Azure Synapse vs Databricks: 10 Must-Know Differences (2025)

Data is the foundation of modern enterprise innovation—but you need a solid platform to make the most of it. That means being able to handle massive amounts of data, power real-time analytics, and simplify machine learning workflows. There are several platforms out there, but two really stand out for this: Azure Synapse and Databricks. Both are popular, powerful, and live in the cloud, but that's where a lot of the similarity ends. To choose between them, you need to know what each one does best. Databricks is basically Apache Spark supercharged for the cloud. It's built around the "Lakehouse" concept, which combines the benefits of data lakes and data warehouses. On the flip side, Azure Synapse Analytics is Microsoft's all-in-one data analytics service. It combines data warehousing, big data processing, data integration, and data exploration in one place on Azure.

In this article, we will deep dive into an in-depth comparison between Azure Synapse vs Databricks, diving into their features, architectures, ecosystem integration, data processing engines, machine learning features, security, governance, developer experience, pricing breakdown, and more. Let’s dive right in!

Table of Contents

What is Databricks?

Databricks originated from research at the University of California, Berkeley’s AMP Lab and is built on Apache Spark—a fast, open source engine for large‐scale data processing. Founded by the creators of Apache Spark (Ali Ghodsi, Andy Konwinski, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin, and Arsalan Tavakoli-Shiraji), Databricks was established to address enterprise challenges by simplifying complex deployments, enforcing code consistency, and providing dedicated support that standalone Spark environments lacked.

So, what is Databricks? Databricks is a unified platform for data engineering, machine learning, and analytics. It fuses the flexibility of data lakes with the performance of data warehouses into a “lakehouse” architecture, enabling organizations to manage both raw and curated data seamlessly.

Databricks Feature

Databricks offers a range of features and tools for all your data needs, which includes:

1) Data Lakehouse Architecture: Databricks seamlessly combines the scalability of data lakes with the structure and performance of data warehouses to enable efficient management of both raw and curated data.

2) Delta Lake: Databricks also has Delta Lake, which is like a supercharged data lake with ACID transactions, making sure your data is reliable and consistent.

3) Unified Workspace: Databricks offers a collaborative environment where data engineers, scientists, and analysts can work together on projects.

4) Databricks Notebooks: Databricks has interactive notebooks that support multiple languages (Python, R, Scala, and SQL) for code development, data visualization, and documentation.

5) Apache Spark Integration: Databricks is built on Apache Spark, which delivers efficient, distributed processing of large-scale datasets for both batch and streaming applications.

6) Scalability and Flexibility: Databricks can scales compute resources based on workload demands, optimizing performance while controlling costs.

7) ETL and Data Processing Tools: Databricks has robust capabilities for building, scheduling, and monitoring data pipelines and workflows.

8) Machine Learning and AI: Databricks support the entire machine learning lifecycle—from building and training models to deploying them. It also includes MLflow for tracking experiments and managing models.

9) Real-Time Data Processing: Databricks leverages Spark Structured Streaming to process and analyze streaming data in real time.

10) Data Visualization: Databricks connects seamlessly with popular data visualization tools. Users can create interactive dashboards and data visualizations.

11) Security and Compliance: Databricks implements enterprise-grade security features including role-based access control, data encryption (at rest and in transit), and auditing to meet regulatory requirements.

12) Governance with Unity Catalog: Databricks has Unity Catalog built-in, which provides a centralized, unified governance solution for managing data and AI assets across the platform.

13) Multi-Cloud Support: Databricks is available on major cloud platforms such as Azure, AWS, and Google Cloud.

14) Generative AI Capabilities: Databricks offers tools for integrating generative AI applications, allowing businesses to leverage advanced AI capabilities within their data workflows.

...and many more features!

What is Databricks used for?

Databricks is commonly used for:

What makes Databricks stand out is its ability to handle diverse workloads in one place—eliminating the need for separate systems and streamlining your data operations.

Check out this video on Databricks for a complete overview of its capabilities and features.

What is Azure Synapse Analytics?

Azure Synapse Analytics evolved from Microsoft's early cloud data warehousing solutions. It was initially launched as Azure SQL Data Warehouse (SQL DW) in 2016 and designed to overcome the limitations of traditional, siloed storage and compute architectures by decoupling these resources. Microsoft's vision was to unify enterprise data warehousing with big data analytics into a single, integrated platform—ultimately formalized as Microsoft Azure Synapse Analytics.

So, what is Microsoft Azure Synapse Analytics? Microsoft Azure Synapse Analytics is a comprehensive, cloud-native analytics service that combines enterprise data warehousing, big data analytics, data integration, and data exploration within one unified environment. It enables organizations to analyze vast amounts of data using both serverless and dedicated (provisioned) resource models, effectively catering to diverse analytical workloads.

Azure Synapse is designed to streamline the processes of ingesting, preparing, managing, and serving data for business intelligence (BI) and machine learning (ML) applications.

Azure Synapse Analytics uses a distributed query engine for T-SQL, enabling robust data warehousing and data virtualization scenarios. It offers both serverless and dedicated resource models, and it leverages Azure Data Lake Storage Gen2 for scalable, secure data storage. The service also deeply integrates Apache Spark for big data processing, data preparation, data engineering, ETL, and machine learning tasks.

On top of that, Azure Synapse Analytics comes with built in Synapse Studio, a built‑in, web‑based workspace that provides a single environment for data preparation, data management, data exploration, enterprise data warehousing, big data analytics, and AI tasks.

Microsoft Azure Synapse Features

Microsoft Azure Synapse Analytics offers a bunch of features and tools for all your data needs, including:

1) Unified Workspace: Microsoft Azure Synapse Analytics provides a single interface (Synapse Studio) for data ingestion, preparation, exploration, warehousing, and big data analytics.

2) Multiple Compute Models: Microsoft Azure Synapse Analytics offers Dedicated SQL Pools for predictable, high‑performance queries, Serverless SQL Pools for on‑demand, ad hoc analytics and Apache Spark Pools for big data workloads.

3) Massively Parallel Processing (MPP): Microsoft Azure Synapse Analytics utilizes an MPP architecture to distribute query processing across numerous compute nodes, enabling rapid analysis of petabyte‑scale datasets.

4) Apache Spark Integration: Microsoft Azure Synapse Analytics natively integrates with Apache Spark which provides scalable processing for big data, interactive analytics, data engineering, and machine learning workloads.

5) Data Integration Capabilities: Microsoft Azure Synapse Analytics includes native data pipelines—powered by the same integration runtime as Azure Data Factory—to support seamless ETL/ELT operations.

6) Security and Compliance: Microsoft Azure Synapse Analytics features advanced security features:

• Dynamic Data Masking
• Column‑ and Row‑Level Security
• Transparent Data Encryption (TDE) for data at rest
• Integration with Microsoft Entra ID (formerly Azure Active Directory) for authentication and role‑based access control

Also, it offers features like Virtual Network Service Endpoints and Private Link for powerful, secure connectivity.

7) Interoperability with the Azure Ecosystem: Microsoft Azure Synapse Analytics integrates deeply with Azure services like Azure Data Lake Storage, Power BI, Azure Machine Learning, and various other Azure services (like Azure Data Explorer, Logic Apps, and more).

8) Language Flexibility: Microsoft Azure Synapse Analytics supports multiple languages and query engines (T‑SQL, Python, Scala, .Net, and Apache Spark SQL) to suit varied developer and analyst preferences.

...and many more features that extend its capabilities even further.

What is Synapse Analytics used for?

Microsoft Azure Synapse Analytics is commonly used in the following scenarios:

Check out this video on Microsoft Azure Synapse Analytics for a complete overview of its capabilities and features.

Now that we've introduced both Databricks and Microsoft Azure Synapse Analytics, let's dive into our detailed comparison of these two powerful titans.

Azure Synapse vs Databricks—Head-to-Head Feature Showdown

Short on time? Here’s a brief overview of the main differences between Azure Synapse vs Databricks!

