Cloud data simplified: a great all-in-one tool for all your data analytics needs
Our data warehouse was growing at a 1TB/year rate, and we needed a solution that would be both cheap and effective.
Previously we were …
Previously we were …
Starting at $4,700 per month 5000 Synapse Commit Units (SCUs)
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What is Azure Synapse Analytics?
Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless…
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Pricing
Tier 1
$4,700
Cloud
per month 5,000 Synapse Commit Units (SCUs)
Tier 2
$9,200
Cloud
per month 10,000 Synapse Commit Units (SCUs)
Tier 3
$21,360
Cloud
per month 24,000 Synapse Commit Units (SCUs)
Entry-level set up fee?
- No setup fee
For the latest information on pricing, visithttps://azure.microsoft.com/en…
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- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Starting price (does not include set up fee)
- $4,700 per month 5000 Synapse Commit Units (SCUs)
Product Details
- About
- Tech Details
- FAQs
What is Azure Synapse Analytics?
Azure Synapse Analytics Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Azure Synapse Analytics starts at $4700.
The most common users of Azure Synapse Analytics are from Enterprises (1,001+ employees).
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Our data warehouse was growing at a 1TB/year rate, and we needed a solution that would be both cheap and effective.
Previously we were using Azure SQL Database with its JSON capabilities and various Azure serverless services to manage our data, but at that growth rate, time and cost were becoming limiting factors.
Previously we were using Azure SQL Database with its JSON capabilities and various Azure serverless services to manage our data, but at that growth rate, time and cost were becoming limiting factors.
- Build, schedule and monitor complex data pipelines (Azure Data Factory component)
- Access your data lake using the familiar T-SQL syntax and TDS-enabled tools (SSMS, ADS, ...). This is especially useful for business people that are used to a specific workflow.
- Support a wide range of data transformation tools, from low-code (DataFlows) to full-code (Spark), all integrated in a single central orchestrator (Azure Data Factory-like)
- Provide all these services as a single very convenient package, without the need to know beforehand all the configuration behind
- There's no support for Synapse Serverless objects (e.g., views) in SSDT - the VCS-friendly approach to schema deployments from Microsoft. SSDT is available for almost all other SQL Server and Azure SQL products, including Synapse Dedicated SQL Pools.
- There are lots of ways to accomplish the same task, and it's not very clear which one is best suited for a given scenario other than trial and error. Also, some scenarios (e.g., efficient management of late arrivals) don't have a clear solution path.
- I think it would be cool to have a tighter integration of the product with the Azure Data Studio client, not only for connecting to SQL Serverless or Dedicated Pools. For example, PySpark development and debugging would be much easier if done from ADS.
- Synapse Serverless Pools
- Pipeline orchestration (Azure Data Factory-style)
- PySpark support
- Reduce operative costs by switching from VLDB on Azure SQL to a data lake architecture
- Improve data reliability by providing timely monitoring and alerting
They're all part of the Microsoft Azure family, so they are not exactly competitors. They overlap in functionality, but they're targeted at different levels of customers.
Azure Data Factory is an excellent stand-alone PaaS (included in Synapse Analytics) for writing, scheduling, and monitoring pipelines.
Azure SQL Database (and all the Azure SQL family) is excellent for traditional, SQL-based data warehouses, especially if you're migrating from on-premises. Combined with Azure Data Factory (that can run SSIS packages), it's a perfect solution for a simple path to the cloud.
Azure Databricks is effectively the only internal "competitor" to Synapse Analytics but targeted more to a "platform-agnostic" audience. On the other hand, Synapse is more of a proprietary mix of products that are more tightly related to Microsoft technologies.
Azure Data Factory is an excellent stand-alone PaaS (included in Synapse Analytics) for writing, scheduling, and monitoring pipelines.
Azure SQL Database (and all the Azure SQL family) is excellent for traditional, SQL-based data warehouses, especially if you're migrating from on-premises. Combined with Azure Data Factory (that can run SSIS packages), it's a perfect solution for a simple path to the cloud.
Azure Databricks is effectively the only internal "competitor" to Synapse Analytics but targeted more to a "platform-agnostic" audience. On the other hand, Synapse is more of a proprietary mix of products that are more tightly related to Microsoft technologies.
