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Data transformation
in AWS
#cloudconf2022
Alessandra Bilardi
Data & Automation Specialist
alessandra.bilardi@corley.it
corley.it
The importance
Methods
AWS Services
Comparison
SOMMARIO
The importance of data transformation
1 Quantity
2 Quality
3 Noise
4 Compatibility
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
AWS data transformation services
1 AWS Glue
DataBrew
2 AWS Data Pipeline
3 Amazon SageMaker
Data Wrangler
4 Notebooks
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
Questions
1 Qual è il mio obiettivo ?
2 Quanto esperto sono ?
3 Quante risorse ho ?
4 Qual è lo strumento giusto ?
Contacts
Thanks for listening.
#cloudconf2022

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