Finally, let's dive deeper into the comparison between Azure Synapse vs Databricks.

What Is the Difference Between Databricks and Azure Synapse Analytics?

Let's deep dive into the top ten key features to compare Azure Synapse Analytics and Databricks, helping you select the perfect platform for your requirements.

1️⃣ Azure Synapse vs Databricks—Architecture Breakdown

Azure Synapse Architecture

Azure Synapse Analytics integrates data warehousing, big data analytics, data integration, and enterprise-grade data governance into a unified platform. Its architecture is engineered for high performance, scalability, and flexibility by decoupling compute and storage—enabling independent scaling and optimized cost management.

Here is a detailed breakdown of its architectural components and internal workings. But before we dive into the inner workings, let's briefly review the core architectural components that Azure Synapse Analytics provides.

Core Architectural Components

1) Azure Synapse SQL (Dedicated & Serverless SQL Pools):Azure Synapse SQL is the engine for both traditional data warehousing and on-demand query processing:

a) Dedicated SQL Pools: Dedicated SQL Pools are provisioned with dedicated compute resources measured in Data Warehousing Units (DWUs) and leverage a Massively Parallel Processing (MPP) architecture where:

➥ Control Node: Acts as the entry point that receives T-SQL queries, parses, and optimizes them before decomposing them into smaller, parallel tasks.

➥ Compute Nodes & Distributions: Data is horizontally partitioned—by default into 60 distributions—using methods such as hash, round robin, or replication. Each compute node concurrently processes its assigned distribution(s).

➥ Data Movement Service (DMS): When a query requires data from multiple distributions (for joins or aggregations), DMS efficiently shuffles data between compute nodes to assemble the final result.

b) Serverless SQL Pools:Serverless SQL Pools provide on‑demand query capabilities directly over data stored in Azure Data Lake Storage or Blob Storage. They employ a distributed query processing (DQP) engine that automatically breaks complex queries into tasks executed across compute resources—dynamically scaling without the need for pre‑provisioned infrastructure.

2) Apache Spark Pools:

Azure Synapse integrates an Apache Spark engine as a first‑class component for big data processing, machine learning, and data transformation. The Spark pools:

• Support multiple languages (Python, Scala, SQL, .NET, and R).
• Offer auto‑scaling and dynamic allocation to reduce cluster management overhead.
• Seamlessly share data with Azure Synapse SQL and ADLS Gen2, enabling integrated analytics workflows.

3) Data Integration (Synapse Pipelines)Azure Synapse integrates the capabilities of Azure Data Factory within its workspace, allowing you to build and orchestrate ETL/ELT workflows that can:

• Ingest data from various different sources (over 90 different sources).
• Transform and move data between storage (Azure Data Lake Storage Gen2) and compute layers (SQL or Apache Spark).
• Automate data workflows with triggers, control flow activities, and monitoring built into a unified experience.

4) Data Storage – Azure Data Lake Storage Gen2:

Azure Synapse Analytics uses ADLS Gen2 as its underlying storage layer, which offers:

• Hierarchical file system semantics.
• Scalability and high throughput for both structured and unstructured data.
• Seamless integration with both SQL and Apache Spark engines—enabling direct querying of formats such as Parquet, CSV, JSON, and TSV.

5) Azure Synapse Studio:

Azure Synapse Studio is the unified web-based interface that serves as the development and management environment for the entire Synapse workspace. It offers:

• Integrated authoring tools for SQL scripts, Spark notebooks, and pipelines.
• Monitoring dashboards that display resource usage and query performance across SQL, Apache Spark, and Data Explorer.
• Role‑based access controls integrated with Azure Active Directory for secure collaboration.

Here is how the overall Azure Synapse Analytics works:

➥ Control Node Orchestration:First, whenever a user submits a query (via T‑SQL or notebooks), the control node handles query parsing, optimization, and task decomposition. It formulates an execution plan by analyzing data distribution, available indexes, and workload characteristics. 

➥ Compute Node Processing & Data Distribution:In a dedicated SQL pool, once the control node generates the execution plan, it dispatches multiple parallel tasks to compute nodes. Each compute node processes its local partitioned data (i.e., its distribution) concurrently, leveraging MPP to minimize latency on large datasets.

➥ Data Movement Service (DMS):Now, for operations that require data from different distributions (such as joins, aggregations, or orderings), DMS shuffles data efficiently between compute nodes, ensuring that intermediate results are properly aligned for final result assembly.

➥ Serverless Distributed Query Processing (DQP):In the serverless SQL model, the query engine automatically decomposes a submitted query into multiple independent tasks executed over a pool of transient compute resources. This abstraction removes the burden of infrastructure management from the user while ensuring that the query scales to meet demand.

Azure Synapse Analytics' architectural design not only maximizes performance for large-scale analytics but also ensures that both data engineers and data scientists have the tools they need in a secure, manageable, and highly scalable environment.

Now, let's move on to Databricks' architecture.

Databricks Architecture

Databricks is built on Apache Spark which is designed to run seamlessly on major cloud providers—including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Its architecture decouples compute from storage, enabling elastic scalability, robust security, and streamlined operations. The layered Databricks architecture integrates several core components:

a) Control Plane:

Control plane is fully managed by Databricks and is responsible for all orchestration and administrative tasks, which includes:

• Cluster Management & Job Scheduling: Orchestrates the provisioning, monitoring, auto‑scaling, and lifecycle management of clusters, as well as scheduling batch and streaming jobs.
• User Authentication & Authorization: Integrates with enterprise identity providers (e.g., Azure Active Directory, AWS IAM, Google Identity) and supports multi‑factor authentication and role‑based access control.
• Metadata & Workspace Management: Manages Databricks notebooks, job metadata, cluster configurations, and system logs while providing a web‑based collaborative workspace.
• Configuration & Security Policies: Enforces centralized security controls, compliance measures, auditing, and network security configurations (such as IP access lists and VPC/VNet peering).

Because the control plane is decoupled from user-managed resources, it abstracts infrastructure complexities and allows users to focus solely on their analytics workloads.

b) Compute Plane:

Compute plane is where data processing and analytics tasks are executed. Databricks supports two primary deployment modes:

• Serverless Compute Plane: In this mode, Databricks fully manages compute resources—automatically provisioning and scaling clusters on demand.
• Classic Compute (User-Managed Clusters): In this mode, clusters run within the user’s cloud account, offering enhanced control over configuration, network isolation, and compliance. Workspaces can be configured with dedicated virtual networks to meet strict security and regulatory requirements.

Both modes leverage the underlying Apache Spark engine.

c) Workspace Storage and Data Abstraction:

Each Databricks workspace is integrated with cloud-native storage services, such as an S3 bucket for AWS or Azure Blob Storage for Azure and Google Cloud Storage for Google Cloud Platform (GCP). This storage is utilized for operational data, including notebooks, job run details, and logs. The Databricks File System (DBFS) serves as an abstraction layer that allows users to interact with data stored in these buckets seamlessly. It supports various data formats and provides a unified interface for data access.

Check out this article to learn more in-depth about Databricks architecture.

2️⃣ Azure Synapse vs Databricks—Ecosystem Integration & Cloud Deployment

Now that we've covered the architecture and components of Azure Synapse vs Databricks, let's take a closer look at how they work with other tools and services, and how easy they are to deploy.

Azure Synapse Ecosystem Integration & Cloud Deployment

Azure Synapse lives entirely in the Microsoft Azure ecosystem. Its design leverages a broad suite of native integrations that streamline analytics and data management:

1) Native Connectivity:

• Azure Data Lake Storage
• Power BI
• Azure Machine Learning
• Microsoft Purview

2) Unified Development Environment:

You also get access to a unified portal—Synapse Studio—that lets you build ETL pipelines(via Synapse Pipelines), write queries on both Dedicated SQL Pools (provisioned compute) and serverless SQL pools (on-demand query execution), as well as develop Apache Spark jobs in multiple languages (Python, Scala, SQL, etc.).

