- \n
- What It Is<\/b>: A unified analytics platform combining data warehousing, big data processing, and data integration in one workspace.<\/li>\n
- Core Components<\/b>: Synapse SQL (dedicated and serverless pools), Apache Spark pools, Synapse Pipelines, and Synapse Studio.<\/li>\n
- Pricing Model<\/b>: Pay-per-use across DWU-hours for dedicated pools, per-TB for serverless queries, and vCore-hours for Spark.<\/li>\n
- Best For<\/b>: Organizations already in the Microsoft ecosystem needing to consolidate SQL, Spark, and ETL under one roof.<\/li>\n
- Key Differentiator<\/b>: Native integration with Power BI, Azure ML, and ADLS Gen2 without stitching together separate tools.<\/li>\n<\/ul>\n<\/div>\n\n\n\n
What is Azure Synapse Analytics?<\/h2>\n\n\n\n
![\"What](\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/01\/What-is-Azure-Synapse-Analytics.webp\" "\\"What")
<\/figure>\n\n\n\nvia Microsoft Learn<\/a><\/p>\n\n\n\n
Azure Synapse Analytics is Microsoft’s unified analytics platform. Instead of stitching together separate tools for each function, Synapse provides one environment where data warehouses, big data analytics, and data integration work side by side.<\/p>\n\n\n\n
At its core, the platform offers:<\/p>\n\n\n\n
- \n
- Data warehousing:<\/strong> Run SQL queries against structured data at scale using dedicated or serverless SQL pools. This handles traditional BI workloads, reporting, and analytical queries.<\/li>\n\n\n\n
- Big data analytics:<\/strong> Process massive datasets using Apache Spark. It covers data engineering tasks, machine learning pipelines, and transformations that SQL cannot handle efficiently.<\/li>\n\n\n\n
- Data integration:<\/strong> Build ETL and ELT pipelines directly within Synapse using a visual interface or code. The platform includes 95+ connectors to pull data from databases, SaaS applications, and cloud storage.<\/li>\n\n\n\n
- Unified workspace:<\/strong> Manage everything from Synapse Studio, a single web-based interface where SQL developers<\/a>, data engineers, and analysts can collaborate without context switching.<\/li>\n<\/ul>\n\n\n\n
The platform also integrates natively with Azure Data Lake Storage Gen2. This means SQL and Spark can query the same files, whether Parquet, CSV, or JSON, without duplicating data or moving it between systems.<\/p>\n\n\n\n
For organizations already invested in the Microsoft ecosystem, Synapse connects seamlessly with Power BI, Azure Machine Learning, and Azure Purview for governance.<\/p>\n\n\n\n
\n \n\n<\/div>\n<\/p>\n
\nAlso Read:<\/strong> Humanizing Data Deluge – Azure Synapse Insights<\/a><\/div>\n<\/p>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n
Core Components of Azure Synapse Analytics<\/h2>\n\n\n\n
Synapse brings multiple engines together under one roof. Each component handles a specific type of workload, and understanding what they do individually clarifies how they work together.<\/p>\n\n\n\n
![\"Azure](\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/01\/Azure-Synapse-Analytics-architecture.webp\" "\\"Azure")
<\/figure>\n\n\n\nHere are the four core pieces that make up the Azure Synapse Analytics <\/em><\/strong>architecture.<\/strong><\/p>\n\n\n\n
1. Synapse SQL<\/h3>\n\n\n\n
Synapse SQL is the data warehousing<\/a> engine. It uses a distributed query system based on T-SQL, which means teams already familiar with SQL Server can work with it immediately without learning new syntax.<\/p>\n\n\n\n
The engine comes in two flavors. Dedicated SQL pools<\/strong> provision reserved compute capacity measured in Data Warehouse Units (DWUs). They deliver consistent performance for production workloads, scheduled reports, and dashboards that need predictable response times.<\/p>\n\n\n\n
