AWS Glue DataBrew vs dplyr
AWS Glue DataBrew
aws.amazon.comdplyr
dplyr.tidyverse.orgSide by side
| Figure | AWS Glue DataBrew | dplyr |
|---|---|---|
| Price from | $0.48/mo | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | — |
| Plans published | 4 | 1 |
| Spec score · Data Transformation Tools | 8.9 (#6) | 8.2 (#14) |
| Intended users | AWS identifies data analysts and data scientists who prepare data for analytics and machine learning as DataBrew’s primary users. | The documentation recommends dplyr to people learning data transformation in R for Data Science. |
| Purpose | AWS Glue DataBrew is a visual tool for preparing data for analytics and machine learning without writing code. | dplyr is a grammar of data manipulation with verbs for common data manipulation tasks. |
| Transformations | It offers over 250 built-in transformations, including options to filter anomalies, standardize formats, and correct invalid values. | Its core verbs add variables, select variables by name, filter cases by value, summarize values, and order rows. |
Plans
AWS Glue DataBrew
- DataBrew interactive sessions$1 a month
- DataBrew jobs$0.48 a month
- DataBrew interactive sessions$1, paid once
- Pay-as-you-goPriced on request
In their own words
AWS Glue DataBrew
AWS Glue DataBrew has no write-up on Specifiction yet; its figures are above.
dplyr
dplyr is a free R package for transforming data frames and tibbles through a set of code-based operations. Its main verbs add variables with mutate(), choose variables with select(), filter cases with filter(), summarize values with summarise(), and order rows with arrange(). Combine them with group_by() to apply operations to groups, or use two-table verbs to work with pairs of datasets. The across() helper repeats an operation over several columns, while rowwise() supports row-by-row work. The programming guide covers data masking, tidy selection, and use in functions or loops. For other data settings, dbplyr translates dplyr code into SQL for relational databases; the documentation also lists Arrow, dtplyr, duckplyr, and sparklyr as alternative backends for larger datasets. dplyr runs on Linux, macOS, and Windows and is licensed under MIT. The CRAN package requires R 4.1.0 or later, and installation instructions cover CRAN and a development version from GitHub. The project recommends dplyr to people learning data transformation in R for Data Science.