3) Integrated Security & Governance:

Synapse leverages Microsoft Entra ID (formerly Azure Active Directory) for identity management, supports Virtual Network (VNet) integration, and enforces security policies consistently across the platform.

Every part of Synapse is built to plug directly into other Azure services, so your data moves smoothly from storage to analysis without extra configuration steps.

☁️ For Azure Synapse Deployment ☁️ 

Azure Synapse Analytics is offered exclusively as a fully managed Platform-as-a-Service (PaaS) within Microsoft Azure.

➥ Azure-First Deployment: As a fully managed Azure PaaS, deploying Synapse is simple. Microsoft handles much of the operational overhead—including scaling, backups, patching, and infrastructure management.

➥ Flexible Compute Options: Choose from dedicated SQL pools for high-performance, predictable workloads or serverless SQL pools that bill per query. In addition, integrated Apache Spark pools empower data science and machine learning workloads within the same environment.

➥ Consistent Performance & Compliance: Because every component is natively built for Azure, you benefit from consistent performance characteristics, unified monitoring, and a cohesive security model aligned with other Azure cloud services.

Databricks Ecosystem Integration & Cloud Deployment

Databricks is designed as a multi-cloud SaaS platform that is purpose-built for big data processing and advanced analytics, with a strong foundation in Apache Spark and Delta Lake. 

➥ Multi-Cloud & Open Architecture: Databricks is available on Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), due to this it allows organizations to avoid vendor lock-in. Despite its multi-cloud nature, each deployment is optimized to leverage the native storage and security features of its host environment).

➥ Built Around Apache Spark & Delta Lake: Databricks extends Apache Spark with Delta Lake—a storage layer that brings ACID transactions, schema enforcement, and time travel to big data workloads.

➥ Integrated Data Science & ML Ecosystem: Databricks seamlessly integrates with MLflow and supports popular libraries, streamlining the development, tracking, and deployment of machine learning models. It also includes features like the Databricks ML Runtime, AutoML, Feature Store, Model Serving, and many more tools to smooth out ML development. Databricks has also introduced Unity Catalog, which further improves data governance across data and AI assets.

➥ Notebooks & Third-Party Integrations: Databricks’ collaborative notebook environment supports multiple languages (Python, Scala, SQL, and R) and integrates with version control systems enabling efficient team collaboration and CI/CD practices.

☁️ For Databricks Deployment ☁️ 

Databricks platform is a managed service that works across multiple clouds. You can set up Databricks clusters on Azure, AWS, or Google Cloud Platform (GCP). Databricks takes care of the underlying infrastructure for you. This means you've got flexibility—it's easier to avoid being tied to one vendor or use different cloud regions. Databricks scales your clusters automatically based on your workload. Pricing is simple: it's based on Databricks Units tied to how much computing power you actually use. That way, you only pay for what you need.

3️⃣ Azure Synapse vs Databricks—Data Processing Engines

Azure Synapse Analytics and Databricks are both highly capable platforms when it comes to data processing. But while they share some similarities, their underlying architectures, strengths, and use cases are actually quite different. Let's take a closer look at what sets the data processing engine in Azure Synapse apart from Databricks.

Azure Synapse Data Processing Engine

Azure Synapse Analytics distinguishes itself by offering a dual-engine architecture, providing specialized engines for different analytical needs. This is a core differentiator from Databricks' single-engine approach. Synapse offers:

1) Azure Synapse SQL Engine

Azure Synapse SQL engine is designed for data warehousing workloads and excels at processing structured data using SQL. It comprises two distinct pool types:

a) Dedicated SQL Pools (formerly SQL Data Warehouse):

Dedicated SQL Pool leverage a Massively Parallel Processing (MPP) architecture.  This architecture is fundamental to their performance and scalability for large-scale data warehousing. Here is the architecture breakdown:

➥ Control Node: Acts as the brain, responsible for query optimization, distribution, and overall orchestration. It receives the SQL query, parses it, and generates an execution plan.

➥ Compute Nodes: These are the workhorses. The control node distributes query execution tasks to multiple compute nodes, which operate in parallel. Each compute node has its own dedicated CPU, memory, and storage.

➥ Data Movement Service (DMS): A critical component for MPP. When a query requires data from different compute nodes, DMS efficiently shuffles data between nodes. This data shuffling is optimized to minimize network latency and maximize parallelism.

➥ Distributed Query Engine (DQE): The engine on each compute node executes its assigned portion of the query against the locally stored data.

b) Serverless SQL Pools:

Serverless SQL Pool executes your queries on-demand. Here is the architecture breakdown:

➥ Metadata-Driven Querying: Serverless SQL Pools don't require pre-provisioned compute. Instead, they dynamically allocate compute resources based on the incoming query. They rely on metadata about your data in ADLS Gen2 (schema, data types, file formats).

➥ Control Node Orchestration: Similar to Dedicated Pools, a control node parses and optimizes the query. But, instead of dispatching to dedicated compute nodes, it leverages a pool of transient compute resources managed by Azure.

➥ Stateless Compute: Compute resources are ephemeral and automatically scaled up or down based on query demands. You only pay for the data processed by your queries.

2) Apache Spark Pools

Azure Synapse also provides integrated Apache Spark Pools, allowing you to leverage the power of Apache Spark for big data processing, machine learning, and real-time analytics within the Synapse ecosystem.

A significant advantage of Synapse is its unified data access and management. Both SQL and Spark engines are tightly integrated with Azure Data Lake Storage Gen2. This architecture offers:

• Data resides in a single, scalable data lake (ADLS Gen2), eliminating data silos and simplifying data governance.
• Data can be seamlessly processed and accessed by both SQL and Spark engines without complex data movement or duplication.
• Azure Synapse Analytics provides a unified metadata catalog across both engines, enhancing data discovery and lineage.

Databricks Data Processing Engine

Databricks takes a single-engine approach, built entirely around Apache Spark.  However, Databricks is far from "just" vanilla Apache Spark. It delivers a highly optimized and managed Spark runtime that significantly enhances performance, reliability, and ease of use.

Databricks Runtime:  Beyond Open Source Spark

Databricks Runtime is the core differentiator of the Databricks platform. It's a performance-optimized runtime engine built on top of Apache Spark, incorporating proprietary enhancements and optimizations. Here are some key optimizations in Databricks Runtime:

➥ Photon Engine (Vectorized Query Engine): Databricks Photon is a native vectorized query engine written in C++ that dramatically accelerates SQL and Dataframe workloads. Photon processes data in columnar format, leveraging vectorized execution to process batches of data simultaneously, leading to significant performance gains (often orders of magnitude faster than standard Spark SQL for certain workloads). Photon is particularly effective for analytical queries with aggregations, filtering, and joins. It automatically integrates with existing Spark APIs and workloads, often requiring no code changes to benefit.

➥ Optimized Spark Execution Engine: Beyond Databricks Photon, the Databricks Runtime includes various other optimizations to the core Spark engine, including:

• Improved Query Optimizer
• Adaptive Query Execution
• Enhanced Shuffle Performance
• Caching Enhancements

➥ Delta Lake Integration (Deep and Native): Databricks is the creator of Delta Lake, an open-source storage layer built on top of data lakes. Delta Lake is deeply integrated into the Databricks Runtime, providing:

• ACID Transactions
• Schema Evolution
• Time Travel (Data Versioning)
• Unified Batch and Streaming Data Processing
• Data Governance and Reliability

TL;DR:

Here's a table summarizing the key technical differences:

4️⃣ Azure Synapse vs Databricks—SQL Capabilities & Data Warehousing

Azure Synapse Analytics and Databricks are two powerful platforms widely used for SQL-based querying and data warehousing, but they have distinct architectures, features, and use cases. Here is a detailed comparison of their SQL and data warehousing capabilities.

