I am building Azure IoT solution for my BI project. For now I have an application that once per set time window sends a .csv blob to Azure Blob Storage with incremental number in name. So after some time I will have in my storage files such as 'data1.csv', 'data2.csv', 'data3.csv', etc.
Now I will need to load these data into a database which will be my warehouse with the use of Azure Stream Analytics job. The issue might be that .CSV files will have overlapping data. They will be send every 4h and contain data for past 24h. I need to always read only last file (with highest number) and prepare lookup so it properly updates data in the warehouse. What will be the best approach to make Stream Analytics read only latest file and for updating records in DB?
EDIT:
TO clarify - I am fully aware that ASA is not capable of being an ETL job. My question is what would be best approach for my case with using IoT tools
I would suggest one of these 2 ways:
use ASA to write in a temporary SQL table, and the use a SQL trigger
to update the main table of the DW with the diff.
Or remove duplicates by adding a unique constraint as described here:
https://blogs.msdn.microsoft.com/streamanalytics/2017/01/13/how-to-achieve-exactly-once-delivery-for-sql-output/
Thanks,
JS - Azure Stream Analytics
Related
I have data that needs to be moved from azure data lake to cosmosdb. The data is small, maybe < 1000 records per day. Each record is maybe < 5kb. I need this data to be exported from azure data lake and imported to cosmosdb as a timed job. The data should be moved 1 time per day. Ideally this would be configurable to many times a day. Right now I am considering using a function app to spin up on a schedule and make this export/import. However this feels wrong. I feel like there must be a better way to do this. What is the correct way to solve this problem?
You can use copy data tool, with schedule task for source: Azure Data Lake and sink: CosmosDB
See: Copy Data tool
I currently have pipelines developed that leverage Azure Data Factory for orchestration and Azure DataBricks for it's compute to perform the following actions... I receive tens of thousands of single record json files into Azure Blob in a real-time basis and on a 15 minute basis i check the folders for any new files and once found I load them into a dataframe using Databricks and load these into a single file in SQL DB before having other ADF jobs trigger stored procedures which then transform my data into final SQL tables.... We are looking to move away from Databricks as we are not using it for it's true capabilities but are of course paying the Databricks costs. Looking for ideas on other solutions to load tens of thousands of jsons into SQL DB (with minimal to no transformations) on a periodic (i.e. 15 minute) basis. We are a microsoft shop so not looking to necessarily move away from Azure tools.
Here a few ideas:
use Azure Functions + Blob Trigger / Event Grid to process the JSON files in real time (every time a new JSON file arrives, it will trigger your function). Then you could either insert into the final table or on a temporary table.
another idea would be combine Azure Functions + Blob Trigger / Event Grid to sink the data to a data lake. You can use ADF to sink it to SQL final tables.
Azure SQL DB is actually pretty capable as far as JSON goes so you could just use OPENROWSET to import the data directly from blob store and OPENJSON to shred it. You could then use a Logic App running on a schedule to call the proc say every 15 minutes, you wouldn't even need ADF as part of the solution.
I've worked up a couple of similar answers previously, eg here and here, but let me know if you want to progress more down this route and we can work up something more detailed.
I am working for an energy provider company. Currently, we are generating 1 GB data in form of flat files per day. We have decided to use azure data lake store to store our data, in which we want to do batch processing on a daily basis. My question is that what is the best way to transfer the flat files into azure data lake store? and after the data is pushed into azure I am wondering whether it is good idea to process the data with HDInsight spark? like Dataframe API or SparkSQL and finally visualize it with azure?
For a daily load from a local file system I would recommend using Azure Data Factory Version 2. You have to install Integration Runtimes on Premise (more than one for High Avalibility). You have to consider several security topics (local firewalls, network connectivity etc.) A detailed documentation can be found here. There are also some good Tutorials available. With Azure Data Factory you can trigger your upload to Azure with a Get-Metadata-Activity and use e. g. an Azure Databricks Notebook Activity for further Spark processing.
We have a Azure Storage Account with 2 blob stores. A Full and a Inc.
In the Full we place the full upload CSV files whenever a Full Upload is needed, in the Inc we just place day by day small incremental CSV Files.
We load all our data first in a staging, then to the ODS en finally to a Edw (Enterprise DW).
A full upload is only needed when there are structural changes to the tables.
Basically the only difference between the two uploads is that the full also cleares all data in the ODS and the EDW, but runs the sames stored procedures in the pipelines, ...
Anybody has tips on how to handle such a situation in a Azure Data Factory.
I would prefer not to double the data-factories, but due to the different avalability/frequency of the output datasets I can't use the same staging logical (in the data-factory) table as output dataset ....
So any hint(s) are appreciated ...
First of all to be clear ADF is just there to invoke other Azure services, it doesn't do any of the work itself. So the question really is; what services in Azure could you call from ADF to do this work and manage this situation?
To answer that...
Option 1: I would suggest you look at Azure Data Lake. I've written simply procedures to what you've described above in USQL where parameters can be passed to the USQL procedures from ADF for different types of behaviour.
The code you create can live in an Azure Data Lake Analytics database, similar to TSQL objects. Then maybe start using Azure Data Lake Storage as well, instead of normal blobs.
Option 2: Break out the C# and create yourself an Azure Data Factory custom activity and create a set of classes to do exactly what you require. Again with params passed by ADF or include logic in the methods to check the 'full' table contents. This will however involve a lot more development work and require an Azure Batch Service for the compute.
I'm trying to understand the best way to migrate a large set of data - ~ 6M text rows from (an Azure Hosted) SQL Server to Blob storage.
For the most part, these records are archived records, and are rarely accessed - blob storage made sense as a place to hold these.
I have had a look at Azure Data Factory and it seems to be the right option, but I am unsure of it fulfilling requirements.
Simply the scenario is, for each row in the table, I want to create a blob, with the contents of 1 column from this row.
I see the tutorial (i.e. https://learn.microsoft.com/en-us/azure/data-factory/data-factory-copy-activity-tutorial-using-azure-portal) is good at explaining migration of bulk-to-bulk data pipeline, but I would like to migrate from a bulk-to-many dataset.
Hope that makes sense and someone can help?
As of now, Azure Data Factory does not have anything built in like a For Each loop in SSIS. You could use a custom .net activity to do this but it would require a lot of custom code.
I would ask, if you were transferring this to another database, would you create 6 million tables all with the same structure? What is to be gained by having the separate items?
Another alternative might be converting it to JSON which would be easy using Data Factory. Here is an example I did recently moving data into DocumentDB.
Copy From OnPrem SQL server to DocumentDB using custom activity in ADF Pipeline
SSIS 2016 with the Azure Feature Pack, giving Azure Tasks such as Azure Blob Upload Task and Azure Blob Destination. You might be better off using this, maybe an OLEDB command or the For Each loop with an Azure Blob destination could be another option.
Good luck!
Azure has a ForEach activity which can be place after LookUp or Metadata to get the each row from SQL to blob
ForEach