Row 7140

Row ID: 7140 | Dataset Entry | Axioma AXP Content Repository

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dlt library added a very cool feature - a high level abstraction for extracting data. We're still working to improve it so feedback would be very welcome.

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* one interface is a python dict configurable (many advantages to staying in python and not going yaml) * the other are the imperative functions that power this config based extraction, if you prefer code.

​

So if you are pulling api data, it just got simpler if you use these toolkits - the extractors we added will simplify going from what you want to pull to working pipeline, while the dlt library will do best practice loading with schema evolution, unnesting and typing, giving you an end to end best practice scalable pipeline in minutes.

More details in this [blog post](https://dlthub.com/docs/blog/rest-api-source-client) which is basically a walkthrough of how you would use the declarative interface.

FieldValue
text dlt library added a very cool feature - a high level abstraction for extracting data. We're still working to improve it so feedback would be very welcome. ​ * one interface is a python dict configurable (many advantages to staying in python and not going yaml) * the other are the imperative functions that power this config based extraction, if you prefer code. ​ So if you are pulling api data, it just got simpler if you use these toolkits - the extractors we added will simplify…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-15
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  "text": "dlt library added a very cool feature - a high level abstraction for extracting data. We're still working to improve it so feedback would be very welcome.\n\n​\n\n* one interface is a python dict configurable (many advantages to staying in python and not going yaml)\n* the other are the imperative functions that power this config based extraction, if you prefer code.\n\n​\n\nSo if you are pulling api data, it just got simpler if you use these toolkits - the extractors we added will simplify going from what you want to pull to working pipeline, while the dlt library will do best practice loading with schema evolution, unnesting and typing, giving you an end to end best practice scalable pipeline in minutes.\n\nMore details in this [blog post](https://dlthub.com/docs/blog/rest-api-source-client) which is basically a walkthrough of how you would use the declarative interface.",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-15",
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