But before we dive in lets dive briefly into its architectural foundations:

Azure Synapse is a unified analytics service that integrates enterprise data warehousing with big data and Spark analytics. Its architecture brings together several key components within a single workspace.

➥ Dedicated SQL Pool (MPP Engine):

Dedicated SQL pool is designed for large-scale data warehousing, the dedicated SQL pool employs a massively parallel processing (MPP) architecture. Data is distributed across compute nodes using strategies such as hash distribution, round-robin, or replication. It provides full T‑SQL support, advanced join strategies, aggregations, window functions, and columnstore indexing for high-speed queries.

➥ Serverless SQL Pool: 

For ad hoc querying over data stored in Azure Data Lake Storage Gen2, the serverless SQL pool allows on-demand query processing without the need for pre-provisioned compute, making it ideal for exploratory analytics and intermittent workloads.

Databricks is built atop Apache Spark and embodies the “lakehouse” paradigm—a unified platform that merges data lake flexibility with data warehousing reliability:

➥ Spark SQL & Delta Lake:

SQL endpoints in Databricks run on Spark SQL, leveraging the Catalyst optimizer to transform ANSI SQL into efficient distributed execution plans. The underlying Delta Lake layer provides ACID transactions, schema enforcement, time travel, and data skipping—features that ensure reliable and performant operations even over a data lake.

➥ Cluster Management & Tuning:

Unlike Synapse’s managed SQL pools, optimal performance in Databricks often requires manual tuning of cluster configurations (such as executor memory and parallelism) to match workload characteristics.

Azure Synapse SQL Capabilities

➥ Full T‑SQL Support: Azure Synapse’s dedicated SQL pools use T-SQL as their query language. The engine is optimized with cost-based query optimization techniques, supporting features like advanced join strategies, aggregations, and window functions.

➥ Indexing & Distribution: Columnstore indexes (often clustered) and data distribution strategies help accelerate scan and join operations on large, partitioned tables. PolyBase allows external table definitions over data stored in Azure Blob or Data Lake Storage, enabling seamless querying of both internal and external data sources.

➥ Workload Management: Databricks has built-in workload management and resource classes which allow fine-tuning of concurrency and query performance, which is crucial in high-concurrency, enterprise-scale data warehousing environments.

Databricks SQL Capabilities

➥ Catalyst Optimizer: Databricks leverages Spark SQL’s Catalyst optimizer, which applies rule-based and cost-based optimizations to transform logical plans into highly optimized physical execution plans. Techniques like predicate pushdown, dynamic partition pruning, and vectorized reading are essential in improving query performance.

➥ Delta Lake Enhancements: Delta Lake’s transaction log ensures ACID properties and supports optimizations such as data skipping and Z-order clustering, which are critical for performance when dealing with large, frequently updated datasets.

➥ Cluster Tuning: Unlike Synapse’s managed SQL pools, achieving optimal performance in Databricks often requires careful tuning of cluster configurations (executor memory, parallelism) to match the workload’s characteristics.

Azure Synapse Data Warehousing Capabilities

➥ Purpose-Built MPP Data Warehouse: The dedicated SQL pool is architected to serve as a high-performance data warehouse. Its design ensures predictable performance with enterprise features such as query result caching, concurrency scaling, and integrated data distribution.

➥ Separation of Compute and Storage: Synapse allows independent scaling by decoupling compute (provisioned via SQL pools) from storage (typically in Azure Data Lake Storage Gen2), which is vital for managing cost and performance in data warehousing workloads.

➥ Enterprise Security & Governance: Synapse offers dynamic data masking, row-level security, and Azure Active Directory (AAD) integration. Its connection with Azure Purview enhances data lineage and governance.

Databricks Data Warehousing Capabilities

➥ Delta Lake as the Foundation: Delta Lake redefines data warehousing by enabling a “warehouse on a data lake”, supporting schema evolution, time travel, and ACID transactions atop scalable storage.

➥ Unified Analytics: Databricks SQL Analytics provides interactive SQL querying and dashboarding, bridging big data processing with BI workflows.

➥ Workload Versatility: Databricks excels in hybrid workloads combining SQL querying with advanced analytics, data science, and machine learning. However, for ultra-low-latency, high-concurrency scenarios typical of traditional MPP warehouses, additional tuning (e.g., caching, partitioning) is required.

5️⃣ Azure Synapse vs Databricks—Machine Learning and Analytics

Azure Synapse vs Databricks both support machine learning, but they approach it differently.

Azure Synapse Machine Learning and Analytics

Azure Synapse handles machine learning with Synapse ML which simplifies scalable ML pipelines for tasks like text analytics or document parsing. It integrates with Azure Machine Learning for model training and deployment, though you’ll need extra setup—like managed endpoints—for secure workflows. For analytics, you get dedicated and Serverless SQL Pools for querying, plus Apache Spark for big data. Power BI hooks in tight, making it a perfect pick if you’re already deeply rooted in Microsoft’s ecosystem. It’s flexible—scale up or down as needed—and handles petabyte-scale data, relational or not.

Databricks Machine Learning and Analytics

Databricks brings Mosaic AI to the table, a full-on ML platform covering data prep, model building, and monitoring. It also supports various libraries like TensorFlow, PyTorch, and Ray, with pre-configured GPU access for heavy lifting. You’vealso got MLflow for tracking experiments, a feature store for managing features, and Model Serving for deploying models, even LLMs, with ease. Analytics runs on an optimized Apache Spark engine, with SQL support and a collaborative workspace for teams—think notebooks in Python, R, or Scala. Visualization’s built-in, and it scales across clouds like AWS or Azure. It’s less tied to one ecosystem, giving you room to maneuver.

Databricks wins on machine learning with a slick, all-in-one setup and broader framework and tool support. Azure Synapse shines in analytics if you’re hooked on Power BI and Microsoft’s ecosystem. Pick based on your priorities.

6️⃣ Azure Synapse vs Databricks—Scalability & Resource Management

When you're working with data, you need systems that can grow when your work gets bigger and shrink when it gets smaller. This is scalability. Both Azure Synapse Analytics and Databricks are powerful cloud-based platforms designed for big data processing and analytics, but they approach scalability and resource management in distinct ways.

Azure Synapse: Pools and Planning

Azure Synapse Analytics provides a unified analytics service that includes data warehousing, integration, and big data processing. Its scalability and resource management methodology are distinguished by granular control and a unified management interface within Synapse Studio.

Dedicated SQL Pools:

In Synapse Dedicated SQL Pools data is distributed across compute nodes, allowing for parallel query processing across vast datasets.

➥ Scalability in Dedicated SQL Pools is measured in Data Warehouse Units (DWUs) or the newer Compute Data Warehouse Units (cDWUs). These units abstractly represent compute, memory, and IO resources. Scaling up or down is achieved by adjusting the DWU/cDWU setting—increasing them provides more compute power for faster query performance and handling larger workloads.

➥ You can manually scale DWUs/cDWUs via the Azure portal, Azure CLI, or programmatically to match workload demands. Also, Dedicated SQL Pools offer elasticity —the ability to pause the compute pool when not in use, significantly reducing costs, and resume it quickly when needed.

➥ Synapse Dedicated SQL Pools include robust Workload Management features. You can define Workload Classifiers to categorize incoming queries based on user, importance, or source. Workload Groups then allocate resources (CPU, memory, concurrency) to these classifications, ensuring performance predictability and preventing resource contention between different types of workloads or users.

Serverless SQL Pools:

Synapse Serverless SQL Pools provide a truly serverless query engine for data lake exploration and ad-hoc analysis.

➥ You don't provision or manage any infrastructure. Serverless SQL Pools automatically scale based on query complexity and data volume. The cost is based on data processed by your queries, not on compute uptime.

➥ The cost model for Synapse Serverless SQL Pools requires attention. Inefficient queries that process large amounts of data can become expensive. Optimizing queries and data formats becomes important for cost management.