Serverless SQL pools <\/strong>require no provisioning. They spin up on demand, charge per terabyte of data scanned, and shut down when idle.<\/p>\n\n\n\n
Common use cases for Synapse SQL include:<\/p>\n\n\n\n
- \n
- Enterprise reporting and BI dashboards<\/li>\n\n\n\n
- Ad-hoc data exploration without infrastructure setup<\/li>\n\n\n\n
- Querying external data in Azure Data Lake<\/a> without importing it first<\/li>\n\n\n\n
- Serving as the semantic layer for Power BI and other visualization tools<\/li>\n<\/ul>\n\n\n\n
The ability to query data in place, whether it lives in a dedicated warehouse or sits as raw files in a data lake, makes Synapse SQL flexible across different data architectures.<\/p>\n\n\n\n
2. Synapse Spark<\/h3>\n\n\n\n
Synapse Spark handles big data processing and data engineering<\/a> workloads. It runs Apache Spark, the open-source engine widely used for distributed computing, machine learning, and large-scale transformations.<\/p>\n\n\n\n
Spark pools in Synapse provision clusters automatically. The platform handles startup, autoscaling, and shutdown based on workload demand.<\/p>\n\n\n\n
Synapse Spark supports:<\/p>\n\n\n\n
- \n
- Python, Scala, R, and .NET for Spark development<\/li>\n\n\n\n
- SparkML for distributed machine learning<\/li>\n\n\n\n
- Delta Lake for reliable, versioned data storage<\/li>\n\n\n\n
- Direct integration with data stored in Azure Data Lake Storage Gen2<\/li>\n<\/ul>\n\n\n\n
Data engineers use Spark for ETL pipelines, data cleansing, and complex transformations that SQL cannot handle efficiently. Data scientists use it for training models on large datasets. <\/p>\n\n\n\n
Both work on Synapse Studio using notebooks that combine code, visualizations, and markdown documentation.<\/p>\n\n\n\n
3. Synapse Pipelines<\/h3>\n\n\n\n
Synapse Pipelines is the data integration layer. It uses the same engine as Azure Data Factory (ADF), which means teams already using ADF will recognize the interface and functionality.<\/p>\n\n\n\n
Pipelines handle the movement and transformation of data across systems. They connect to sources, extract data, apply transformations, and load results into destinations. This covers both ETL (extract, transform, load)<\/a> and ELT (extract, load, transform)<\/a> patterns depending on where processing happens.<\/p>\n\n\n\n
Key capabilities include:<\/p>\n\n\n\n
- \n
- 95+ built-in connectors for databases, SaaS apps, file systems, and cloud storage<\/li>\n\n\n\n
- Mapping data flows for code-free transformations at scale<\/li>\n\n\n\n
- Orchestration features, including triggers, scheduling, and dependency chaining<\/li>\n\n\n\n
- Parameterization and dynamic content for reusable pipeline templates<\/li>\n<\/ul>\n\n\n\n
Pipelines run within the same workspace as SQL and Spark, so data engineers can build end-to-end workflows without switching tools or managing separate environments.<\/p>\n\n\n\n
\n \n\n<\/div>\n<\/p>\n
\nAlso Read:<\/strong> Seamless Integration: Data Lake Analytics to Azure Synapse<\/a><\/div>\n<\/p>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n
4. Synapse Studio<\/h3>\n\n\n\n
Synapse Studio is the unified web-based interface that ties everything together. It is where teams write queries, build notebooks, design pipelines, monitor jobs, and manage security settings.<\/p>\n\n\n\n
The workspace is organized around hubs that group related functionality. The Data hub <\/strong>lets users browse databases, tables, and linked storage. The Develop hub is where SQL scripts, Spark notebooks, and pipeline definitions live.<\/p>\n\n\n\n
The Integrate hub handles pipeline orchestration. The Monitor hub shows job runs, resource usage, and performance metrics. The Manage hub covers security, access control, and infrastructure settings.<\/p>\n\n\n\n
