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AWS Glue python ApplyMapping / apply_mapping example

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The ApplyMapping class is a type conversion and field renaming function for your data. To apply the map, you need two things: A dataframe The mapping list
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The Glue code that runs on AWS Glue and on Dev Endpoint

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When you develop code for Glue with the Dev Endpoint , you soon get annoyed with the fact that the code is different in Glue vs on Dev Endpoint glueContext is created in a different manner there's no concept of 'job' on dev endpoint, and therefore no arguments for the job, either So Mike from The MIS Theorist asked if there was a simpler way. And sure there is!
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AWS Glue, Dev Endpoint and Zeppelin Notebook

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AWS Glue is quite a powerful tool. What I like about it is that it's managed : you don't need to take care of infrastructure yourself, but instead AWS hosts it for you. You can schedule scripts to run in the morning and your data will be in its right place by the time you get to work. The downside is that developing scripts for AWS Glue is cumbersom , a real pain in the butt. I first tried to code the scripts through the console, but you end up waiting a lot only to realize you had a syntax error in your code.
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Popular posts from this blog

How to access AWS S3 with pyspark locally using AWS profiles tutorial

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At Zervant , we currently use databricks for our ETL processes, and it's quite great. However, there's been some difficulty in setting up scripts that work both locally and on the databricks cloud. Specifically, databricks uses their own prorpietary libraries to connect to AWS S3 based on AWS hadoop 2.7. That version does not support accessing using AWS profiles. Internally, we use SSO to create temporary credentials for an AWS profile that then assumes a role. Therefore, reading the ACCESS_ID and ACCESS_SECRET from the .credentials file is something we don't want to do. In order to accomplish this, we need to set two hadoop configurations to the Spark Context fs.s3a.aws.credentials.provider com.amazonaws.auth.profile.ProfileCredentialsProvider This is done by running this line of code: sc._jsc.hadoopConfiguration().set("fs.s3a.aws.credentials.provider", "com.amazonaws.auth.profile.ProfileCredentialsProvider") Note! You need to set your environment var...
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Snowflake UPSERT operation (aka MERGE)

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You want to insert data to a table, but if a corresponding row already exists (by some rule, e.g. unique key) you want to update that instead of adding a new row, keeping the dataset's unique requirements intact. That's an "UPDATE AND INSERT" operation, or UPSERT. Some SQL languages have native support for it.  PostgreSQL has UPSERT as native. Also MySQL supports the operation with INSERT and ON DUPLICATE KEY UPDATE. How do you do UPSERT on Snowflake? Here's how: Snowflake UPSERT i.e. MERGE operation Snowflake's UPSERT is called MERGE and it works just as conveniently. It just has a different name. Here's the simple usage: MERGE INTO workspace.destination_table d USING workspace.source_table s ON d.id = s.id AND d.val1 = s.val1 WHEN MATCHED THEN update SET d.val2 = s.val2, d.val3 = s.val3 WHEN NOT MATCHED THEN INSERT (id, val1, val2, val3) VALUES (s.id, s.val1, s.val2, s.val3); Here the destination_table and source_table are of similar form,...
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