Glue has two main components: Crawlers and workflows. The former can be used to ingest data from multiple sources and make it accessible to the user. The latter can be used to schedule and orchestrate multiple jobs. In addition, it can be used to analyze data in real time. A Glue workflow is a complex sequence of steps, which can be scheduled or started at a specified time. The workflow can also be used to create and manage triggers. For example, a workflow may be used to identify the best time to run a job to load data from a data store. It can also be used to schedule a series of crawlers.
The Glue dev endpoints, on the other hand, are Spark clusters that allow interactive PySpark code writing. This is where the awsglue Python package comes into play. It contains Python interfaces to some of the key data structures and methods in the AWS Glue service. Using this python package in conjunction with the Glue service is necessary to successfully load data from a database.
AWS Glue also has a data catalog, which is not only a source of information about a particular data store but also an integrator of multiple services. For example, it can be used to retrieve the filter value from a redshift database. In addition, it can be used to retrieve data from other data stores, which is a good example of ETL in action. The AWS Glue data catalog is a good place to begin when deciding which data stores to use, whether it’s a traditional relational database or an unstructured data source such as Redshift.
The data catalog has some neat features. For example, it can be used to identify which data sources can be used for which types of queries. It also allows you to load data from a database in order to perform a simple transformation. In addition, it can be used to create dynamic rule engines. It also provides information about other data stores, which you can leverage in order to build your own data transformation pipeline.
The awsglue Python package also contains a Python library that is not only useful for Glue tasks, but it can also be used with other services such as Amazon CloudFormation, Cloud Development Kit, and CloudFormation. Among other things, this python library will help you solve the infamous “Module Not Found” error in the AWS online Development endpoint.
In addition to the awsglue Python package, there are many other python libraries to choose from. These include the Py4J library, which provides a Python interface to the AWS Glue library. It also supports many additional features, including support for multiple languages and a built-in python package installer, called pip3. Pip3 supports package upgrade, which means that if you want to change the version of a particular package you can do so with ease.
The AWS Glue service also contains a notebook, which is a managed Jupyter notebook server. A Glue notebook is built upon the Sagemaker notebooks. This allows you to write code in a python environment without the need for a Spark cluster. The notebook is also integrated with the Glue Dev endpoints, which can be a useful way to interactively write Python code.