Data transformation on AWS - 2022-10-11
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This document discusses data transformation in AWS. It covers the importance of data transformation in terms of quantity, quality, and noise/compatibility. It then describes common transformation methods like extraction, parsing, cleaning, and enrichment. Several AWS services for data transformation are presented, including AWS Glue, AWS DataBrew, AWS Data Pipeline, Amazon SageMaker Data Wrangler, and notebooks. These services are compared based on difficulty, execution times, and costs.
Data transformation on AWS - 2022-10-11
- 2. Alessandra Bilardi Data & Automation Specialist [email protected] corley.it
- 4. The importance of data transformation 1 Quantity 2 Quality 3 Noise 4 Compatibility
- 5. AWS data transformation methods 1 Extraction, parsing, reduction, cleaning, anonymization and encryption 2 Translation, typecasting, formatting, renaming, and mapping 3 Filtering, aggregation, summarization, indexing and ordering 4 Enrichment
- 6. AWS data transformation services 1 AWS Glue DataBrew 2 AWS Data Pipeline 3 Amazon SageMaker Data Wrangler 4 Notebooks
- 7. Comparison of AWS data transformation services Services Difficulty Execution times Costs Glue (Job) 🏖🤓🔧 17 secs (12 s) $ 0.00 DataBrew 🏖🏖📚 1 min 19 secs $ 0.16 Notebooks 🏖🤓🔧 2 mins (1.61s) $ 0.00 SageMaker (Job) 🤓🔧🔧 5 mins (1m 12s) $ 0.02 Data Wrangler 🏖📚🔧 5 mins (1m 12s) $ 0.74
- 8. Questions 1 Qual è il mio obiettivo ? 2 Quanto esperto sono ? 3 Quante risorse ho ? 4 Qual è lo strumento giusto ?
- 9. Contacts