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- 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.
- 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
- The combination of SQL/unstructured data
- Keeping things "complicated, but simple"; [heterogeneous] data formats seen as just SQL tables to business experts used to use Power BI, Excel, and any other traditional SQL-oriented BI tools
- Integration options using "Synapse pipelines", the application of ADFs
- The greatly integrated solution of independent things (Spark MPP cluster, MPP SQL Servers, ADFs) - all sitting under one roof. Great job!
- Integration with super-fast, globally replicated data. I really appreciate the integration of NoSQL databases (namely Core API and Mongo API under Cosmos DB) with purely batch-processed BI data
- I have no idea right now. But... Synapse Analytics are typically seen as batch-processing of source data. What about tighter cooperation with streaming features like Event Hubs?
- fast query results
- integrated systems
- one application/area for all processes
- Delta Lake doesn't have full capabilities yet
- spark doesn't yet have delta live tables
- coding differences from Databricks' spark aren't well documented
- Easy to Manage data
- Blazing fast query processing
- Supports Modern fileformats
- Documentation and Usecases
- Admin capabilities
- 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
- It is very cost-effective
- Development time needed was much less in comparison to other systems
- Played very nicely with our ETL and OLAP reporting tools
- More features would be a plus
- I would like to see Microsoft offer some diagramming tools for data warehouse
- I believe processing time and speed could always be improved
SQL Data Warehouse is always well suited in a Microsoft SQL environment. When you are using tools like SSIS, SSAS and SSRS, SQL Data Warehouse fits in nicely as the OLAP backend.
Some challenges faced for this product are in very large expansive environments where the transact databases might be coming from different sources like Oracle or Sybase.
- 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.
- It takes some time to setup a proper SQL Datawarehouse architecture. Without proper SSIS/automation scripts, this can be a very daunting task.
- It takes a lot of foresight when designing a Data Warehouse. If not properly designed, it can be very troublesome to use and/or modify later on.
- It takes a lot of effort to maintain. Businesses are continually changing. With that, a full time staff member or more will be required to maintain the SQL Data Warehouse.