Documentation from Microsoft and others strongly emphasizes the separation between storage and compute in Azure Synapse Analytics.
In the case of a Serverless SQL pool, it is clearly explained that the data is stored in an Azure Data Lake DSL Gen2.
However, in the case of a Dedicated SQL Pool, the documentation is not explicit enough on data storage.
In a book that deals with Azure Synapse, it is stated that in the case of Dedicated SQL Pool, data is stored in Storage Nodes which are completely separate from Compute Nodes.
Since this claim is not in Microsoft's documentation, I dare not trust it.
So, is there an official resource that sheds light on this question?
This is a question that has been on my mind for a long time as well. However, I have come to the conclusion that data is actually stored in Dedicated SQL Pools.
Let me explain why I believe this.
Take a look at the documentation given here,
https://learn.microsoft.com/en-us/azure/synapse-analytics/quickstart-copy-activity-load-sql-pool
Notice that it is about loading data into a Dedicated SQL Pool. Further, to quote part of the documentation,
A dedicated SQL pool offers T-SQL based compute and storage
capabilities. After creating a dedicated SQL pool in your Synapse
workspace, data can be loaded, modeled, processed, and delivered for
faster analytic insight.
It is said that Dedicated SQL Pools provide both compute and storage capabilities.
Furthermore, with Dedicated SQL Pools, you may already know that it is possible to create traditional tables. We can organize these tables into something along the lines of a star or snowflake schema to model our data warehouses.
Creation of such tables, however, is not possible with Serverless SQL Pools. Only the creation of metadata objects, i.e. views or external tables are allowed. This is explained here,
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/on-demand-workspace-overview
To quote the relevant passage of the article,
Serverless SQL pool has no local storage, only metadata objects are
stored in databases. Therefore, T-SQL related to the following
concepts isn't supported:
Tables Triggers Materialized views DDL statements other than ones
related to views and security DML statements
To me, the fact that tables can actually be created in Dedicated SQL Pools is further proof that the data is physically stored in them.
My final argument is around the idea of distributions. The concept is explained here,
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/massively-parallel-processing-mpp-architecture
This talks about how data is divided up among the compute nodes and how queries are executed in parallel on the distributions in these nodes. It would not be possible to implement this if the data was not actually stored in these nodes.
In my humble opinion, how I believe Azure Storage comes into the picture (at least, when it comes to Dedicated SQL Pools) is with regards to storing data as files in a data lake and then ingesting them into the pool for analysis.
An explanation can be found here,
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/overview-architecture
Yet another quote,
Serverless SQL pool allows you to query your data lake files, while
dedicated SQL pool allows you to query and ingest data from your data
lake files. When data is ingested into dedicated SQL pool, the data is
sharded into distributions to optimize the performance of the system.
This is where Polybase comes into play. You can define various data loading patterns (into Dedicated SQL Pools) using Polybase as explained here,
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/load-data-overview
The Microsoft documentation on Design tables using dedicated SQL pool in Azure Synapse Analytics, found at https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-overview, states the following:
Table persistence: Tables store data either permanently in Azure
Storage, temporarily in Azure Storage, or in a data store external to
dedicated SQL pool.
Regular table A regular table stores data in Azure Storage as part of
dedicated SQL pool...
Related
We are currently extracting multiple tables from Azure SQL Servereless pool in Synapse. Unlike a regular Azure SQL Database it is very easy to increase the performance from Basic all the way through to Premium or Business continuity.
Can someone let me know how to go about increasing the performance of Azure SQL Serverles Pool in synapse?
Serverless SQL pool is a distributed data processing system and it doesn't have any inbuilt storage to store data. It uses external table to query the data from Azure data lake storage. Therefore, data cannot be copied to the serverless SQL pool. If data needs to be extracted from serverless SQL pool, you can extract data directly from the underlying external storage. If the target datastore supports polybase data loading, use that to load to the target table from ADLS.
I have question regarding SQL Pool. Not sure i understood what it is. Does SQL Pool service is the service for SQL Server type databases? I have Postgres database and consider to move it to Azure nevertheless what is there any usage of SQL Pool service in case of Azure Postgres or it's only for Azure SQL Server database? Last: Does SQL Pool also used by Synapse ETL?
Azure SQL Pool is used with Azure Synapse Analytics to query Big Data. You can consider it as a Data Warehouse. Once your dedicated SQL pool is created, you can import big data with simple PolyBase T-SQL queries, and then use the power of the distributed query engine to run high-performance analytics.
