1: I created a datastore using azure blob Storage and I selected the table which is in parquet format now i am using that table while data assets creation.
2: I am able to create a dataframe of that data asset but..
Can I perform query operation on that azure data asset inside Azure ML notebook ??
I want to perform some DQL operation on those data.
Here are the docs on working with Tables in AzureML:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?tabs=cli%2Cadls
MLTable allows you to do certain transformations on the data, such as reading, subsetting, enforcing schema.
Related
So, I have some parquet files stored in azure devops containers inside a blob storaged. I used data factory pipeline with the "copy data" connector to extract the data from a on premise Oracle database.
I'm using Synapse Analytics to do a sql query that uses some of the parquet files store in the blob container and I want to save the results of the query in another blob. Which Synapse connector can I use the make this happen? To do the query I'm using the "develop" menu inside Synapse Analytics.
To persist the results of a serverless SQL query, use CREATE EXTERNAL TABLE AS SELECT (CETAS). This will create a physical copy of the result data in your storage account as a collection of files in a folder, so you cannot specify how many files nor the naming scheme of the files.
I'm developing a pipeline that be able to insert data from a .txt file located in the Blob Storage into a table in a SQL Data Base.
Problem: Somehow the activity configuration is not working properly cause' is not reading all the records in the file and in consequence is not loading all the data into the Data Base (I realized this issue when I opened the file and compared the number of records from .text file against SQL table. Also, when I searched records from the last month in the table on SQL I didn't find them)
Note: I checked out the size limit of characters in the table from SQL and that isn't the problem.
I'd like to share with you the Data Copy activity and Source Data Set configuration as well:
Sink Dataset:
Do you know, guys what I'm doing wrong here? Hope you can help me, best regards.
P.S. Here's the Source Dataset
As discussed in comments, while using copy activity you would have to make sure to set the schema before running the activity. By design the schema mapping is left empty and has to be configured by the user either manually or asking adf to import the schema from the dataset.
Note: While using Auto create table option in sink, it automatically creates sink table (if nonexistent) in source schema,
but won't be supported when a stored procedure is specified (on the
sink side) or when staging is enabled.
Using COPY statement to load data into Azure Synapse Analytics as sink, the connector supports automatically creating destination table with DISTRIBUTION = ROUND_ROBIN if not exists based on the source schema.
Refer official doc: Copy and transform data in Azure Synapse Analytics by using Azure Data Factory or Synapse pipelines
Source...
Sink...
So Azure Synapse will be used as the sink. Additionally, an Azure Synapse table has to be created which matches the column names, column order, and column data types of source.
For dynamic mapping
If you view the pipeline code, you can see in the Translator section the JSON equivalent of the mapping section from UI.
You can reuse this as a base in Dynamic mapping to enable further copying similar files without having to manually configure schema.
Copy the JSON under mappings in translator
I have a json file stored in Azure Blob Storage and I have loaded it into Azure SQL DB using Data Factory.
Now I would like to find a way in order to load only new records from the file to my database (as the file is being updated every week or so). Is there a way to do it?
Thanks!
You can use the upsert ( slowly changing dimension type 1 ) that is already implemented in Azure Data Factory.
It will add new record and update old record that changed.
Here a quick tutorial :
https://www.youtube.com/watch?v=MzHWZ5_KMYo
I would suggest you to use Dataflow activity.
In Dataflow Activity, you have the option of alter row as shown in below image.
In Alter row you can use Upsert if condition.
Here mention condition as 1 == 1
I have the following ETL requirements for Snowflake on Azure and would like to implement the simplest possible solution because of timeline and technology constraints.
Requirements :
Load CSV data (only a few MBs) from Azure Blob Storage into Snowflake Warehouse daily into a staging table.
Transform the loaded data above within Snowflake itself where transformation is limited to just a few joins and aggregations to obtain a few measures. And finally, park this data into our final tables in a Datamart within the same Snowflake DB.
Lastly, automate the above pipeline using a schedule OR using an event based trigger (i.e. steps to kick in as soon as file lands in Blob Store).
Constraints :
We cannot use use Azure Data Factory to achieve this simplest design.
We cannot use Azure Functions to deploy Python Transformation scripts and schedule them either.
And, I found that Transformation using Snowflake SQL is a limited feature where it only allows certain things as part of COPY INTO command but does not support JOINS and GROUP BY. Furthermore, although the following THREAD suggests that scheduling SQL is possible, but that doesn't address my Transformation requirement.
Regards,
Roy
Attaching the following Idea diagram for more clarity.
https://community.snowflake.com/s/question/0D50Z00009Z3O7hSAF/how-to-schedule-jobs-from-azure-cloud-for-loading-data-from-blobscheduling-snowflake-scripts-since-dont-have-cost-for-etl-tool-purchase-for-scheduling
https://docs.snowflake.com/en/user-guide/data-load-transform.html#:~:text=Snowflake%20supports%20transforming%20data%20while,columns%20during%20a%20data%20load.
You can create snowpipe on Azure blob storage, Once snowpipe created on top of your azure blob storage, It will monitor bucket and file will be loaded into your stage table as soon as new file comes in. After copied the data into stage table you can schedule transformation SQL using snowflake task.
You can refer snowpipe creation step for azure blob storage in below link:
Snowpipe on microsoft Azure blob storage
We have an Azure SQL database where we collect a large amount of sensor data and we regularly extract the data from it and transform it a bit with a python script. The end result is a pandas DataFrame file. We would like to store the transformed data in an Azure database and use it as a source of a power BI dashboard.
On the one hand, we want to show the "almost" real-time data on a dashboard (the latency due to the transformation etc. is acceptable, but the dashboard needs to refresh very frequently, let's say once a minute), but we also want to store the transformed data and query it later e.g. to visualize the data only for a given day.
Is it possible to convert the pandas DataFrame into SQL and store it on Data Lake and stream the data from there? I read that it is possible to store structured data on Data Lake and even query it, but I am unsure if this would be the best solution.
(My current task is to choose the best database for storing the transformed data to enable both streaming and querying it later. I am very new in Azure products and I don't have a sandbox account yet to even try around and identify possible pitfalls. I've just figured out that PowerBI does not support DirectQuery for DataLake and I feel like this can be an issue - meaning we would have to query the data on DataLake at first and store it somewhere if we wanted to visualize a subset, is that correct?)
Azure Datalake is not a database, just a store for the data both structured and unstructured, so as mentioned you can't direct query it unless you have some compute capacity (Databricks, Azure Synapse, Azure DataLake Analytics, Power BI Premium with enhanced compute)
Depending on your approach, it may be best to move from Azure SQL Database and Pandas, to Azure Databricks, that can ingest the streaming data, transform, and provide an outputted table that is stored in the data lake. You will then connect Power BI to the Databricks instance and query that. The data will only be available while the cluster is running.
Moving to Databricks, will involve rewriting your Panda code to Koalas, or preferably Pyspark.
You do have the option of using Databricks to write the items back to a Azure SQL Database table. Depending on what transformations you are doing you could keep it all in Azure SQL, or if it is sensor data streaming, take the data through Azure Event Hubs, to Azure Streaming Analytics (does transformations), to Azure SQL Database (store Realtime and historical).