I been working long with on-premises DWH solutions. Now moving to AZURE DWH.
Right now am up-to doing most of the processing / transformation in Azure Databricks and writing the result set to Azure SQL DWH Staging Tables.
Now I want to MERGE (UPSERT) the Dimensions and Load Fact Tables.
As MERGE is not supported in AZURE SQL DWH, what is the best way to accomplish this?
MERGE is not support with AZURE SQL DWH, Azure SQL DWH team said they are planning to support this feature.
Reference: MERGE statement support.
I found this blog, MSFT give an example to use UPDATE/INSERT statements instead of MERGE.
Hope this helps.
Related
Unity Catalog is the Azure Databricks data governance solution for the Lakehouse. Whereas, Microsoft Purview provides a unified data governance solution to help manage and govern your on-premises, multicloud, and software as a service (SaaS) data.
Question: In our same Azure Cloud project, can we use Unity Catalog for the Azure Databricks Lakehouse, and use Microsoft Purview for the rest of our Azure project?
Update: In our current Azure subscription, we have divided workload as follows:
SQL related workload: we are doing all our SQL database work using Databricks only (no Azure SQL databases are involved). That is, we are using Databricks Lakehouse, Delta Lake, Deatricks SQL etc. to perform ETL and all Data Analytics work.
All Non-SQL workload: All other assets (Excel files, csv files, pdf, media files etc.) are stored in various Azure storage accounts.
MS Purview is doing a good job in scanning assets in scenario 2 above, and it easily creates a holistic, up-to-date map of our data landscape with automated data discovery, sensitive data classification, and end-to-end data lineage. It also enables our data consumers to access valuable, trustworthy data management.
However, our almost 50% of the work (SQL, ETL, Data Analytics etc.) is done in Azure Databricks where we have significant challenges with Purview. We were wondering if it's possible to keep Purview and Unity Catalog separate as follows: Purview does its Data Governance work for scenario 1 only and Unity Catalog does its Data Governance work for scenario 2 only.
This recently released update may resolve our issue of making Purview work better with Azure Databricks but we have not tried it yet: Connect to and manage Azure Databricks in Microsoft Purview (Preview)
As of right now there is no official integration between Unity Catalog and Purview yet, but it may come in the future. You may join Azure Databricks roadmap webinar that will be tomorrow to get more information.
Regarding the actual question - imho, nothing prevents you from using UC & Purview in the same Azure project.
P.S. You can get metadata & lineage information into Purview by loading data from information schema tables and using Purview APIs to store it in Purview.
I'm trying to get my head around Databricks.
I've found documentation stepping through importing data from S3 or Azure Datalake, and then outputting into Azure Synapse Analytics or another Data Warehouse solution.
After a quick play, I've recognised that you can simply save a table in Databricks, access it using SQL, and even pull it into PowerBI as a source.
So my question: for a small Datamart (10 dims, 5 facts), why would I choose to pay for an additional database solution like Azure SQL, Synapse, RDS or other when I could simply leave the data in a table in Databricks and then access it directly from my reporting tool from there?
Thank you in advance.
Andy
Yes this is very much possible . Just to let you know that SQL Azure and Synapse may be a Microsoft offering but they are for different purpose , Synapse supports MPP and so it more big data implementation . Also its not only how many dimension and fact table you have , how much data you have , what kind of aggregation it has etc becomes decisive .
I wanted to move some data on daily basis from azure data lake to Big query using Azure Data Factory. However, ADF does not support Big Query as sink. What would you suggest? Any GCP service analogue to ADF to perform this task?
Thanks!
However, ADF does not support Big Query as sink.
Yes, ADF can only support Google Big Query as the source. So this means ADF can not achieve your requirement.
Any GCP service analogue to ADF to perform this task?
It seems that there is no ready-made tool, maybe you can write code to get data from datalake and copy it?
I have been trying to use Azure Databricks to push changes happening at azure data lake (source) to azure SQL data warehouse (destination). If someone could help to list down the steps involved and how can it be achieved?
Also I would want to put it across in a very cost efficient way like the cluster should create/start and delete/stop at run-time. Will I require a job to do it?
Please let me know!
I'm no MS expert - recently hopped onto the Azure train and apologies in advance if I get some information wrong.
Basically need some input in Azure's architecture utilising Azure Data Factory (as the ETL/ELT tool) and Azure SQL database (as the storage), to a BI output - Power BI. My situation is this;
I have on-premise data sources such as Oracle DB, Oracle Cloud SSAS, MS SQL server db
I'd like to have a MS cloud infrastructure solution for reporting purposes.
No data migration needed - merely pumping on-prem data onto cloud and producing a BI reporting solution
Based on my limited knowledge and Google research, Azure Data Factory caters for all my on-prem sources, as well as the future cloud Azure SQL database. If future analysis is needed, Azure Storage and Azure Databricks can be added in to this architecture. I have sketched out the architecture of my proposed solution.
Just confirming my understanding
Without Azure Storage & Databricks (the 2 pink boxes), the 2 Azure component (DF & SQL database) is sufficient to take data from on-premise sources, process on cloud & output into Power BI.
With Azure Storage & Databricks (the 2 pink boxes), processing will be more efficient as their summarised function is to store training data models & act as an analytics processing engine.
Azure SQL database is more suitable, as compared to Azure SQL datawarehouse as my data sources does not exceed 1TB; cost-wise is cheaper AND one of my data sources contain data from call centers, hence OLTP is more suitable. Plus I have Azure Databricks to support the analytical bit that SQL datawarehouse does (OLAP).
Any other comments to help me understand this whole architecture will be great!
I am a new learner of Azure. I was wondering if we have #Query (value="...") kind or any equivalence for DocumentDb (CosmosDB). Because, the documentDB does not take #Query. I am looking to convert the sql query (From jpa to cosmosDB).
Taking data from on-prem or IaaS sources like SQL on a VM, Oracle etc, requires a Self-Hosted Integration Runtime (SHIR).
Please review the Modern Data Warehouse pattern which sounds similar to what you are proposing.