➥ You have less direct control over the underlying compute resources. Serverless SQL Pools prioritize ease of use and automatic scaling for data exploration and reporting rather than fine-grained performance tuning of the compute infrastructure itself.

Apache Spark Pools:

Synapse Apache Spark Pools provide a managed Apache Spark environment integrated within Synapse Analytics.

➥ Spark Pools utilize the standard Spark architecture with a driver node and worker nodes (executors). Scaling involves increasing the number of executors within the defined cluster node limits.

➥ You configure autoscaling by setting minimum and maximum node counts for the Spark cluster. You can also define parameters like idle time before scaling down and choose between aggressive or conservative scaling behaviors to optimize for cost or performance.

➥ Synapse Spark Pools allow you to choose different Azure Virtual Machine instance types optimized for various Spark workloads, such as memory-optimized instances for data-intensive tasks or compute-optimized instances for CPU-bound computations.

Databricks: Dynamic Cluster Control

Databricks is a platform deeply rooted in Apache Spark. Its scalability and resource management are centered around dynamic clusters and intelligent performance optimizations.

Spark Clusters:

Databricks clusters are the core compute unit and are built upon Apache Spark. They are designed for dynamic autoscaling to efficiently handle fluctuating workloads.

➥ You define a minimum and maximum number of worker nodes when creating a Databricks cluster. The platform automatically scales the cluster up or down in real-time based on the current processing demand.

➥ Databricks offers distinct cluster types: Interactive Clusters are designed for interactive development, data exploration in notebooks, and collaborative work. Job Clusters are optimized for running automated, production-ready jobs. Job clusters can be configured to terminate automatically after job completion, further optimizing costs.

➥ Databricks provides access to a vast selection of instance types across major cloud providers (Azure, AWS, GCP). You can choose highly specialized instances optimized for memory, compute, GPU acceleration, and storage, tailoring the cluster infrastructure precisely to the needs of your Spark workloads.

A key differentiator in Databricks' resource management is the Photon engine—a vectorized, native-code execution engine compatible with the Apache Spark API. It's designed to significantly accelerate query performance, particularly for larger datasets and complex operations. Photon indirectly optimizes resource utilization and reduces costs by shortening compute times. This makes Databricks more cost-effective for demanding Spark workloads.

On top of that, Databricks also offers workload management features to control resource allocation and ensure fairness within a Databricks Workspace. This includes the Fair Scheduler in Spark to manage resource sharing between jobs, and Cluster Policies which allow users to enforce constraints on cluster configurations.

Choose Azure Synapse Analytics if:

• Your primary need is for a robust data warehouse with predictable performance and workload management.
• You require a single analytics platform that includes data warehousing, integration, and exploration.
• You are heavily invested in the Azure ecosystem.
• You need granular control over data warehouse compute resources and workload prioritization.
• You have diverse workloads, including SQL-centric data warehousing and Spark-based processing, and want a single platform to manage them.

Choose Databricks if:

• Your workloads are primarily Spark-based, focused on data engineering, data science, and machine learning.
• You need highly dynamic and automated scalability for Spark workloads that fluctuate significantly.
• Performance optimization for Spark is critical, and you want to leverage the benefits of the Databricks Photon engine.
• You value a collaborative environment optimized for data science and engineering teams.
• You need flexibility across cloud providers.
• Cost optimization for Spark workloads is a major focus.

7️⃣ Azure Synapse vs Databricks—Real-Time Streaming & Data Ingestion

Azure Synapse and Databricks both support real‑time streaming and data ingestion—but they approach the challenge from distinct architectural and operational standpoints.

Azure Synapse Streaming Ingestion

Azure Synapse is primarily architected as a unified analytics service that excels in large‑scale data warehousing and batch processing. It integrates with tools such as Azure Data Factory and Azure Stream Analytics for orchestrating data ingestion workflows. Although Synapse offers Apache Spark pools that support Spark Structured Streaming, these pools are generally optimized for batch and ad‑hoc processing rather than continuous, low‑latency streaming. In practice, real‑time ingestion in Synapse is typically managed via Synapse Pipelines or by leveraging external services (such as Azure Stream Analytics (ASA)) to feed data into dedicated or serverless SQL pools for near‑real‑time querying. This model is ideal when streaming is just one component of a broader enterprise analytics strategy that leverages the full Azure ecosystem.

Databricks Streaming Ingestion

Databricks is built on Apache Spark and Delta Lake, and its real‑time streaming capabilities are centered on Spark Structured Streaming. Databricks supports conventional micro‑batch processing as well as continuous processing modes—with configurable trigger intervals (down to 500 ms in continuous mode, noting that continuous processing is still evolving in some contexts)—to achieve near‑real‑time performance. The integration with Delta Lake introduces robust ACID transactional guarantees, time travel, and schema evolution, which are essential for managing streaming data reliably. Furthermore, Databricks offers additional features that streamline real‑time ingestion:

1) Databricks Auto Loader:

Databricks Auto Loader watches your cloud storage (e.g. Azure Blob Storage or ADLS) for new files and loads them incrementally. It maintains an internal state to avoid re‑processing files and offers configuration options such as file notification and incremental directory listing, thereby simplifying ingestion from data lakes.

2) Delta Live Tables (DLT):

Delta Live Tables (DLT) provide a managed framework for building streaming pipelines with built‑in support for schema evolution, data quality checks, and automated checkpointing. DLT runs continuous or triggered streaming jobs on Delta Lake, leveraging Structured Streaming under the hood to simplify operational management and enhance pipeline reliability.

8️⃣ Azure Synapse vs Databricks—Security, Governance & Data Cataloging

Now let's deep dive into the Security, Governance & Data Cataloging of Azure Synapse vs Databricks.

Azure Synapse Security

Azure Synapse provides robust security by leveraging Azure’s advanced network controls and identity management infrastructure:

➥ Network Security: You can deploy Azure Synapse into a managed Virtual Network (VNet) with private endpoints, ensuring data stays within a secure perimeter. Firewall rules allow you to restrict access, and public network access to Synapse Studio can be disabled for enhanced isolation.

➥ Data Encryption: Data at rest is safeguarded with 256‑bit AES encryption, typically implemented via Transparent Data Encryption (TDE) in Dedicated SQL Pools, with support for customer-managed keys in Azure Key Vault. Data in transit is encrypted using TLS v1.2 or higher, adhering to modern security standards.

➥ Identity and Access Management: Azure Synapse integrates seamlessly with Microsoft Entra ID (formerly Azure Active Directory) for centralized identity management and implements role-based access control (RBAC). It also supports advanced features like row-level security (RLS) and column-level security (CLS) in Dedicated SQL Pools for granular access control.

➥ Threat Monitoring: Integration with Microsoft Defender for Cloud provides real-time activity monitoring, detecting threats such as SQL injection attempts, anomalous access patterns, and authentication failures.

➥ Compliance: Azure Synapse aligns with standards like GDPR, HIPAA, and SOC 2, supported by comprehensive audit logging and compliance certifications.

Databricks Security

Databricks implements security using multiple layers. A key component is Unity Catalog, which centralizes governance and enforces fine‑grained permissions at the catalog, schema, table, and column levels. Databricks supports integration with external identity providers, ensuring that access is consistently managed. Data is encrypted at rest using server‑side encryption—with the option for customer-managed keys—and in transit via TLS. On top of that, you can integrate security features provided by various cloud services in Databricks.

Azure Synapse Governance

Azure Synapse Analytics achieves enterprise-grade governance through integration with Microsoft Purview.

Purview scans and classifies data assets in your Synapse workspace, automatically registering metadata, lineage, and data classification details. In addition, Synapse’s native capabilities—like built-in data discovery and classification within Dedicated SQL Pools—help identify sensitive data and capture audit logs.