This unified experience matters because it:<\/p>\n\n\n\n
- \n
- Reduces context switching between separate tools<\/li>\n\n\n\n
- Keeps SQL developers, data engineers, and analysts in the same environment<\/li>\n\n\n\n
- Centralizes governance and access control<\/li>\n\n\n\n
- Simplifies onboarding for new team members<\/li>\n<\/ul>\n\n\n\n
Synapse Studio runs entirely in the browser. There is nothing to install, and teams can access workspaces from anywhere with appropriate credentials.<\/p>\n\n\n\n
Azure Synapse Analytics Key Features and Capabilities<\/h2>\n\n\n\n
Now, here\u2019s a closer look at what Azure Synapse Analytics does, its features, and its capabilities.<\/p>\n\n\n\n
Unified Experience<\/h3>\n\n\n\n
![\"Unified](\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/01\/Unified-workspace-interface-in-Azure-Synapse-Analytics-1024x683.webp\" "\\"Unified")
<\/figure>\n\n\n\nvia Microsoft Azure<\/a><\/p>\n\n\n\n
Synapse consolidates your tools into a single workspace called Synapse Studio. From one web-based interface, teams can:<\/p>\n\n\n\n
- \n
- Write and execute SQL queries<\/a> against data warehouse tables<\/li>\n\n\n\n
- Build and run Apache Spark notebooks for big data processing<\/li>\n\n\n\n
- Design data pipelines<\/a> using visual drag-and-drop tools or code<\/li>\n\n\n\n
- Monitor jobs, manage resources, and configure security settings<\/li>\n<\/ul>\n\n\n\n
Data engineers building pipelines and analysts writing reports work in the same environment with access to the same underlying data. This reduces context switching and makes collaboration practical rather than aspirational.<\/p>\n\n\n\n
Flexible Querying<\/h3>\n\n\n\n
Not every workload needs the same compute model. Synapse offers multiple options depending on performance requirements and cost sensitivity.<\/p>\n\n\n\n
![\"Microsoft](\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/01\/Microsoft-Azure-Synapse-Analytics-user-interface-1024x566.webp\" "\\"Microsoft")
<\/figure>\n\n\n\nvia Microsoft Azure<\/a><\/p>\n\n\n\n
Apache Spark pools round out the options. They handle:<\/p>\n\n\n\n
- \n
- Large-scale data processing beyond what SQL can manage efficiently<\/li>\n\n\n\n
- Machine learning model training and scoring<\/li>\n\n\n\n
- Complex transformations using Python, Scala, R, or .NET<\/li>\n<\/ul>\n\n\n\n
Teams can mix these compute options within the same workspace. A dataset might be explored with serverless SQL, transformed with Spark, and served to dashboards through a dedicated pool.<\/p>\n\n\n\n
Cohesive Data Integration<\/h3>\n\n\n\n
Moving data into and within an analytics platform is often the hardest part. Synapse includes built-in pipeline capabilities inherited from Azure Data Factory<\/a>, which means data engineers can ingest, transform, and load data without leaving the Synapse environment.<\/p>\n\n\n\n
The platform supports:<\/p>\n\n\n\n
- \n
- Connections to databases, file systems, and cloud storage<\/li>\n\n\n\n
- Code-free transformations using mapping data flows<\/li>\n\n\n\n
- Orchestration features for scheduling, triggers, and dependency management<\/li>\n<\/ul>\n\n\n\n
Pipelines can be built visually using drag-and-drop tools or written in code for more complex logic.<\/p>\n\n\n\n
Data Lake Integration<\/h3>\n\n\n\n
Synapse treats Azure Data Lake <\/strong>as a first-class citizen. Both SQL and Spark engines can query files stored in Azure Data Lake Storage Gen2 directly, without loading data into a separate warehouse first.<\/p>\n\n\n\n
This matters for several reasons. Raw data can be queried in place without duplication. The same files are accessible to both SQL analysts and Spark developers. Storage and compute remain decoupled, which reduces costs and increases flexibility.<\/p>\n\n\n\n
