feat: Kickoff Transformation implementation #5130
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Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
| def get_feature_transformation(self) -> Optional[Transformation]: | ||
| if not self.udf: | ||
| return None | ||
| if self.mode == TransformationMode.pandas or self.mode == "pandas": |
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Probably can just do a dictionary mapping
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sure will add that
| udf_string: str = "", | ||
| tags: Optional[Dict[str, str]] = None, | ||
| description: str = "", | ||
| owner: str = "", |
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Probably can add the singleton parameter too
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| class TransformationMode(Enum): | ||
| PYTHON = "python" |
HaoXuAI
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Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
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| def __init__( | ||
| self, | ||
| *, | ||
| name: str, | ||
| mode: Union[TransformationMode, str], |
| class TransformationMode(Enum): | ||
| PYTHON = "python" | ||
| PANDAS = "pandas" | ||
| spark = "spark" |
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Suggested change
| spark = "spark" | |
| SPARK = "spark" |
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
HaoXuAI
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HaoXuAI
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Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
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Do you think we can scope one type of batch transformation as our MVP? If so, which one would you feel most comfortable doing? |
yep, maybe Spark SQL. that should be easy to implement, and we can get it live soon. |
LFG! |
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What this PR does / why we need it:
Created a Transformation interface. it still works with the current pandas_transformation, python_transformation etc.
The next step is refactor the BatchMaterializationEngine to make it works for both Materialization and Transformation.
Which issue(s) this PR fixes:
#4584
#4277 (comment)
#4696
Misc