I am moving from SSIS to Azure.
we have 100's of files and MSSQL tables that we want to push into a Gen2 data lake
using 3 zones then SQL Data Lake
Zones being Raw, Staging & Presentation (Change names as you wish)
What is the best process to automate this as much as possible
for example build a table with files / folders / tables to bring into Raw zone
then have Synapse bring these objects either full or incremental load
then process the them into the next 2 zones I guess more custom code as we progress.
Your requirement can be accomplished using multiple activities in Azure Data Factory.
To migrate SSIS packages, you need to use SSIS Integrated Runtime (IR). ADF supports SSIS Integration which can be configured by creating a new SSIS Integration runtime. To create the same, click on the Configure SSIS Integration, provide the basic details and create a new runtime.
Refer below image to create new SSIS IR.
Refer this third-party tutorial by SQLShack to Move local SSIS packages to Azure Data Factory.
Now, to copy the data to different zones using copy activity. You can make as much copy of your data as your requirement using copy activity. Refer Copy data between Azure data stores using Azure Data Factory.
ADF also supports Incrementally load data using Change Data Capture (CDC).
Note: Both Azure SQL MI and SQL Server support the Change Data Capture technology.
Tumbling window trigger and CDC window parameters need to be configured to make the incremental load automated. Check this official tutorial.
The last part:
then process them into the next 2 zones
This you need to manage programmatically as there is no such feature available in ADF which can update the other copies of the data based on CDC. You need to either create a separate CDC for those zones or do it logically.
Related
I can create an Azure data lake database with pre-built tables using Azure Synapse database templates from the Synapse Studio UI, but is there a way to use these templates programmatically? So far from my research I have not found a command, API, or SDK for this. Perhaps I could create the database and tables via the UI, then generate the associated spark sql creation scripts, but don't see a way how to do that either. Does anyone have any ideas on how to do either of the prior?
You can create the data lake storage, tables and data insertion programmatically using Azure SDKs. But these templates have been made available to overcome these series of manual tasks. Using these templates save your time and efforts to create an environment and sample data for your development.
Therefore, asking to deploy these templates programmatically challenging the complete concept of templates. If you want to deploy these resources manually, you can use Azure SDKs.
I am archiving rows that are older than a year into ADLSv2 as delta tables, when there is a need to report on that data, I need to join archived data with some tables existing on on-premise database. Is there a way we can do a join without re-hydrating from or hydrating data to cloud?
Yes, you can achieve this task by using Azure Data Factory.
Azure Data Factory (ADF) is a fully managed, serverless data integration
service. Visually integrate data sources with more than 90 built-in,
maintenance-free connectors at no added cost. Easily construct ETL and
ELT processes code-free in an intuitive environment or write your own
code.
Firstly, you need to install the Self-hosted Integration Runtime in your local machine to access the on-premises SQL Server in ADF. To accomplish this, refer Connect to On-premises Data in Azure Data Factory with the Self-hosted Integration Runtime.
As you have archived the data in ADLS, you need to change the Access tier of that container from Cold -> Hot in order to retrieve the data in ADF.
Later, create a Linked Service using Self-hosted IR which you have created. Create a Dataset using this Linked Service to access the on-premises database.
Similarly, create a Linked Service using default Azure IR. Create a Dataset using this Linked Service to access the data from ADLS.
Now, you also require a destination database where you will store the data after join. If you are storing it in same on-premises database, you can use the existing Linked Service but you need to create a new Dataset mentioning the destination table name.
Once all this configuration done, create a Data Flow activity pipeline in ADF.
Mapping data flows are visually designed data transformations in Azure
Data Factory. Data flows allow data engineers to develop data
transformation logic without writing code. The resulting data flows
are executed as activities within Azure Data Factory pipelines that
use scaled-out Apache Spark clusters.
Learn more about Mapping data flow here.
Finally, in data-flow activity, your sources will be on-premises dataset and ADLS dataset which you have created above. You will be using join transformation in mapping data flow to combine data from two sources. The output stream will include all columns from both sources matched based on a join condition.
The sink transformation will take your destination dataset where the data will be stored as an output.
I read a few threads on this but noticed most are outdated, with excel becoming an integration in 2020.
I have a few excel files stored in Drobox, I would like to automate the extraction of that data into azure data factory, perform some ETL functions with data coming from other sources, and finally push the final, complete table to Azure SQL.
I would like to ask what is the most efficient way of doing so?
Would it be on the basis of automating a logic app to extract the xlsx files into Azure Blob, use data factory for ETL, join with other SQL tables, and finally push the final table to Azure SQL?
Appreciate it!
Before using Logic app to extract excel file Know Issues and Limitations with respect to excel connectors.
If you are importing large files using logic app depending on size of files you are importing consider this thread once - logic apps vs azure functions for large files
Just to summarize approach, I have mentioned below steps:
Step1: Use Azure Logic app to upload excel files from Dropbox to blob storage
Step2: Create data factory pipeline with copy data activity
Step3: Use blob storage service as a source dataset.
Step4: Create SQL database with required schema.
Step5: Do schema mapping
Step6: Finally Use SQL database table as sink
I have a need to load data into Azure Hyperscale incrementally.
Source data is in Azure VM that has SQL server installed in it.
Source database is about 6Tb in size and has about 370 tables.
We need a way to get incremental changes in the last X amount of hours and sync them into the same database in Hyperscale.
Ideally, we would extend our database with the availability group setup but since Hyperscale does not support that, we need to find a way to keep these in sync.
Source database does have change data capture enabled.
The best on-line migration option is to use the Azure Database Migration Service (link) where the Online (continuous sync) migration support scenario (link) you need is supported:
The sync will essentially run in the background until completed while being able to access the data that has been migrated. I believe this is a continuous copy scenario and is not incremental. With PaaS database services, you do not have access to perform snapshot replication operations from external data sources. The Hyperscale instance is built upon snapshot replication but it currently only serves the hosted database functionality.
Regards,
Mike
I need expert opinion on a project I am working on. We currently get data files that we load into our Azure sql database using a local script that calls stored procedures. I am planning on replacing the script with ssis jobs to load the data into our Azure Sql but wondering if that's a good option given our needs.I am opened to different suggestions too. The process we go through is to load data file to staging tables and validate before making updates to live tables. The validation and updates are done by calling stored procedures...so the ssis package will just load the data and make calls to those stored procedures. I have looked at ADF IR and Databricks but they seem overkill but am open to hear people with experience using those as well. I am currently running the ssis package locally as well. Any suggestion on better architecture or tools for this scenario? Thanks!
I would definitely have a look at Azure Data Factory Data flows. With this you can easily build your ETL pipelines in the a Azure Data Factory GUI.
In the following example two text files from a Blob Storage are read, joined, a surrogate key is added and finally the data is loaded to Azure Synapse Analytics (would be the same for Azure SQL):
You finally put this Mapping Data Flow into a pipeline and can trigger it, e. g. if new data arrives.
You can just BULK INSERT data from Azure Blob Store:
https://learn.microsoft.com/en-us/sql/relational-databases/import-export/examples-of-bulk-access-to-data-in-azure-blob-storage?view=sql-server-ver15#accessing-data-in-a-csv-file-referencing-an-azure-blob-storage-location
Then you can use ADF (no IR) or Databricks or Azure Batch or Azure Elastic Jobs to schedule the execution.