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Conditional writes to DynamoDB when executing an AWS glue script without Boto?

I've written an AWS glue job ETL script in python, and I'm looking for the proper way to perform conditional writes to the DynamoDb table I'm using as the target.
# Write to DynamoDB
glueContext.write_dynamic_frame_from_options(
frame=SelectFromCollection_node1665510217343,
connection_type="dynamodb",
connection_options={
"dynamodb.output.tableName": args["OUTPUT_TABLE_NAME"]
}
)
My script is writing to dynamo with write_dynamic_frame_from_options. The aws glue connection parameter docs make no mention of the ability to customize the write behavior in the connection options.
Is there a clean way to write conditionally without using boto?

You cannot do conditional updates with the EMR DynamoDB connector which Glue uses. It does a complete overwrite of the data. For that you would have to use Boto3 and distribute it using forEachPartition across the Spark executors.

Is there any way to replicate realtime streaming from azure blob storage to to azure my sql

We can basically use databricks as intermediate but I'm stuck on the python script to replicate data from blob storage to azure my sql every 30 second we are using CSV file here.The script needs to store the csv's in current timestamps.

There is no ready stream option for mysql in spark/databricks as it is not stream source/sink technology.
You can use in databricks writeStream .forEach(df) or .forEachBatch(df) option. This way it create temporary dataframe which you can save in place of your choice (so write to mysql).
Personally I would go for simple solution. In Azure Data Factory is enough to create two datasets (can be even without it) - one mysql, one blob and use pipeline with Copy activity to transfer data.

Snowflake - compare 2 tables and send notification for mismatches

I am looking for setting up a alert notification either from snowflake or aws side or by glue jobs / lambda functions using python or scala.
I would like to compare 2 tables which holds table names and counts in source and target.
data is loaded from s3 to snowflake via aws glue job and after that I would like to compare the 2 tables to verify if source and target record counts are matching and for any mismatches send a notification.
Please let me know your inputs to achieve this task.
Thanks,
Jo

If you are using AWS Glue to load the tables in Snowflake, you can continue using Glue to orchestrate the desired result:
Have Glue load the table.
Have Glue run a stored procedure in Snowflake comparing both tables.
https://snowflakecommunity.force.com/s/article/How-to-Use-AWS-Glue-to-Call-Procedures-in-Snowflake
Have AWS Glue send a notification through SNS.
https://aws.amazon.com/blogs/big-data/build-and-automate-a-serverless-data-lake-using-an-aws-glue-trigger-for-the-data-catalog-and-etl-jobs/
See the chapter "Monitoring and notification with Amazon CloudWatch Events".
If you need SQL for the stored procedure that compares two tables, please feel free to add a new question.

AWS Redshift Data Processing

I'm working with a small company currently that stores all of their app data in an AWS Redshift cluster. I have been tasked with doing some data processing and machine learning on the data in that Redshift cluster.
The first task I need to do requires some basic transforming of existing data in that cluster into some new tables based on some fairly simple SQL logic. In an MSSQL environment, I would simply put all the logic into a parameterized stored procedure and schedule it via SQL Server Agent Jobs. However, sprocs don't appear to be a thing in Redshift. How would I go about creating a SQL job and scheduling it to run nightly (for example) in an AWS environment?
The other task I have involves developing a machine learning model (in Python) and scoring records in that Redshift database. What's the best way to host my python logic and do the data processing if the plan is to pull data from that Redshift cluster, score it, and then insert it into a new table on the same cluster? It seems like I could spin up an EC2 instance, host my python scripts on there, do the processing on there as well, and schedule the scripts to run via cron?
I see tons of AWS (and non-AWS) products that look like they might be relevant (AWS Glue/Data Pipeline/EMR), but there's so many that I'm a little overwhelmed. Thanks in advance for the assistance!