July 21, 2022
Plays with Power BI nicely
Microsoft Azure Synapse Analytics (formally Azure SQL Data Warehouse) is being used as our marketing data warehouse. We are pulling data down from a number of different API's such as Facebook ads, Google ads and Google analytics, and then pumping that information back into the Azure Synapse Analytics Warehouse on a daily basis.
- They unify many data sources easily
- There is some "code free" ETL work it enables
- There is some AI integration that works nice
- The cost structure is difficult to understand
- The job scheduling capabilities aren't easy to use
- The logging metrics aren't easy to see
- Ability to remove dependency on data gateway
- data processing speed
- serverless maintainance
- Easy database management using modern tools
- Easy integration with Power BI
- Cloud-native HTAP
Azure Synapse Analytics stacks up well against the competitors I mentioned above. Technically, Azure SQL Datawarehouse is an upgraded version of the Azure SQL Database. So, the choice to move from one to the other depends on the processing needs of your company. If you need more power, go with the data warehouse.
March 17, 2022
Azure Synapse Does It All
As a consulting company, we implement data warehouse solutions for our clients. We use Azure Synapse for enterprises data warehouse implementations. Data from various internal sources like sales, finance and operations are integrated into Synapse via Azure Data Factory and Data Lake. It’s used as reporting data source for Microsoft Power BI as well.
- Data integration via poly base
- Data distribution
- Create table as select
- Resource allocation via user groups (for production ETL and report users)
- Integrating external 3rd party data sources is very easy in Snowflake and it’s missing in Azure Synapse
- Master data services and data quality services are missing in Azure Synapse. They are useful features present in on Orem Sql server
- Resource usage reports (top 10 expensive queries, most frequently run queries, etc) are a feature that can be added in Azure Synapse. It’s present in an on-prem SQL server. DMVs are there but viewing it visually as a report is more helpful.
- External table creation via polybase
- Azure Synapse workspace integration with Microsoft Power BI, data sources and Azure Data Factory
- Create table as select
- Licensing fees is replaced with Azure subscription fee. No big saving there
- More visibility into the Azure usage and cost
- It can be used a hot storage and old data can be archived to data lake. Real time data integration is possible via external tables and Microsoft Power BI
When client is already having or using Azure then it’s wise to go with Synapse rather than using Snowflake. We got a lot of help from Microsoft consultants and Microsoft partners while implementing our EDW via Synapse and support is easily available via Microsoft resources and blogs. I don’t see that with Snowflake
We've been using Azure Synapse Analytics to create data pipelines for onPrem/onCloud ETL processing where the transform data will store inside the Azure Data Lake for further processing using PowerBI.
- Create data pipelines to connect with multiple data workspace(s) and external data
- Ability to connect with Azure Data Lake (sequentially) for data warehousing
- Being able to manage connections and create integration runtimes (for onPrem data capture)
- Thus far haven't seen any complications and/or any missing features
- Being able to create Pipeline(s) to pull both onPrem (eg: JDE) and onCloud (Dataverse) data for ETL processing
- Smooth transformation and transfer the processed data to Data Lake
- Azure Synapse has predominately reduced the time being used for processing data being extracted from multiple sources
- Ability to integrate multiple data sources and create a single dataset that could be used for further analytics to find KPIs
In comparing Azure Synapse to the Google BigQuery - the biggest highlight that I'd like to bring forward is Azure Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes whereas Google BigQuery only takes into account computation and storage.
August 25, 2021
Modern Database
We use Azure Synapse Analytics (Azure SQL Data Warehouse) to hold all our daily sales data to serve reports. Without any storage constraint, we save large datasets and process them in a matter of time, thanks to the Azure lake storage support and Massive Parallel processing capabilities. It supports major file formats like Avro, Parquet and many more.
- Easy to Manage data
- Blazing fast query processing
- Supports Modern fileformats
- Documentation and Usecases
- Pricing
- Admin capabilities
- External Tables
- Modern File formats
- Massive Parallel Processing
- Realtime Analytics
- Cost Efficient
- Lesser Turn-around Time