Traditional ETL vs AWS Glue - Data Warehousing

ETL stands for Extraction, Transformation, and Load, and we will discuss these topics:

Extraction: The process of retrieving data from one or more sources such as CRM, ERP, operational systems, legacy data, social media data, data from third parties, and so on.
Transformation: The process of mapping, reformatting, conforming, adding meaning, and more to prepare the data for easier consumption, like currency conversion from USD to JPY before storage.
Loading: Involves inserting data into a target database or data warehouse.
Cloud and ETL: With the explosion of internet-connected devices collecting data, the amount and variety of data have increased exponentially. Data integration tools, including ETL (mainly referring to the batch portion of data integration), are essential for managing these tasks at scale and on time. Many sources and targets are now in the cloud, such as Snowflake or AWS Redshift in the data warehousing space.

Cloud and ETL

Data Integration tools that can complete these tasks on scale and are able to complete them on time are increasingly important as the number of sources and targets on the cloud grows. These tools allow users to create, store, and run ETL jobs online, such as AWS Glue (ETL as a service). Traditional ETL tools often require maintenance, like software updates, and live on-premise servers.

Serverless ETL	Traditional ETL
ETL pipeline job runs as code on servers maintained off-premise or in the cloud	ETL pipeline jobs typically run in on-premise servers that are maintained, sometimes by another team
ETL tools such as AWS Glue allow users to customize the code for serverless ETL operations within the ETL data pipeline.	Traditional ETL tools often have a canvas-based function represented by an icon with configurable UI for customizing ETL operations in a data pipeline

What to read next?

AWS Glue (Serverless ETL)

Data Warehousing: Traditional ETL vs AWS Glue

In the realm of data processing, two popular methods have emerged over time: Traditional Extract, Transform, Load (ETL) and Amazon Web Services' (AWS) Glue. This article aims to shed light on both approaches, comparing their functionalities, strengths, and potential use cases.

Traditional ETL

Overview

ETL is a batch-oriented data integration technique designed to extract data from disparate sources (often known as operational systems), transform it to meet the requirements of a target system (usually a data warehouse), and load the transformed data into the data warehouse for analysis.

Components

Extraction: The process of pulling data from various sources.
Transformation: The process of cleaning, filtering, and structuring the extracted data to fit the target system's requirements.
Loading: The process of writing the transformed data into the target system, which is typically a data warehouse or data mart.

Example ETL Workflow (Pseudocode)

``` foreach source_system in sources: extract data from source_system transform extracted data load transformed data into target system ```

AWS Glue

Overview

AWS Glue is a fully managed Extract, Transform, Load (ETL) service that makes it easy for customers to move data between various data stores, such as Amazon S3 and databases, at any scale. It simplifies the ETL process by automating much of the heavy lifting involved in creating and executing ETL jobs.

Components

Crawlers: Auto-generated metadata about your data sources, which are used to optimize job creation and execution.
ETL Jobs: Automated data integration workflows that include Extract, Transform, and Load steps.

Example Glue Workflow (Pseudocode)

``` create a crawler to gather metadata about the source system use the generated metadata to automatically create an ETL job execute the ETL job, which includes extraction, transformation, and loading of data ```

Comparing Traditional ETL and AWS Glue

Dimension	Traditional ETL	AWS Glue
Management	Requires more manual management and configuration	Fully managed by AWS with minimal user intervention required
Scalability	Scaling requires additional resources to be provisioned and configured	Automatically scales based on data volume and complexity
Optimization	Optimizations must be implemented manually or with custom scripts	Automatically optimizes data processing through intelligent job scheduling and resource allocation

Conclusion

In the ongoing quest to efficiently manage and analyze vast amounts of data, understanding the nuances between Traditional ETL and AWS Glue is crucial. While both methods serve similar purposes, AWS Glue shines in its ability to automate much of the heavy lifting associated with data integration. When considering your data warehousing needs, it's essential to weigh the specific requirements of your use case against the advantages that each approach offers.