We had a scheduled AWS Lambda job that went out and ran an Athena query daily and inserted that data into a 3rd party utility.
This utility is no longer in use and the lambda job has been deleted but there are still a lot of the Athena queries that were run showing up in our Saved Queries list. Is there a way to batch delete these queries other than writing a utility to do it as going into Athena and selecting each one for deletion is very time consuming and ends up not being done.
You could write a script that calls ListNamedQueries, then loops through them and calls DeleteNamedQuery.
This could be done with the AWS CLI or any AWS SDK.
Related
We know that,
the procedure of writing from pyspark script (aws glue job) to AWS data catalog is to write in s3 bucket (eg.csv) use a crawler and schedule it.
Is there any other way of writing to aws glue data catalog?
I am looking for a direct way to do this.Eg. writing as a s3 file and sync to the aws glue data catalog.
You may manually specify the table. The crawler only discovers the schema. If you set the schema manually, you should be able to read your data when you run the AWS Glue Job.
We have had this same problem for one of our customers who had millions of small files within AWS S3. The crawler practically would stall and not proceed and continue to run infinitely. We came up with the following alternative approach :
A Custom Glue Python Shell job was written which leveraged AWS Wrangler to fire queries towards AWS Athena.
The Python Shell job would List the contents of folder s3:///event_date=<Put the Date Here from #2.1>
The queries fired :
alter table add partition (event_date='<event_date from above>',eventname=’List derived from above S3 List output’)
4. This was triggered to run post the main Ingestion Job via Glue Workflows.
If you are not expecting schema to change, use Glue job directly after creating manually tables using Glue Database and Table.
I'm trying to create the Architecture on AWS where a lambda function runs SQL Code to refresh a materialized view on AWS Redshift. I would like the materialized view to refresh after the daily ETL processes have completed on the Redshift cluster. Is there a way of setting up the lambda function to be triggered after a particular SQL command on the Redshift Cluster has completed?
Unfortunately, I've only seen examples of people scheduling the Lambda Function to run on particular intervals/at a particular time. Any help would be much appreciated.
A couple of ways that this can be done (out of many):
Have the ETL process trigger the Lambda - this is straight forward
if the ETL tool can generate the trigger but organizational factors
can make changing ETL frameworks difficult.
Use an S3 semaphore - have your ETL SQL UNLOAD some small data (like
a text string of metadata) to S3 where the objects creation will
trigger the Lambda. Insert the UNLOAD at the point in the ETL SQL
where you want the update to occur.
I am trying to automate an ETL pipeline that outputs data from AWS RDS MYSQL to AWS S3. I am currently using AWS Glue to do the job. When I do an initial load from RDS to S3. It captures all the data in the file which is exactly what I want. However, when I add new data to the MYSQL database and run the Glue job again. I get an empty file instead of the added rows. Any help would be MUCH appreciated.
Bookmarking rules for JDBC Sources are here. Important point to remember for JDBC sources is that values have to be increasing or decreasing order and Glue only processes new data from last checkpoint.
Typically, either an autogenerated sequence number or a datatime used as key for bookmarking
For anybody who is still struggling with this (it drove me mad, because i thought my spark code was wrong), disable bookmarking in job details.
I have multiple files present in different buckets in S3. I need to move these files to Amazon Aurora PostgreSQL every day on a schedule. Every day I will get a new file and, based on the data, insert or update will happen. I was using Glue for insert but with upsert Glue doesn't seem to be the right option. Is there a better way to handle this? I saw Load command from S3 to RDS will solve the issue but didn't get enough details on it. Any recommendations please?
You can trigger a Lambda function from S3 events, that could then process the file(s) and inject them into Aurora. Alternatively you can create a cron-type function that will run daily on whatever schedule you define.
https://docs.aws.amazon.com/lambda/latest/dg/with-s3-example.html
Is there any support for running Athena queries on a schedule? We want to query some data daily, and dump a summarized CSV file, but it would be best if this happened on an automated schedule.
Schedule an AWS Lambda task to kick this off, or use a cron job on one of your servers.