Azure Analysis Services vs. Azure Synapse Analytics

We would have many technical issues and glitches with previous similar providers but found that Azure Analysis Services can simply handle our workload and memory better. I remember we lost an account due to cloud issues not fully saving or corrupting some files. Granted, this is rare with any cloud but haven't had that issue with the same load of memory with Azure Analysis Services.

Incentivized

It's well suited for large, fastly growing, and frequently changing data warehouses (e.g., in startups). It's also suited for companies that want a single, relatively easy-to-use, centralized cloud service for all their data needs. Larger, more structured organizations could still benefit from this service by using Synapse Dedicated SQL Pools, knowing that costs will be much higher than other solutions. I think this product is not suited for smaller, simpler workloads (where an Azure SQL Database and a Data Factory could be enough) or very large scenarios, where it may be better to build custom infrastructure.

Incentivized

• Providing role based access or we can say privilege based on the role to the user if it is integrated with Azure active directory and hence securing the access to sensitive data.
• We use to run different type of analytics services to get the better result which is hectic if done manually or with human efforts.
• We also use to collect bulk of data with the help of this tool and run customized test cases for better efficiency of result and better decision making. The result are very crucial and helps in taking big decision.
• It supports different or we can say heterogeneous database vendors like the Oracle, SQL, and hence make the task easy.

Incentivized

• Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
• Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
• Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.

Incentivized

• There is very least content available on Microsoft docs and the Internet to get through with AAS.
• i faced lots of connection issues while implementation.
• The need for serious platform experience or expertise.

Incentivized

• With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
• Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
• Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools

Incentivized

The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.

Incentivized

Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.

Incentivized

The platform has vast number of features and modules. The UI is sleek and once you get to use to it, you will be able to do a lot of stuff. Also support for data sources is more in Azure Analysis Services.

Incentivized

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.

Incentivized

Basically, the billing is predictable, and this all about it.

Incentivized

• The tool has helped us a lot for taking critical decision which has resulted in the increase of profit of organization by 18 percent.
• Return on investment is 70 percent as we are unable to utilize the tool with full functionalities, so we are still testing some of the use cases.
• Human effort has been reduced and task has become automated hence helped in cost management

Incentivized

• 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

Incentivized