Bring together Big Data analytics and enterprise data warehousing!!!
March 10, 2022

Bring together Big Data analytics and enterprise data warehousing!!!

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Synapse Analytics (Azure SQL Data Warehouse)

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.

Pros

  • 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)

Cons

  • 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.

Do you think Azure Synapse Analytics delivers good value for the price?

Yes

Are you happy with Azure Synapse Analytics's feature set?

Yes

Did Azure Synapse Analytics live up to sales and marketing promises?

Yes

Did implementation of Azure Synapse Analytics go as expected?

Yes

Would you buy Azure Synapse Analytics again?

Yes

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.

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