Supported formats include:<\/p>\n\n\n\n
- \n
- Parquet<\/li>\n\n\n\n
- CSV<\/li>\n\n\n\n
- JSON<\/li>\n\n\n\n
- Delta Lake<\/li>\n<\/ul>\n\n\n\n
For organizations adopting a lakehouse architecture<\/a>, this integration is central to how Synapse operates.<\/p>\n\n\n\n
\n \n\n<\/div>\n<\/p>\n
\nAlso Read:<\/strong> Data Lakehouse vs. Data Warehousing<\/a><\/div>\n<\/p>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n
Machine Learning and AI<\/h3>\n\n\n\n
Synapse connects natively with Azure Machine Learning, allowing teams to train, register, and deploy models without leaving the analytics environment.<\/p>\n\n\n\n
Within Synapse Studio, users can access registered ML models, score data directly in SQL or Spark, and build predictive pipelines that combine data transformation and inference. This matters for organizations looking to operationalize machine learning alongside traditional BI workloads.<\/p>\n\n\n\n
The platform also supports:<\/p>\n\n\n\n
- \n
- Running Python and R scripts for data science workloads<\/li>\n\n\n\n
- SparkML algorithms for distributed model training<\/li>\n\n\n\n
- Built-in support for Delta Lake, which simplifies versioning and data reliability<\/li>\n<\/ul>\n\n\n\n
Security<\/h3>\n\n\n\n
![\"Secure](\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/01\/Secure-your-workspace-with-Microsoft-Azure-Synapse-Analytics-1024x683.webp\" "\\"Secure")
<\/figure>\n\n\n\nvia Microsoft Azure<\/a><\/p>\n\n\n\n
Enterprise analytics platforms handle sensitive data. Synapse includes multiple layers of protection to address compliance and governance requirements.<\/p>\n\n\n\n
Column-level and row-level security restrict access to specific data based on user roles. Dynamic data masking obscures sensitive fields for users without clearance. Private Link enables secure connections that never traverse the public internet.<\/p>\n\n\n\n
Additional security features include:<\/p>\n\n\n\n
- \n
- Managed virtual networks that isolate Synapse resources from other Azure workloads<\/li>\n\n\n\n
- Integration with Azure Purview for data cataloging, lineage tracking, and governance<\/li>\n\n\n\n
- Automated threat detection and always-on encryption<\/li>\n\n\n\n
- Support for compliance frameworks, including HIPAA, ISO 27001, PCI DSS, and SOC<\/li>\n<\/ul>\n\n\n\n
These capabilities matter for industries with strict regulatory requirements, such as finance, healthcare, and government.<\/p>\n\n\n \t
\n \t\t \n \t\t\t\n \t\t\t\t<\/div>\n \t\t\t\t<\/p>\n
\nNeed help getting started? Aegis Softtech's<\/a> Azure experts can assess your current environment and design a Synapse architecture tailored to your workloads.<\/div>\n<\/p>\n \t\t\t<\/div>\n \t\t\t \t\t\t\t
\n \t\t\t\t\tTalk to Our Team<\/a>\n \t\t\t\t<\/div>\n \t\t\t \t\t<\/div>\n \t<\/section>\n \n\n\n\nMonitoring<\/h3>\n\n\n\n
Visibility into what is running, how long it takes, and where issues occur is critical for managing costs and performance. Synapse provides built-in monitoring without requiring third-party tools.<\/p>\n\n\n\n
![\"Power](\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/01\/Power-BI-dashboards-in-Microsoft-Azure-1024x504.webp\" "\\"Power")
<\/figure>\n\n\n\nvia Microsoft Azure<\/a><\/p>\n\n\n\n
Synapse Studio dashboards show active queries, pipeline runs, and Spark jobs in real time. Azure Monitor integration enables centralized logging and alerting across the broader infrastructure.<\/p>\n\n\n\n
Teams can use monitoring to:<\/p>\n\n\n\n
- \n
- Track resource usage and identify cost optimization opportunities<\/li>\n\n\n\n
- Spot slow-running queries and performance regressions<\/li>\n\n\n\n
