As an AWS Advanced Partner, Kitsilano Technologies deploys AWS Glue to help African organisations build scalable, secure, and cost-effective data integration pipelines. Whether you’re feeding a data warehouse, preparing data for machine learning, or synchronising operational databases, AWS Glue eliminates the infrastructure overhead that has historically made ETL projects slow and expensive.
Key Takeaways
- ✓AWS Glue is fully serverless — you pay only for the DPUs consumed during job execution, with no idle infrastructure costs.
- ✓The Glue Data Catalog acts as a persistent, centralised metadata store integrated directly with Athena, EMR, and Redshift Spectrum.
- ✓Glue Crawlers automatically discover and catalogue schemas across S3, RDS, Redshift, and 70+ other sources.
- ✓Glue Streaming ETL processes data from Kinesis and Kafka in near real-time for event-driven pipelines.
- ✓Data quality rules can be applied inside pipelines to catch anomalies before bad data reaches analytics consumers.
What is AWS Glue?
AWS Glue is a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application development. It covers the full ETL lifecycle:
The AWS Glue documentation covers the full service architecture and all supported connector types.
- Extract
Connect to 70+ data sources and ingest structured and semi-structured data.
- Transform
Apply business logic, schema mapping, data quality rules, and enrichments in Python or Scala.
- Load
Write transformed data to Amazon S3, Redshift, RDS, or any JDBC-compatible target.
Key Features
Serverless Architecture
AWS Glue manages the infrastructure automatically, you define your ETL logic and Glue provisions the compute, runs the job, and scales down when complete. No servers to manage, no idle capacity to pay for.
ETL Automation
Glue generates ETL code automatically from your source and target schema definitions. Customise the generated Python or Scala script as needed, then schedule jobs to run on triggers or on a defined schedule.
AWS Glue Data Catalog
A centralised, persistent metadata store that catalogues all your data assets, tables, schemas, partitions, across S3, RDS, Redshift, and more. Integrated directly with Athena, EMR, and Redshift Spectrum.
Data Quality
AWS Glue Data Quality lets you define quality rules and run automated checks on your data as it flows through your pipelines, catching anomalies, missing values, and schema violations before they reach analytics consumers.
Scalability
Glue scales automatically from processing megabytes to petabytes. Dynamic Frame allocation adjusts the number of DPUs (Data Processing Units) based on the volume of data being processed, you pay only for what you use.
AWS Ecosystem Integration
Native connectors for Amazon S3, RDS, Aurora, Redshift, DynamoDB, Kinesis, Kafka, JDBC databases, and 70+ SaaS sources, enabling you to connect all your data sources without writing custom integration code.
See the AWS Glue product page for current DPU pricing across regions.
Common Use Cases
Data Warehouse Ingestion
Continuously extract transactional data from operational databases (RDS, Aurora, SAP), transform it for analytical schemas, and load it into Amazon Redshift for business intelligence reporting.
Data Lake Preparation
Process raw data landed in Amazon S3, converting formats (JSON, CSV, XML → Parquet), applying deduplication, and partitioning data optimally for query performance with Athena and EMR.
ML Feature Engineering
Build feature engineering pipelines that transform raw operational data into structured datasets for machine learning model training, ensuring consistent, reproducible feature generation across training and inference.
Real-Time and Micro-Batch Processing
Glue Streaming ETL processes data from Kinesis Data Streams and Kafka in near real-time, enabling use cases like fraud detection, operational dashboards, and event-driven data pipelines.
How Kitsilano Technologies Delivers AWS Glue Solutions
As an AWS Advanced Partner, Kitsilano Technologies provides end-to-end AWS Glue implementations, from architecture design and connector configuration to pipeline development, data quality implementation, and ongoing optimisation:
- Source system discovery and data profiling to understand the structure and quality of your raw data.
- Data catalog configuration mapping all data assets to business domains and ownership.
- ETL job development with business transformation logic, error handling, and retry mechanisms.
- Data quality rules implementation to catch and quarantine invalid or anomalous records automatically.
- Scheduling, monitoring, and alerting setup to ensure pipeline reliability in production.
- Cost optimisation through job partitioning, DPU sizing, and Glue DataBrew for low-code transformations.
Implementation Tip
Start with AWS Glue Crawlers to automatically discover and catalogue your existing data sources. Crawlers infer schemas, detect partitions, and update the Data Catalog automatically as your data evolves, eliminating the need to maintain schema definitions manually.
“Data without integration is just storage. Integration without quality is just noise.”
AWS Glue addresses both, connecting your data estate and ensuring the data that reaches analysts and models is accurate, timely, and governed.
Frequently Asked Questions
Glue is fully serverless — you define your transformation logic and Glue handles all compute provisioning automatically. EMR gives you a persistent cluster of EC2 instances running Spark or Hadoop, with fine-grained control over cluster configuration. Glue is the right choice for scheduled, event-driven ETL pipelines. EMR suits complex, long-running jobs where you need direct cluster control.
Glue charges for Data Processing Units (DPUs) consumed during job execution, crawlers, and interactive sessions. One DPU equals 4 vCPUs and 16 GB of memory. Billing is per second with a 1-minute minimum. The Glue Data Catalog costs $1 per 100,000 objects stored after the free tier. There is no charge for idle time — serverless means you pay only for what you use.
Yes. Glue connects to on-premise JDBC-compatible databases through AWS Direct Connect or a site-to-site VPN. You configure a Glue Connection with the JDBC URL and credentials, and Glue handles the rest. For databases without native JDBC support, the Glue Connector Library provides a framework for building custom connectors.
Glue ETL jobs support Python (PySpark) and Scala. Glue Studio provides a visual drag-and-drop interface for pipeline authoring without code. AWS Glue DataBrew offers a low-code data preparation environment for analysts who need to profile, clean, and normalise data without writing Python or Scala.