ETL
Amazon Redshift does not support stored procedures. Also, I should point out that stored procedures are generally a bad thing because you are putting logic into a storage layer, which makes it very hard to migrate to other solutions in the future. (I know of many Oracle customers who have locked themselves into never being able to change technologies!)
You should run your ETL logic external to Redshift, simply using Redshift as a database. This could be as simple as running a script that uses psql to call Redshift, such as:
`psql <authentication stuff> -c 'insert into z select a, b, from x'`
(Use psql v8, upon which Redshift was based.)
Alternatively, you could use more sophisticated ETL tools such as AWS Glue (not currently in every Region) or 3rd-party tools such as Bryte.
Machine Learning
Yes, you could run code on an EC2 instance. If it is small, you could use AWS Lambda (maximum 5 minutes run-time). Many ML users like using Spark on Amazon EMR. It depends upon the technology stack you require.
Amazon CloudWatch Events can schedule Lambda functions, which could then launch EC2 instances that could do your processing and then self-Terminate.
Lots of options, indeed!

The 2 options for running ETL on Redshift
Create some "create table as" type SQL, which will take your source
tables as input and generate your target (transformed table)
Do the transformation outside of the database using an ETL tool. For
example EMR or Glue.
Generally, in an MPP environment such as Redshift, the best practice is to push the ETL to the powerful database (i.e. option 1).
Only consider taking the ETL outside of Redshift (option 2) where SQL is not the ideal tool for the transformation, or the transformation is likely to take a huge amount of compute resource.
There is no inbuilt scheduling or orchestration tool. Apache Airflow is a good option if you need something more full featured than cron jobs.

Basic transforming of existing data
It seems you are a python developer (as you told you are developing Python based ML model), you can do the transformation by following the steps below:
You can use boto3 (https://aws.amazon.com/sdk-for-python/) in order
to talk with Redshift from any workstation of you LAN (make sure
your IP has proper privilege)
You can write your own functions using Python that mimics stored procedures. Inside these functions, you can put / constrict your transformation
logic.
Alternatively, you can create function-using python in Redshift as well that will act like Stored Procedure. See more here
(https://aws.amazon.com/blogs/big-data/introduction-to-python-udfs-in-amazon-redshift/)
Finally, you can use windows scheduler / corn job to schedule your Python scripts with parameters like SQL Server Agent job does
Best way to host my python logic
It seems to me you are reading some data from Redshift then create test and training set and finally get some predicted result (records).If so:
Host the scrip in any of your server (LAN) and connect to Redshift using boto3. If you need to get large number of rows to be transferred over internet, then EC2 in the same region will be an option. Enable the EC2 in ad-hoc basis, complete you job and disable it. It will be cost effective. You can do it using AWS framework. I have done this using .Net framework. I assume boto3 does have this support.
If your result set are relatively smaller you can directly save them into the target redshift table
If result sets are larger save them into CSV (there are several Python libraries) and upload the rows into a staging table using copy command if you need any intermediate calculation. If not, upload them directly into the target table.
Hope this helps.

Combining many log files in Amazon S3 and read in locally

I have a log file being stored in Amazon S3 every 10 minutes. I am trying to access weeks and months worth of these log files and read it into python.
I have used boto to open and read every key and append all the logs together but it's way too slow. I am looking for an alternate solution to this. Do you have any suggestion?

There is no functionality on Amazon S3 to combine or manipulate files.
I would recommend using the AWS Command-Line Interface (CLI) to synchronize files to a local directory using the aws s3 sync command. This can copy files in parallel and supports multi-part transfer for large files.
Running that command regularly can bring down a copy of the files, then your app can combine the files rather quickly.
If you do this from an Amazon EC2 instance, there is no charge for data transfer. If you download to a computer via the Internet, then Data Transfer charges apply.

Your first problem is that you're naive solution is probably only using a single connection and isn't making full use of your network bandwidth. You can try to roll your own multi-threading support, but it's probably better to experiment with existing clients that already do this (s4cmd, aws-cli, s3gof3r)
Once you're making full use of your bandwidth, there are then some further tricks you can use to boost your transfer speed to S3.
Tip 1 of this SumoLogic article has some good info on these first two areas of optimization.
Also, note that you'll need to modify your key layout if you hope to consistently get above 100 requests per second.
Given a year's worth of this log file is only ~50k objects, a multi-connection client on a fast ec2 instance should be workable. However, if that's not cutting it, the next step up is to use EMR. For instance, you can use S3DistCP to concatenate your log chunks into larger objects that should be faster to pull down. (Or see this AWS Big Data blog post for some crazy overengineering) Alternatively, you can do your log processing in EMR with something like mrjob.
Finally, there's also Amazon's new Athena product that allows you to query data stored in S3 and may be appropriate for your needs.