- Set up alerts for job failures or threshold breaches<\/li>\n\n\n\n
- Audit access patterns and security events<\/li>\n<\/ul>\n\n\n\n
\n \n\n<\/div>\n<\/p>\n
\nAlso Read:<\/strong> Azure Data Lake: Key Trends and Innovations<\/a><\/div>\n<\/p>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n
Azure Synapse Analytics Pricing<\/h2>\n\n\n\n
The platform uses a consumption-based model with each component\u2014SQL pools, Spark pools, storage, and pipelines\u2014carrying its own pricing structure.<\/p>\n\n\n\n
Dedicated SQL Pool Pricing<\/h3>\n\n\n\n
Dedicated SQL pools charge based on Data Warehouse Units (DWUs)<\/a>, a composite measure of CPU, memory, and I\/O resources. The Service Level Objective (SLO) determines the cost and performance level, with Gen2 pools measured in DWUs (for example, DW2000c).<\/p>\n\n\n\n
Key pricing factors include:<\/p>\n\n\n\n
- \n
- Hourly billing: <\/strong>DWUs are billed for each hour a pool exists using the largest compute size that applied during that hour, regardless of the number of minutes used.<\/li>\n\n\n\n
- Compute tiers:<\/strong> DWU levels range from DW100c (entry-level) to DW30000c (enterprise-scale). Higher DWU counts provide greater parallelism and faster query performance.<\/li>\n\n\n\n
- Storage: <\/strong>Data storage is charged per TB per month and includes the size of your data warehouse and seven days of incremental snapshot storage. Storage transactions are not billed separately.<\/li>\n\n\n\n
- Disaster Recovery:<\/strong> Geo-redundant storage for disaster recovery incurs additional charges per GB per month.<\/li>\n<\/ul>\n\n\n\n
Reserved capacity enables you to save up to 65%<\/a> on data warehousing resources compared to pay-as-you-go pricing by pre-paying for compute capacity on a one- or three-year term.<\/p>\n\n\n \t
\n \t\t \n \t\t\t\n \t\t\t\t<\/div>\n \t\t\t\t<\/p>\n
\nOur team of experts helps organizations right-size their pools, implement auto-pause, and avoid billing surprises.<\/div>\n<\/p>\n \t\t\t<\/div>\n \t\t\t \t\t\t\t
\n \t\t\t\t\tBook a FREE Consultation<\/a>\n \t\t\t\t<\/div>\n \t\t\t \t\t<\/div>\n \t<\/section>\n \n\n\n\nServerless SQL Pool Pricing<\/h3>\n\n\n\n
Serverless SQL pools<\/em><\/strong> follow a pay-per-query model, making them ideal for ad-hoc analysis and exploratory workloads:<\/p>\n\n\n\n
- \n
- Per-TB pricing: <\/strong>You pay for executed queries based on the amount of data processed. A minimum charge of 10 MB applies to each query, rounded up to the nearest 1 MB.<\/li>\n\n\n\n
- DDL statements:<\/strong> Metadata-only queries (DDL statements) do not incur a cost.<\/li>\n\n\n\n
- No provisioning required: <\/strong>Serverless pools eliminate the need to manage or pay for idle compute resources.<\/li>\n<\/ul>\n\n\n\n
This model works well for teams that query data lakes intermittently or need to explore datasets before committing to dedicated infrastructure.<\/p>\n\n\n\n
Apache Spark Pool Pricing<\/h3>\n\n\n\n
Apache Spark pools in Azure Synapse support big data processing, machine learning, and data engineering workloads. Pricing follows:<\/p>\n\n\n\n
- \n
- vCore-Hour billing: <\/strong>Apache Spark pool usage is charged per vCore hour and prorated by the minute.<\/li>\n\n\n\n
- Node sizes: <\/strong>Spark pools support node sizes ranging from Small (4 vCores, 32 GB memory) up to XXLarge (64 vCores, 432 GB memory) per node.<\/li>\n\n\n\n
- Pool types:<\/strong> Memory-optimized and hardware-accelerated pools are available, with pricing varying by configuration.<\/li>\n\n\n\n
- Auto-pause: <\/strong>Creating Spark pool definitions is free; you are charged for usage when pools are running.<\/li>\n<\/ul>\n\n\n\n
