I am wondering if it is possible to natively write to geoparquet (v1 released) format on azure using synapse notebooks.
Regards
The particular combination of software enables reduction in imports or over coding done to achieve storing data in latest format.
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I have a question about Azure Data Factory (ADF). I have read (and heard) contradictory info about ADF being ETL or ELT. So, is ADF ETL? Or, is it ETL? To my knowledge, ELT uses the transformation (compute?) engine of the target (whereas ETL uses a dedicated transformation engine). To my knowledge, ADF uses Databricks under the hood, which is really just an on-demand Spark cluster. That Spark cluster is separate from the target. So, that would mean that ADF is ETL. But, I'm not confident about this.
Good question.
It all depends on what you use and how you use it.
If it is strictly a copy activity, then it is ELT.
https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-overview
The transform can be a stored procedure (does not matter RDBMS) and the source/destination are tables. If the landing zone is a data lake, then you want to call a Databricks or Synapse notebook. Again, the source is a file. The target is probably a delta table. Most people love SQL and delta tables give you those ACID properties.
Now, if you using a mapping or wrangling data flow, then it is ETL, if the pattern is pure. Of course you can mix and match. Both these data flows use a spark engine. It cost money to have big spark clusters running.
https://learn.microsoft.com/en-us/azure/data-factory/concepts-data-flow-overview
https://learn.microsoft.com/en-us/azure/data-factory/wrangling-tutorial
Here is an article from MSDN.
https://learn.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl
It has old (SSIS) and new (SYNAPSE) technologies.
I have two sources:
A csv that will be uploaded to a cloud storage service, probably GCP Cloud Storage.
The output of a scrapping process done with Python.
When a user updates 1) (the cloud stored file) an event should be triggered to execute 2) (the scrapping process) and then some transformation should take place in order to merge these two sources into one in a JSON format. Finally, the content of this JSON file should be stored in a DB of easy access and low cost. The files the user will update are of max 5MB and the updates will take place once weekly.
From what I've read, I can use GCP Cloud Functions to accomplish this whole process or I can use Dataflow too. I've even considered using both. I've also thought of using MongoDB to store the JSON objects of the two sources final merge.
Why should I use Cloud Functions, Dataflow or both? What are your thoughts on the DB? I'm open to different approaches. Thanks.
Regarding de use of Cloud Functions and Dataflow. In your case I will go for Cloud Functions as you don't have a big volume of data. Dataflow is more complex, more expensive and you will have to use Apache Beam. If you are confortable with python and having into consideration your scenario I will choose Cloud Functions. Easy, convenient...
To trigger a Cloud Functions when Cloud Storage object is updated you will have to configure the triggers. Pretty easy.
https://cloud.google.com/functions/docs/calling/storage
Regarding the DB. MongoDB is a good option but if you wanth something quick an inexpensive consider DataStore
As a managed service it will make your life easy with a lot of native integrations. Also it has a very interesting free tier.
My assumptions where MDF might be right fit are as follows:
MDF can be used as a Data Wrangling Tool by end-users
MDF is better suited for SQL Server-based Datawarehouse architectures to load the data into staging or data lake in clean format (prepare the data before loading it to SQL Server DWH and then use a proper ETL tool to do transformations)
If MDF has to be used for light ELT / ETL tasks directly on Data Lake or DWH, it needs customization for complex transformations...
My question would be:
A) Did anyone use Mapping Data Flow in production for option 2 and 3 above?
B) If assumption 3 is valid, would you suggest going for Spark-based transformation or an ETL tool rather than patching the MDF with customizations as new versions might not be compatible with, etc..
I disagree with most of your assumptions. Data Flow is a part of a larger ETL environment, either Data Factory (ADF) or Azure Synapse Pipelines and you really can't separate it from it's host. Data Flow is a UI code generator that executes at runtime as a Spark job. If your end user is a data engineer, then yes Data Flow is a good tool for them.
ADF is a great service for orchestrating data operations. ADF supports all the things you mentioned (SSIS, Notebooks, Stored Procedures, and many more). It also supports Data Flow, which is absolutely a "proper" tool for transformations and has a very rich feature set. In fact, if you are NOT doing transformations, Data Flow is likely overkill for your solution.
I'm converting a process from postgreSQL over to DataBrick ApacheSpark,
The postgresql process uses the following sql function to get the point on a map from a X and Y value. ST_Transform(ST_SetSrid(ST_MakePoint(x, y),4326),3857)
Does anyone know how I can achieve this same logic in SparkSQL o databricks?
To achieve this you need to use some library, like, Apache Sedona, GeoMesa, or something else. Sedona, for example, has the ST_TRANSFORM function, maybe it has the rest as well.
The only thing that you need to take care, is that if you're using pure SQL, then on Databricks you will need:
install Sedona libraries using the init script, so libraries should be there before Spark starts
set Spark configuration parameters, as described in the following pull request
Update June 2022nd: people at Databricks developed the Mosaic library that is heavily optimized for geospatial analysis on Databricks, and it's compatible with standard ST_ functions.
I need to migrate the tables from the BigQuery to the on-prem Postgres database.
How can I efficiently achieve that?
Some thoughts that are coming
I will use Google APIs to export the data from the tables
Store it locally
And finally, import to Postgres
But I am not sure if that can be done for a huge amount of data in TBs. Also, how can I automate this process? Can I use Jenkins for that?
Exporting the data from BigQuery, store it and importing it to PostgreSQL is a good approach. Here are other two alternatives that you can consider:
1) There's a PostgreSQL wrapper for BigQuery that allows to query directly from BigQuery. Depending on your case scenario this might be the easiest way to transfer the data; although, for TBs it might not be the best approach. This suggestion was made by #David in this SO question.
2) Using Dataflow. You can create a ETL process using Apache Beam to made the transfer. Take a look at this how-to for transferring data from BigQuery to CloudSQL. You would need to adapt it for local PostgreSQL, but the idea maintains.
Here's another SO answer that gives more context on this approach.