Databricks Governance

In Databricks, Unity Catalog not only drives security but also serves as the unified governance layer for your lakehouse. It centrally manages data assets—primarily Delta tables, views, and files—as well as machine learning models. With granular permission controls, automated lineage tracking, and detailed audit logging, Unity Catalog streamlines policy administration and ensures that governance practices are applied uniformly across both structured and unstructured data.

Azure Synapse Data Cataloging

For data cataloging, Azure Synapse Analytics relies on its native metadata management capabilities and integration with Microsoft Purview. In Dedicated SQL Pools, built-in data discovery and classification automatically registers metadata for tables, files, and other assets. When linked with Microsoft Purview, these assets are aggregated into a centralized data catalog that spans your enterprise, enabling efficient data discovery and assessment. This unified metadata repository enhances visibility and helps meet compliance requirements.

Databricks Data Cataloging

Databricks leverages Unity Catalog as its native data catalog, automatically collecting and organizing metadata for Delta tables, files, and other assets within your lakehouse. The hierarchical namespace—comprising catalogs, schemas, and tables/views—ensures consistent data management and searchability. Unity Catalog also tracks data lineage and audit information, providing clear visibility into data flows and modifications over time—an essential capability for robust governance and regulatory compliance.

9️⃣ Azure Synapse vs Databricks—Developer Experience & Notebooks

Now lets deep dive intothe technical comparison between Azure Synapse Analytics and Databricks focused on their developer experience and notebooks capabilities.

Azure Synapse Developer Experience & Notebooks

Azure Synapse provides web‐based notebooks experience embedded as part of Synapse Studio. Developers can write code in multiple languages—PySpark (Python), Scala, Spark SQL, .NET (C#), and SparkR—in a single interface. While Synapse Notebooks support Git integration (with Azure DevOps or GitHub), collaboration is largely “file-based” rather than truly real-time co-authoring. Changes are versioned, but simultaneous editing is less fluid compared to modern IDEs.

Synapse notebooks offer rich features such as a variable explorer (for Python), integrated magic commands, and an editor powered by the Monaco engine (providing IntelliSense, code completion, syntax highlighting, and error markers). They also integrate seamlessly with Spark pools (both serverless and provisioned) and can be embedded within pipelines for orchestration.

Databricks Developer Experience & Notebooks

Databricks is known for its robust notebook environment. Its notebooks supports real-time coauthoring across languages (Python, SQL, Scala, and R). The recent next-generation UI streamlines the interface with features like enhanced code navigation, inline visualizations, and contextual AI-assisted code suggestions (Databricks Assistant)

Databricks notebooks are natively integrated with Git repositories with Databricks Repos. This integration enables branching, pull requests, and CI/CD workflows directly from the workspace.

Databricks notebooks now offer advanced debugging tools, step-through debugging, inline error highlighting, and “go to definition” capabilities. They also support interactive visual output (e.g., charts and widgets) and code snippets that accelerate development and make exploratory data analysis more efficient.

🔟 Azure Synapse vs Databricks—Pricing Breakdown

Finally, we have reached the end of the article. Now, let's deep dive into the pricing breakdown between Azure Synapse and Databricks.

Azure Synapse Pricing Breakdown

Azure Synapse pricing model is segmented across multiple components to address diverse workload requirements—from pre-purchase savings to advanced big data analytics. Here is the detailed pricing breakdown for each component:

Note that all prices are estimates in US dollars for the US East 2 region and are quoted on a monthly basis—actual pricing may vary with your agreement, purchase timing, or regional/currency differences.

1) Pre-Purchase Plans

If you are planning to get a predictable Azure Synapse consumption, pre-purchase plans offer significant cost savings. Azure Synapse Analytics Commit Units (SCUs) are pre-purchased blocks of consumption that can be used across most Synapse services (excluding storage). If you commit to a certain level of usage, you unlock tiered discounts over the standard pay-as-you-go pricing. Here are the pricing details:

Note: Purchased SCUs are valid for 12 months and can be consumed across various Azure Synapse services at their respective retail prices until the SCUs are exhausted or the term ends.

2) Data Integration Pricing: Pipelines and Data Flows

Azure Synapse Analytics provides robust data integration capabilities to build hybrid ETL and ELT pipelines. Pricing for data integration is based on several components.

a) Data Pipelines

Data Pipelines are the backbone of data integration in Synapse, orchestrating and executing data movement and transformation activities. Pricing is determined by activity runs and integration runtime hours.

b) Data Flows 

Data Flows in Azure Synapse offer a visually driven interface for building complex data transformations at scale. Pricing is based on cluster execution and debugging time, charged per vCore-hour.

Note: Data Flows require a minimum cluster size of 8 vCores for execution. Execution and debugging times are billed per minute and rounded up.

c) Operation Charges

Beyond execution costs, Data Pipeline operations such as creation, reading, updating, deletion, and monitoring also contribute to the overall data integration cost.

Note: The first 1 million operations per month are free. After exceeding the free tier, operations are charged at a fixed rate per 50,000 operations.

3) Data Warehousing

Azure Synapse Analytics caters to diverse data warehousing needs with both serverless and dedicated SQL pool options. This dual approach allows users to optimize costs and performance based on workload characteristics.

a) Serverless SQL Pool

Serverless SQL pools enable querying data directly within your Azure Data Lake Storage without the need for upfront resource provisioning. This pay-per-query model is ideal for ad-hoc analysis and data exploration workloads. Here is the pricing breakdown: 

Pricing is solely based on the volume of data processed by each query. Data Definition Language (DDL) statements, which are metadata-only operations, do not incur any charges. A minimum charge of 10 MB per query applies, and data processed is rounded up to the nearest 1 MB.

Note that this pricing is specifically for querying data. Storage costs for the Azure Data Lake Storage itself are billed separately according to Azure Data Lake Storage pricing.

b) Dedicated SQL Pool

Dedicated SQL pools, formerly known as SQL DW, provide reserved compute resources designed for intensive data warehousing workloads demanding high query performance and predictable scalability. Pricing for Dedicated SQL Pools offers pay-as-you-go and reserved capacity options.

Dedicated SQL Pool Pay-as-you-go Pricing (Monthly)

• DWUs are a measure of compute resources allocated to the Dedicated SQL pool. Higher DWUs provide more compute power and are suitable for demanding workloads.
• Dedicated SQL pools include adaptive caching to optimize performance for workloads with consistent compute requirements.

Dedicated SQL Pool Reserved Capacity Pricing (Monthly)

c) Data Storage, Snapshots, Disaster Recovery, and Threat Detection for Dedicated SQL Pools

Beyond compute costs, Dedicated SQL Pools also have associated charges for data storage, disaster recovery, and security features.

• Data Storage & Snapshots: Data storage costs include the size of your data warehouse plus 7 days of incremental snapshots for data protection and recovery. Storage transactions are not billed; you only pay for the volume of data stored.
• Geo-redundant Disaster Recovery: For business continuity, geo-redundant disaster recovery replicates your data warehouse to a secondary region. This incurs an additional cost per GB per month for the geo-redundant storage.
• Azure Defender for SQL: For more enhanced security, Azure Defender for SQL provides threat detection capabilities. The pricing is aligned with Azure Security Center Standard tier, billed per protected SQL Database server (node) per month. A 60-day free trial is available. See Microsoft Defender for Cloud pricing for more details.

4) Big Data Analytics Pricing: Apache Spark Pools

Azure Synapse Analytics incorporates Apache Spark pools for large-scale data processing tasks such as data engineering, data preparation, and machine learning. Apache Spark pool usage is billed per vCore-hour.

• Memory-optimized pools are suitable for general-purpose Apache Spark workloads
• GPU-accelerated pools are designed for computationally intensive tasks, particularly in machine learning.

Note: Apache Spark pool usage is billed per minute, rounded up to the nearest minute.

5) Log and Telemetry Analytics (Azure Synapse Data Explorer)

Azure Synapse Data Explorer is optimized for interactive exploration of time-series, log, and telemetry data. Its decoupled compute and storage architecture allows for independent scaling and cost optimization.