\nExample calculation:<\/strong> A Spark pool instantiated with 20 medium nodes (8 vCores each) running for 20 minutes would consume 160 vCores \u00d7 0.33 hours = approximately 48 vCore hours.\n<\/div>\n\n\n\nData Integration and Pipeline Pricing<\/h3>\n\n\n\n
Synapse Pipelines incur costs based on activity execution and data movement.<\/p>\n\n\n\n
- \n
- Activity runs:<\/strong> Charged based on the number of pipeline activity executions.<\/li>\n\n\n\n
- Integration runtime hours<\/strong>: You pay for data pipeline orchestration by activity run and activity execution by integration runtime hours. Execution is prorated by the minute and rounded up.<\/li>\n\n\n\n
- Data flows:<\/strong> Data Flow cluster execution and debugging time is charged per vCore-hour, with a minimum cluster size of 8 vCores.<\/li>\n\n\n\n
- Inactive pipelines: <\/strong>A data pipeline with no associated trigger or runs within the month is considered inactive and incurs no charges.<\/li>\n<\/ul>\n\n\n\n
Storage and Networking Costs<\/h3>\n\n\n\n
Storage costs apply to data held in Azure Data Lake Storage Gen2 and within dedicated SQL pools:<\/p>\n\n\n\n
- \n
- ADLS Gen2: <\/strong>Billed per TB per month based on the storage tier (Hot, Cool, or Archive)<\/li>\n\n\n\n
- Data egress: <\/strong>Outbound data transfer across Azure regions incurs networking charges<\/li>\n\n\n\n
- Backup storage: <\/strong>Automated backups and geo-redundant copies add to storage costs<\/li>\n<\/ul>\n\n\n\n
\nPro Tip:<\/strong> Control costs for dedicated SQL pools by pausing the resource when not in use. For example, pause during nights and weekends, then resume during the day.\n<\/div>\n\n\n\nCommon Use Cases for Azure Synapse Analytics<\/h2>\n\n\n\n
Azure Synapse Analytics addresses diverse analytical needs across industries. Here are the primary scenarios where the platform delivers value.<\/p>\n\n\n\n
1. Enterprise Data Warehousing<\/h3>\n\n\n\n
Walgreens<\/a> serves eight million people every day, generating vast amounts of data in the process. To improve staff support and better serve its customers, the company migrated its on-premises data warehouse to Azure Synapse Analytics.<\/p>\n\n\n\n
Now, reports that previously arrived at 1:00 PM are available by 9:00 AM, at one-third the cost of setting up a new on-premises data warehouse. Performance is at least 3x faster<\/a>, and annual maintenance costs dropped significantly.<\/p>\n\n\n\n
2. Real-Time Operational Analytics<\/h3>\n\n\n\n
Aggreko<\/a>, a global leader in temporary power generation, had data ingestion pipelines that took four hours to run. Data lagged 8-24 hours behind operations.<\/p>\n\n\n\n
After adopting Azure Synapse, ingestion time dropped to less than five minutes. The team estimates they saved 30-40% of the time previously spent solving technology problems in legacy systems.<\/p>\n\n\n\n
3. Fraud Detection and Machine Learning<\/h3>\n\n\n\n
Clearsale<\/a>, a Brazilian fraud detection company, verifies half a million transactions daily. Their dataset doubles every two years, and fraud detection must happen within seconds.<\/p>\n\n\n\n
Using Azure Synapse, Clearsale reduced ML model training time from nearly a week on their previous on-premises platform to hours.<\/p>\n\n\n\n
4. Logistics and Supply Chain Optimization<\/h3>\n\n\n\n
FedEx<\/a>, celebrating 50 years of operation, needed to retire legacy systems that could not keep pace with fast growth. The Enterprise Business Strategies team selected Azure Synapse Analytics for its scalability and affordability.<\/p>\n\n\n\n
Data transformations that previously took hours on-prem now run in minutes, and the team has built prediction models for customer payment segmentation, credit risk scoring, and freight anomaly detection. <\/p>\n\n\n\n
5. Financial Services and Investment Analytics<\/h3>\n\n\n\n
