![]() ![]() We use an AWS Glue workflow to orchestrate the process. We define an AWS Glue crawler with a custom classifier for each file or data type. ACH and check payment records are consolidated into a table that is useful for performing business analytics using Amazon Athena. As part of the ingestion, these two data types need to be merged to get a consolidated view of all payments. Both ACH and check payments data files, which are in fixed width format, need to be ingested in the data lake incrementally over a time series. ACH is a computer-based electronic network for processing transactions, and check payments is a negotiable transaction drawn against deposited funds, to pay the recipient a specific amount of funds on demand. Use caseįor this post, we use automated clearing house ( ACH) and check payments data ingestion as an example. ![]() If the classifier recognizes the data, it stores the classification and schema of the data in the AWS Glue Data Catalog. When the crawler starts, it calls a custom classifier. You can create a custom classifier using a Grok pattern, an XML tag, JSON, or CSV. AWS Glue crawlers enable you to provide a custom classifier to classify your data. For example, if your data originates from a mainframe system that utilizes a COBOL copybook data structure, you need to define a custom classifier when crawling the data to extract the schema. You need to define a custom classifier if you want to automatically create a table definition for data that doesn’t match AWS Glue built-in classifiers. You can track the progress of each node independently or the entire workflow, making it easier to troubleshoot your pipelines. You can trigger workflows on a schedule or on-demand. Relationships can be defined and parameters passed between task nodes to enable you to build pipelines of varying complexity. A workflow consists of one of more task nodes arranged as a graph. ![]() We also demonstrate how to use custom classifiers with AWS Glue crawlers to classify fixed width data files.ĪWS Glue workflows provide a visual and programmatic tool to author data pipelines by combining AWS Glue crawlers for schema discovery and AWS Glue Spark and Python shell jobs to transform the data. This post demonstrates how to accomplish parallel ETL orchestration using AWS Glue workflows and triggers. To simplify the orchestration, you can use AWS Glue workflows. Orchestration for parallel ETL processing requires the use of multiple tools to perform a variety of operations. Extract, transform, and load (ETL) orchestration is a common mechanism for building big data pipelines. ![]()
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