Azure Synapse Analytics (Azure SQL Data Warehouse) vs Denodo

In terms of a well-suited scenario - the Azure Synapse can be used to capture data from multiple sources (especially from onPrem sources apart from Dataverse) and update the transformed data based on the given conditions (eg: refresh data based on the specified date/time ranges). Also, the transformed data can simply be transferred to Azure Data Lake for further processing by utilizing other analytics tools such as PowerBI.

Donodo is very well suited for large organizations with siloed datasets. Denodo thrives in these environments, since large organizations can afford the licensing costs, and are always tasked to mash up data from multiple environments throughout the organization.

• 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.

• Flat learning curve.
• Does not need brilliant developers.
• Less need for custom denodo wrappers, even if needed is not very complex.
• Denodo stored procedures and functions helped address complex, not straightforward requirements.
• Export/import deployment between environments was very helpful.

• 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.

• Not great if you have very large volumes of data
• Can be a little buggy

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.

Denodo is very easy to use. It has a user-friendly drag and drop interface. I'm not a fan of the java platform it resides on.

Denodo is a tool to rapidly mash data sources together and create meaningful datasets. It does have its downfalls though. When you create larger, more complex datasets, you will most likely need to cache your datasets, regardless of how proper your joins are set up. Since DV takes data from multiple environments, you are taxing the corporate network, so you need to be conscious of how much data you are sending through the network and truly understand how and when to join datasets due to this.

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.

We also looked at Oracle Data Warehouse as part of our short list of products to implement as a solution. Oracle's product turned out to have less support by way of easily accessible internet blogs. Oracle was also considerably more expensive and we would have needed to hire the skillset needed to implement.

We selected Denodo as a data visualisation tool because we have only good feedback from our partners and clients. And we are not disappointed, the software meets totally our needs.

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

• 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

• Rising business strategies
• Full data visualization