OMERS, a Canadian pension plan with C$124 billion in net assets and 560,000 members, needed unified insights from data stored across multiple apps.<\/p>\n\n\n\n
With Azure Synapse Analytics, a Fusion Team built an enterprise-class Finance Data Platform in 20% of the typical time frame. The platform has since powered more than 20 new data products, including financial planning dashboards and an enterprise-spend tool with embedded AI for real-time decision support.<\/p>\n\n\n\n
6. Pharmaceutical and Healthcare Data Processing<\/h3>\n\n\n\n
Walgreens<\/a> processes hundreds of millions of prescription dispenses annually across nearly 9,000 stores.<\/p>\n\n\n\n
Using Azure Synapse Analytics with Azure Databricks, the company created an intelligent data platform that processes transaction data and generates insights within minutes. At peak times, the platform handles around 40,000 transactions per second.<\/p>\n\n\n\n
Azure Synapse vs. Other Analytics Platforms<\/h2>\n\n\n\n
Now, let\u2019s understand how Azure Synapse for business<\/a> compares to other analytics platforms.<\/p>\n\n\n\n
Azure Synapse vs. Snowflake<\/h3>\n\n\n\n
Here’s a brief comparison of Azure Synapse and Snowflake<\/a>:<\/p>\n\n\n\n
Feature<\/strong><\/td> Azure Synapse<\/strong><\/td> Snowflake<\/strong><\/td><\/tr> Deployment model<\/strong><\/td> PaaS on Azure<\/td> SaaS on AWS, Azure, GCP<\/td><\/tr> Compute billing<\/strong><\/td> Hourly (dedicated pools) or per-TB (serverless)<\/td> Per-second after 60-second minimum<\/td><\/tr> Auto-suspend<\/strong><\/td> Manual pause for dedicated pools<\/td> Automatic suspend and resume<\/td><\/tr> Scaling<\/strong><\/td> DWU-based; admin-managed<\/td> Instant warehouse resizing; auto-scale clusters<\/td><\/tr> Administration<\/strong><\/td> Requires tuning of indexes, distribution, concurrency<\/td> Near-zero maintenance<\/td><\/tr> Native integrations<\/strong><\/td> Power BI, Azure ML, Data Factory, ADLS Gen2<\/td> Third-party connectors; runs atop cloud providers<\/td><\/tr> Data sharing<\/strong><\/td> Via Azure Data Share<\/td> Secure Snowflake Data Sharing<\/a> across accounts and clouds<\/td><\/tr> Egress fees<\/strong><\/td> ~$0.09\/GB<\/td> ~$0.02\/GB<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n \n - Data egress: <\/strong>Outbound data transfer across Azure regions incurs networking charges<\/li>\n\n\n\n
- Integration runtime hours<\/strong>: You pay for data pipeline orchestration by activity run and activity execution by integration runtime hours. Execution is prorated by the minute and rounded up.<\/li>\n\n\n\n
- Node sizes: <\/strong>Spark pools support node sizes ranging from Small (4 vCores, 32 GB memory) up to XXLarge (64 vCores, 432 GB memory) per node.<\/li>\n\n\n\n
- DDL statements:<\/strong> Metadata-only queries (DDL statements) do not incur a cost.<\/li>\n\n\n\n
- Compute tiers:<\/strong> DWU levels range from DW100c (entry-level) to DW30000c (enterprise-scale). Higher DWU counts provide greater parallelism and faster query performance.<\/li>\n\n\n\n
- Hourly billing: <\/strong>DWUs are billed for each hour a pool exists using the largest compute size that applied during that hour, regardless of the number of minutes used.<\/li>\n\n\n\n
- Write and execute SQL queries<\/a> against data warehouse tables<\/li>\n\n\n\n
- Serving as the semantic layer for Power BI and other visualization tools<\/li>\n<\/ul>\n\n\n\n
- Big data analytics:<\/strong> Process massive datasets using Apache Spark. It covers data engineering tasks, machine learning pipelines, and transformations that SQL cannot handle efficiently.<\/li>\n\n\n\n
- Core Components<\/b>: Synapse SQL (dedicated and serverless pools), Apache Spark pools, Synapse Pipelines, and Synapse Studio.<\/li>\n