- #Redshift alter table hangs how to#
- #Redshift alter table hangs update#
- #Redshift alter table hangs code#
#Redshift alter table hangs code#
The core components I described above are usually created through infrastructure as code frameworks such as CloudFormation, the Cloud Development Kit (CDK) or Terraform. I have visualized the components and their interaction in the following diagram.įrom a developer’s perspective there are different entrypoints when using Glue. Workflows enable you to define and visualize the order in which crawlers and jobs are supposed to be started to facilitate the data transformation. Orchestrating the different components to get a functional data processing pipeline can be done through Glue workflows. Integration in this context usually means that it provides information to these services about where external data stores are located and how they’re organized. It integrates with many different services such as Redshift, Athena, Lake Formation, QuickSight or EMR. This data catalog is arguably the heart of Glue. You can direct it at different data stores and it tries to find out which data it holds, how it’s organized and how it can be accessed.Īn alternative to the crawler would be updating the data catalog through a Glue job.
#Redshift alter table hangs update#
Updating this data catalog could be a tedious and time-consuming manual process and that’s why there is another component of Glue that aims to initialize and update the data catalog. If you come from a networking background, you can think of it like a router that redirects you to the target destination. It only contains metadata and points to the underlying data stores. One of the main differences between data catalogs and relational databases is that the data catalog doesn’t store any of your data.
It’s organized in databases and tables, just like a relational database, which most of you might be familiar with. That’s why it makes sense to have a central system to keep track of these data stores and the data they hold. Sometimes different jobs extract from or write to the same data stores.
In real life there are usually more than one of these processes or jobs as they’re called in Glue. This is the origin of the ETL-acronym: extract-transform-load. This process starts with extracting data from one or more data stores, transforming it in some way and then loading it into one or more different data stores. The idea of Glue is to help you move data from point A to point B while also giving you the option to change the data in the process. This is not a deep dive into any specific topic, just an overview of the service, it’s components and things I consider useful information.
#Redshift alter table hangs how to#
The developer experience, fundamental PySpark concepts and how to orchestrate complex processes. We’ll start with an introduction of the core components and then take a closer look at some aspects: I thought I’d write up what I wish I had known when I began maybe it will help others. In the beginning, I struggled to build a mental model of the different components, what they do and how they interact. It’s not really a single service, but more like an umbrella encompassing multiple capabilities. AWS Glue is a service I’ve been using in multiple projects for different purposes.
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