Note: Azure Synapse Data Explorer billing is rounded up to the nearest minute.

6) Azure Synapse Link

Azure Synapse Link bridges operational data with analytics—eliminating time‑consuming ETL processes. Here is the pricing details of Azure Synapse Link for SQL, Azure Synapse Link for Cosmos DB, and Azure Synapse Link for Dataverse.

a) Azure Synapse Link for SQL

Azure Synapse Link for SQL can automatically move data from your SQL databases without time-consuming extract, transform, and load (ETL) processes. Here is the pricing detail:

b) Azure Synapse Link for Cosmos DB

Pricing for Synapse Link for Cosmos DB is based on analytical storage transactions within Azure Cosmos DB. See Azure Cosmos DB pricing for detailed pricing.

c) Azure Synapse Link for Dataverse

Azure Synapse Link for Dataverse is included with Microsoft Power Platform and certain Microsoft 365 licenses, offering value-added analytical capabilities for users of these platforms. See licensing overviews for specific details.

Databricks Pricing Breakdown

Databricks employs a consumption-based pricing model where users pay only for what they use. At its core lies the Databricks Unit (DBU), which aggregates compute resources—including CPU, memory, and I/O—to run workloads. Here is a detailed breakdown on how DBUs are priced and details the cost structures across Databricks’ key products.

Databricks pricing model is built on a pay‑as‑you‑go basis. Costs are calculated by multiplying the number of DBUs consumed by the applicable DBU rate. The DBU cost varies according to several factors such as cloud provider, region, edition, instance type, compute workload, and any committed usage contracts.

Formula for Cost Calculation:

Databricks DBU Consumed × Databricks DBU Rate = Total Cost

DBU rate is influenced by several factors:

• Cloud Provider & Region: Different providers (AWS, Azure, GCP) and regions incur distinct DBU rates.
• Databricks Edition: Standard, Premium, and Enterprise editions offer tiered pricing—with Enterprise typically at the highest cost.
• Instance & Compute Type: DBU rates vary with instance types (memory‑ or compute‑optimized) and whether the workload uses standard or serverless compute.
• Committed Use: Long‑term capacity commitments can yield discounts proportional to reserved capacity.

Try Before You Buy—Databricks Free Trial

Databricks provides a 14-day free trial on AWS, Azure, and Google Cloud Platform (GCP), allowing users to explore its full range of features, including Apache Spark, MLflow, Delta Lake, and Unity Catalog, without any upfront cost.

Also, Databricks offers the Community Edition, a free, limited-feature version that includes a small Apache Spark cluster and a collaborative Databricks Notebook environment—perfect for learning Apache Spark, experimenting with Databricks Notebooks, and testing basic workloads.

1) Databricks Pricing for Jobs

Databricks Jobs facilitate production ETL workflows by auto‑scaling clusters to match workload needs. Databricks Jobs pricing is available in two main models: Classic/Classic Photon Clusters and Serverless (Preview).

a) Classic/Classic Photon Clusters

Classic and Classic Photon clusters provide a massively parallelized environment for demanding data engineering pipelines and large-scale data lake management. Pricing is DBU-based, varying by Databricks plan and cloud provider.

b) Serverless (Preview)

Serverless Jobs offer a fully managed, elastic platform for job execution, including compute costs in the DBU price.

2) Databricks Pricing for Delta Live Tables

Delta Live Tables (DLT) simplifies the creation of reliable and scalable data pipelines using SQL or Python on auto-scaling Apache Spark. DLT pricing is based on Jobs Compute DBUs and tiered by features: DLT Core, DLT Pro, and DLT Advanced.

DLT Core

For basic scalable streaming/batch pipelines in SQL/Python

DLT Pro

Adds Change Data Capture (CDC) handling.

DLT Advanced

Includes data quality expectations and monitoring.

3) Databricks SQL Pricing

Databricks SQL is optimized for interactive analytics on massive datasets within the lakehouse architecture. It enables high‑performance SQL querying without the need for data movement. Databricks SQL pricing comes in SQL Classic, SQL Pro, and SQL Serverless options.

AWS Databricks Pricing (US East (N. Virginia)):

Premium plan:

• SQL Classic: $0.22 per DBU (Databricks Unit)
• SQL Pro: $0.55 per DBU
• SQL Serverless: $0.70 per DBU (includes cloud instance cost)

Enterprise plan:

• SQL Classic: $0.22 per DBU
• SQL Pro: $0.55 per DBU
• SQL Serverless: $0.70 per DBU (includes cloud instance cost)

Azure Databricks Pricing (US East (N. Virginia)):

Premium Plan (Only plan available):

• SQL Classic: $0.22 per DBU
• SQL Pro: $0.55 per DBU
• SQL Serverless: $0.70 per DBU (includes cloud instance cost)

GCP Databricks Pricing:

Premium Plan (Only plan available):

• SQL Classic: $0.22 per DBU
• SQL Pro: $0.69 per DBU
• SQL Serverless (Preview): $0.88 per DBU (includes cloud instance cost)

5) Databricks Pricing for Data Science & ML

Databricks supports full‑cycle data science and machine learning workloads with collaborative notebooks, MLflow, and Delta Lake integration. Pricing here reflects the cost of running interactive and automated ML workloads.

Databricks offers pricing options for running data science and machine learning workloads, which vary based on the cloud provider (AWS, Azure, or Google Cloud Platform) and the chosen plan (Standard, Premium, or Enterprise).

AWS Databricks Pricing (AP Mumbai region):

Premium plan:

• Classic All-Purpose/Classic All-Purpose Photon clusters: $0.55 per DBU
• Serverless (Preview): $0.75 per DBU (includes underlying compute costs; 30% discount applies starting May 2024)

Enterprise plan:

• Classic All-Purpose/Classic All-Purpose Photon clusters: $0.65 per DBU
• Serverless (Preview): $0.95 per DBU (includes underlying compute costs; 30% discount applies starting May 2024)

Azure Databricks Pricing (US East region):

Standard Plan:

• Classic All-Purpose/Classic All-Purpose Photon clusters: $0.40 per DBU

Premium Plan:

• Classic All-Purpose/Classic All-Purpose Photon clusters: $0.55 per DBU
• Serverless (Preview): $0.95 per DBU (includes underlying compute costs; 30% discount applies starting May 2024)

GCP Databricks Pricing:

Premium Plan (Only plan available):

• Classic All-Purpose/Classic All-Purpose Photon clusters: $0.55 per DBU

6) Databricks Pricing for Model Serving

Databricks Model Serving allows for low-latency, auto-scaling deployment of ML models for inference, enabling integration with applications. Pricing varies based on serving type and Databricks plan, and includes cloud instance costs.

Model Serving and Feature Serving

GPU Model Serving

Databricks also provides a pricing calculator tool to help estimate costs based on your specific use case, service selections, and anticipated workload.

Check out this article to learn more in-depth about Databricks pricing.

Azure Synapse vs Databricks—Pros & Cons

Azure Synapse pros and cons:

Azure Synapse Pros:

• Microsoft Azure Synapse Analytics offers deep integration with the Azure ecosystem and robust enterprise security features.
• Microsoft Azure Synapse Analytics delivers full T-SQL support
• Microsoft Azure Synapse Analytics provides high-performance data warehousing via Dedicated SQL Pools that scale to petabytes of data.
• Microsoft Azure Synapse Analytics includes cost-effective, serverless SQL Pools for ad hoc querying and efficient data lake exploration.
• Microsoft Azure Synapse Analytics features a unified Synapse Studio that centralizes management of SQL scripts, notebooks, data pipelines, and integration with Power BI.
• Microsoft Azure Synapse Analytics offers Data Explorer for efficient log and telemetry analytics, enhancing monitoring and troubleshooting.
• Microsoft Azure Synapse Analytics leverages Azure Active Directory, role-based access, and data encryption, the service helps you manage sensitive data in line with various standards like GDPR and HIPAA.