How SQL Pool works? In a cloud data solution, data is ingested into big data stores from a variety of sources. Once in a big data store, Hadoop, Spark, and machine learning algorithms prepare and train the data. When the data is ready for complex analysis, dedicated SQL pool uses PolyBase to query the big data stores. PolyBase uses standard T-SQL queries to bring the data into dedicated SQL pool tables.
No, PostgreSQL can't be used in SQL Pool. There is actually no link between these two services. If you want to migrate the on-premises PostgreSQL to Azure, you can use Azure Database for PostgreSQL. Check Tutorial: Migrate PostgreSQL to Azure DB for PostgreSQL online using DMS via the Azure CLI.
I have N databases, for example 10 databases.
Every database has the same schema, but different data.
Now i would like to take every data of each database from the table "Table1" and insert them in a common table in a new database "DWHDatabase" in a table named Table1Common.
so it's an insert like n to 1.
How i can do that? i'm trying to solve my issues with the elastic queries but seems it's a 1 to 1 stuff
Use Azure Data Factory with Linked Services to each database. Use the Copy activity to load the data.
You can also paramaterize the solution.
Parameterize linked services
Parameters in Azure Data Factory by Catherine Wilhemsen
Elastic query is best suited for reporting scenarios in which the majority of the processing (filtering, aggregation) may be done on the external source side. It is unsuitable for ETL procedures involving significant amounts of data transfer from a distant database (s). Consider Azure Synapse Analytics for large reporting workloads or data warehousing applications with more sophisticated queries.
You may use the Copy activity to copy data across on-premises and
cloud-based data storage. After you've copied the data, you may use
other actions to alter and analyse it. The Copy activity may also be
used to publish transformation and analysis findings for use in
business intelligence (BI) and application consumption.
MSFT Copy Activity Overview: Here.
While creating stored procedures in Azure data warehouse, I have got some error on "Cursor" and "FOR XML". So wanted to know if they are supported by Azure data warehouse or not. If not then what are the alternatives.
sample code with error msg pictures are attached herein.
Neither FOR XML or cursors are supported in Azure Synapse dedicated SQL pools (formerly known as Azure SQL Data Warehouse) as per the documentation. For cursors, either convert them to use a WHILE loop which is supported or refactor the code to use a set-based approach. Another alternative is to use something external, like Azure Data Factory or Synapse Pipelines and use a For Each loop. Another alternative is to use a nearby Azure SQL DB to do some pre-processing. You should be aware the the MPP architecture of Azure Synapse Analytics does not lend itself well to this kind of row-based processing and you should remember it's a big data platform meant for large volumes of data, millions, billions of rows and set-based approaches should be preferred.
If you are just using FOR XML to do that sleazy string concatenation trick then you should use STRING_AGG instead which is fully supported in Synapse. See this answer for a recent example. If you are actually producing XML then you will need to find an alternative method, eg a nearby Azure SQL DB.
I have the following set up:
Azure service
Azure SQL database
Azure Table Storage
Azure Blob Storage
I am trying to develop a backup strategy for this service.
The thing is, that SQL, Tables and BLOBs should be synced. In the backup all three of those have to be of the same version. (backups taken at the same moment). And the main problem is - I can only afford several minutes downtime, not more than that.
What should I do? May be there is existing solution?
Windows Azure Storage supports geo-replication for Blobs, Tables and Queues. Data in the storage account is made durable by replicating transactions across different storage nodes in the same region (LRS) or a secondary region (GRS). GRS is the default redundancy option when creating a storage account. Refer to http://blogs.msdn.com/b/windowsazurestorage/archive/2013/12/11/introducing-read-access-geo-replicated-storage-ra-grs-for-windows-azure-storage.aspx for more details.
If you want to build a custom backup solution then you could use the techniques suggested in the below 2 blogs
1) http://blogs.msdn.com/b/windowsazurestorage/archive/2010/04/30/protecting-your-blobs-against-application-errors.aspx
2) http://blogs.msdn.com/b/windowsazurestorage/archive/2010/05/03/protecting-your-tables-against-application-errors.aspx
I am not sure of the exact use case of why you need to backup azure table and blob. You can backup All the above services without downtime; might be there would be slight glitch or bottleneck performance with SQL database durning back.
The single shot answer is to write a custom script which would read the data from azure table ( or SQL database, or the required service ) make a archive (packaging) and store it back.
The important thing to note here is where would storage backups, broadly speaking generally store the archives in blob. In this case you have thing where you would be storing, if you are storing on-premises you need calculate upon the storage locally, out bandwidth cost and latency of the data transfer from azure.
PS : cloud storage by itself has good leave of availability and durability, you further improve these factors by enabling geo-replication