Amazon S3 (Simple Storage Service) vs. Azure Synapse Analytics

Amazon S3 is a great service to safely backup your data where redundancy is guaranteed and the cost is fair. We use Amazon S3 for data that we backup and hope we never need to access but in the case of a catastrophic or even small slip of the finger with the delete command we know our data and our client's data is safely backed up by Amazon S3. Transferring data into Amazon S3 is free but transferring data out has an associated, albeit low, cost per GB. This needs to be kept in mind if you plan on transferring out a lot of data frequently. There may be other cost effective options although Amazon S3 prices are really low per GB. Transferring 150TB would cost approximately $50 per month.

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

• Fantastic developer API, including AWS command line and library utilities.
• Strong integration with the AWS ecosystem, especially with regards to access permissions.
• It's astoundingly stable- you can trust it'll stay online and available for anywhere in the world.
• Its static website hosting feature is a hidden gem-- it provides perhaps the cheapest, most stable, most high-performing static web hosting available in PaaS.

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

• Web console can be very confusing and challenging to use, especially for new users
• Bucket policies are very flexible, but the composability of the security rules can be very confusing to get right, often leading to security rules in use on buckets other than what you believe they are

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

Due to princing, availability and scalability.

Incentivized

It is tricky to get it all set up correctly with policies and getting the IAM settings right. There is also a lot of lifecycle config you can do in terms of moving data to cold/glacier storage. It is also not to be confused with being a OneDrive or SharePoint replacement, they each have their own place in our environment, and S3 is used more by the IT team and accessed by our PHP applications. It is not necessarily used by an average everyday user for storing their pictures or documents, etc.

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

AWS has always been quick to resolve any support ticket raised. S3 is no exception. We have only ever used it once to get a clarification regarding the costs involved when data is transferred between S3 and other AWS services or the public internet. We got a response from AWS support team within a day.

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

Overall, we found that Amazon S3 provided a lot of backend features Google Cloud Storage (GCS) simply couldn't compare to. GCS was way more expensive and really did not live up to it. In terms of setup, Google Cloud Storage may have Amazon S3 beat, however, as it is more of a pseudo advanced version of Google Drive, that was not a hard feat for it to achieve. Overall, evaluating GCS, in comparison to S3, was an utter disappointment.

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

• It practically eliminated some real heavy storage servers from our premises and reduced maintenance cost.
• The excellent durability and reliability make sure the return of money you invested in.
• If the objects which are not active or stale, one needs to remove them. Those objects keep adding cost to each billing cycle. If you are handling a really big infrastructure, sometimes this creates quite a huge bill for preserving un-necessary objects/documents.

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