Azure Synapse Cons:

• Microsoft Azure Synapse Analytics incorporates Apache Spark integration; however, its Apache Spark environment is not as optimized as Databricks’ offering.
• Microsoft Azure Synapse Analytics focuses primarily on the Azure ecosystem, providing less multi-cloud flexibility compared to Databricks.
• Microsoft Azure Synapse Analytics delivers less advanced machine learning and real-time streaming capabilities when compared with Databricks.
• Microsoft Azure Synapse Analytics notebook environment lacks automatic versioning, which can complicate collaboration and code tracking.
• Microsoft Azure Synapse Analytics can be more complex to navigate, presenting a steeper learning curve for new users.
• Microsoft Azure Synapse Analytics serverless SQL Pools may experience performance limitations under heavy or unpredictable workloads.
• Microsoft Azure Synapse Analytics has some limits on file sizes and certain table operations. If you work with extremely large files or specific data types, you might have to adjust your workflow or partition your data more carefully.
• Microsoft Azure Synapse Analytics has a complex pricing model that requires careful monitoring to manage costs effectively.

Databricks pros and cons:

Databricks Pros:

• Databricks implements Lakehouse architecture with Delta Lake, providing ACID transactions, schema enforcement, and time travel for data reliability.
• Databricks integrates MLflow natively for model tracking, experiment management, and streamlined MLOps.
• Databricks supports multi-cloud deployments (AWS, Azure, Google Cloud).
• Databricks provides a notebook environment with real-time co-authoring and automatic versioning, enhancing collaborative development.
• Databricks utilizes the Photon engine to accelerate SQL query performance through vectorized processing.
• Databricks offers advanced real-time streaming and incremental data ingestion capabilities via structured streaming and Delta Lake.
• Databricks supports multiple programming languages (Python, R, Scala, SQL) with seamless integration and interactive visualization tools.
• Databricks features automated cluster management and auto-scaling, optimizing resource utilization and reducing operational overhead.

Databricks Cons:

• Databricks centers on Apache Spark; non-Spark workloads require additional integration work or custom connectors.
• Databricks lacks native support for traditional SQL data warehousing (e.g., T-SQL) compared to dedicated SQL DW platforms.
• Databricks cost models are variable and can be unpredictable due to dynamic cluster scaling and on-demand compute usage.
• Databricks demands deep technical expertise in Apache Spark tuning and cluster optimization for peak performance.
• Databricks may require custom solutions for integrating legacy systems and non-Spark-specific data pipelines.
• Databricks has less out-of-the-box support for OLTP workloads and other transactional scenarios.

Further Reading

• Azure Synapse Analytics Documentation
• Databricks documentation
• Databricks Competitors: 13 Best Alternatives to Try
• Databricks Pricing 101
• Intro To Databricks - What Is Databricks
• Tutorial - Databricks Platform Architecture
• Getting Started in Azure Synapse Analytics | Azure Fundamentals
• Why you should look at Azure Synapse Analytics!

Conclusion

And that's a wrap! Microsoft Azure Synapse Analytics and Databricks address different aspects of modern data architectures with highly specialized capabilities. Azure Synapse Analytics is an all-in-one analytics platform that combines dedicated SQL pools, serverless SQL pools, and Apache Spark pools. All of these components work under a single governance and interface smoothly with other Azure services, making Synapse a good choice for modernizing legacy data warehouse systems and handling structured and semi-structured data.

In contrast, Databricks, built on Apache Spark, focuses on data engineering and data science. Its key feature is Delta Lake, a storage layer that offers robust ACID transaction guarantees, enforces schemas, and provides time travel capabilities on data lakes. Databricks also provides a flexible and collaborative Notebook environment. Furthermore, Databricks can be easily deployed across multiple clouds, including AWS, Azure, and GCP. Additionally, it integrates MLflow, allowing for comprehensive management of the machine learning lifecycle, from rapid experimentation to production deployment.

In this article, we have coverd:

• What is Databricks?
• What is Microsoft Azure Synapse Analytics?
• What Is the Difference Between Databricks and Azure Synapse Analytics?
  • Azure Synapse vs Databricks—Architecture Breakdown
  • Azure Synapse vs Databricks—Ecosystem Integration & Cloud Deployment
  • Azure Synapse vs Databricks—Data Processing Engines
  • Azure Synapse vs Databricks—SQL Capabilities & Data Warehousing
  • Azure Synapse vs Databricks—Machine Learning and Analytics
  • Azure Synapse vs Databricks—Scalability & Resource Management
  • Azure Synapse vs Databricks—Real-Time Streaming & Data Ingestion
  • Azure Synapse vs Databricks—Security, Governance & Data Cataloging
  • Azure Synapse vs Databricks—Developer Experience & Notebooks
  • Azure Synapse vs Databricks—Pricing Breakdown

… and more!!!

FAQs

What is Databricks used for?

Databricks is a unified data analytics platform built on Apache Spark that facilitates large-scale data processing, ETL, machine learning, and real-time analytics. It leverages Delta Lake for ACID-compliant data lakes and collaborative notebooks for data science and engineering workflows.

Is Azure Synapse better than Databricks?

They serve different roles. Azure Synapse integrates data warehousing, big data, and data integration into a single service—ideal for large-scale SQL analytics and BI—while Databricks excels in Apache Spark-based, machine learning, and real-time data processing. The choice depends on workload and ecosystem requirements.

What Is the Difference Between Databricks and Azure Synapse Analytics?

Databricks is optimized for Apache Spark workloads and collaborative machine learning; it uses Delta Lake to handle unstructured and streaming data. Azure Synapse offers a unified experience for enterprise data warehousing, ETL, and big data analytics with native SQL support, serverless and dedicated compute options, and deep Azure integration.

What is the alternative of Azure Synapse?

Alternatives include Snowflake, Google BigQuery, and AWS Redshift—each providing robust data warehousing and analytics capabilities with their own strengths in cost, scalability, or integration.

What is equivalent to Databricks in AWS?

On AWS, Amazon EMR is the closest managed Apache Spark service; additionally, AWS Glue offers serverless ETL, and Databricks itself is available on AWS as a managed service.

Is Databricks good for analytics?

Yes. Databricks is engineered for high-performance analytics. Its Apache Spark-powered engine, Delta Lake optimizations, and collaborative notebooks make it excellent for interactive analytics and machine learning applications.

Is Azure Synapse an analytics?

Azure Synapse Analytics is a comprehensive analytics service that unifies data warehousing and big data analytics. It’s designed for enterprise-scale analytics combining SQL, Apache Spark, and data integration features.

What is Azure Synapse Analytics used for?

Azure Synapse is used for end-to-end analytics—from ingesting and preparing data with integrated pipelines to querying massive datasets with both SQL and Apache Spark. It supports interactive BI, advanced data integration, and scalable data warehousing.

Is Azure Synapse Analytics an ETL tool?

Not solely. While it includes robust ETL/ELT capabilities via Synapse Pipelines and integrated data flows, it is a full analytics platform combining data warehousing, big data processing, and BI features.

What are the main components of Azure Synapse Analytics?

• Synapse SQL (dedicated and Serverless SQL Pools) for data warehousing and querying.
• Apache Spark pools for big data processing.
• Data Flow offering a code-free big data transformation.
• Data Integration for orchestrating ETL/ELT workflows.
• Synapse Studio to access all of these capabilities through a single Web UI.

What is Synapse used for?

Synapse is used to integrate, process, and analyze large volumes of data across data warehouses, data lakes, and real-time streams—all within a unified platform that supports BI and ML workloads.

Which SQL is used in Azure Synapse Analytics?

Azure Synapse primarily uses Transact-SQL (T-SQL) for its SQL-based analytics, extended to support scalable querying across both structured and semi-structured data.