform evolved from a standalone SQL data warehouse into a comprehensive analytics service that merges data warehousing, Apache Spark-based big data processing, and data integration into one unified platform. This transformation aligned with modern enterprise needs for handling both structured and unstructured data at scale, as highlighted in the blog’s overview of Azure Synapse’s key capabilities including Synapse Studio, PolyBase technology, and seamless Power BI integration.
Can Azure Synapse do ETL? Yes, Azure Synapse Analytics can perform ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) operations through its built-in Synapse Pipelines feature. However, it’s more than just an ETL tool it’s a comprehensive data integration and analytics platform. Synapse Pipelines enables seamless data workflows by extracting data from multiple sources, transforming it using SQL or Spark, and loading it into data warehouses or data lakes. It supports orchestration, scheduling, and monitoring of complex data pipelines at enterprise scale. Key ETL capabilities in Azure Synapse include: Synapse Pipelines for workflow orchestration PolyBase technology to query across multiple data sources Support for CSV, Parquet, JSON, and ORC formats Integration with Azure Data Lake for scalable storage Organizations looking to build robust ETL pipelines alongside advanced analytics can benefit from expert guidance. Kanerika helps businesses implement Azure Synapse effectively, ensuring scalable, future-ready data integration solutions tailored to specific business needs.
When did Synapse shut down? Azure Synapse Analytics has not shut down. As of 2025, it remains an active Microsoft cloud platform for data warehousing, big data analytics, and business intelligence. The blog content confirms it is a current, fully operational service with ongoing features like Synapse Studio, serverless compute, and Power BI integration. However, Microsoft has been shifting focus toward Microsoft Fabric, its next-generation unified analytics platform, which incorporates many Synapse capabilities. While Synapse itself continues to operate, Microsoft encourages new projects to consider Fabric for long-term scalability. Organizations currently using Azure Synapse can continue doing so without disruption. If you’re evaluating whether to migrate or adopt a new platform, partnering with experts like Kanerika can help you make the right strategic decision based on your specific data and analytics needs.
Is Azure Synapse similar to Databricks? Azure Synapse and Databricks share similarities but serve distinct purposes. Both handle big data analytics and integrate with cloud ecosystems, but their core strengths differ significantly. Synapse is primarily an enterprise data warehousing platform, optimized for structured data, SQL-based analytics, and Business Intelligence through tight Power BI integration. Databricks, built on Apache Spark, focuses on data engineering, machine learning, and real-time streaming pipelines. Key similarities include big data processing capabilities, support for multiple data formats, ETL/ELT workflows, and enterprise-grade security. However, Databricks offers greater flexibility for ML workloads and multi-cloud environments, while Synapse excels within the Azure ecosystem. Many enterprises actually use both together Synapse for BI reporting and structured analytics, Databricks for advanced data science. Organizations like those working with Kanerika often combine both platforms strategically to maximize value across their entire data stack.
