semvec/pypi-packages huggingface dataset:
patch-cli 0.2.16
Spin up analytics APIs over your data in minutes, without writing any code.
Requires Python: >=3.7
Keywords: data,warehouse,data-warehouse,cli,sdk,patch,patch-tech,analytics,dashboard,graphql,api,dataset,snowflake,bigquery,cache,in-memory-datasets

# Patch CLI 

Patch is a zero-config backend for building data products like customer-facing analytics and external data APIs. Connect a source and deploy low latency Dataset APIs instantly. No need to pre-define models, metrics, or OLAP cubes.

Patch is an end-to-end solution for elevating analytics datasets into production. It often replaces a pipeline out of an analytical warehouse into a production database, the production database itself, a cache, and the access and API layers.   

### Features
* Run analytical queries over large datasets with low latency and without hitting the data warehouse
* Perform time series bucketing, aggregations, grouping, filtering and sorting without writing complicated SQL queries
* Leverage data from Snowflake, BigQuery, or Databricks (coming soon) in customer-facing applications
* Look up single row records with single digit millisecond response times
* Turn your dbt models into memory-backed APIs in minutes
* Query data from your application like a data micro-service  
* Automatically scale horizontally without setting up any infrastructure

### How does it work?
The best part is that you can instantly start building your application without setting up any infrastructure.

Patch replicates your tables into a distributed memory grid and exposes APIs to query the datasets. Data is kept up to date with a built-in change data capture process, and you can optionally integrate with your orchestration tool.  Since the tables are fully replicated, queries never hit the data warehouse directly.

## Getting started: CLI
Please follow our [Installation](https://docs.patch.tech/command-line-interface/installation) then [Basic Usage](https://docs.patch.tech/command-line-interface/basic-usage) recommendations on our documentation site.

## Getting help
If we're already in touch, Slack or email is the best place to reach out for help. If you're reading this and our teams haven't engaged yet, please provide your email at [patch.tech](https://www.patch.tech/) and we'll reach out as soon as possible! 

## License
> Copyright 2022 Patch Enterprises
> 
> Licensed under the Apache License, Version 2.0 (the "License");
> you may not use this file except in compliance with the License.
> You may obtain a copy of the License at
> 
>     http://www.apache.org/licenses/LICENSE-2.0
> 
> Unless required by applicable law or agreed to in writing, software
> distributed under the License is distributed on an "AS IS" BASIS,
> WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
> See the License for the specific language governing permissions and
> limitations under the License.
LangaraCourseInfo 2.0.0
Langara Course Information Aggregator
Requires Python: >=3.9
Keywords: langara,college,scraper,data

# LangaraCourseInfo

This project collects course and transfer information from Langara College, Vancouver, into an SQLite database.

Once built, the database weighs around 15 MB for all data (~250 MB with source HTML/PDFs), which should be lightweight enough for most uses.

The transfer agreement scraper currently takes an excruciating amount of time - approximately an hour - this will be improved with multithreading in the future.

# Collected Data

 - Course Information: course description & other attributes
 - Course Offerings: dating from 1999 - present.
 - Transfer Information: only active transfer agreements are collected.

# Table Definitions
 - TransferInformation(subject, course_code, source, destination, credit, effective_start, effective_end)
 - CourseInfo(subject, course_code, credits, title, description, lecture_hours, seminar_hours, lab_hours, AR, SC, HUM, LSC, SCI, SOC, UT)
 - Sections(year, term, seats, waitlist, subject, course_code, crn, credits, additional_fees, repeat_limit)
 - Schedules(year, term, crn, type, days,, time, start_date, end_date, room, instructor)

 - SemesterHTML(year, term, sectionHTML, catalogueHTML, attributeHTML)
 - TransferPDF(subject, pdf)

# Stack  
 - SQLite
 - Selenium
 - Beautifulsoup

# Build
- `python -m build` Build the package.
- `twine upload -r pypi dist/*` Upload the package to pypi.
Fabric39 1.15.3.post2
Fabric is a simple, Pythonic tool for remote execution and deployment (py2.7/py3.4+ compatible fork).


Fabric39 is a fork of `Fabric 3 <https://github.com/mathiasertl/fabric>`_ to provide compatibility with Python3.9+. Fabric3 is a deprecated fork of `Fabric <http://fabfile.org>`_ to provide compatability
with Python 2.7 and 3.4+. Here is the originaly description of Fabric 3:

The goal is to stay 100% compatible with the original Fabric.  Any new releases
of Fabric will also be released here.  Please file issues for any differences
you find. Known differences are `documented on github
<https://github.com/mathiasertl/fabric/>`.

To find out what's new in this version of Fabric, please see `the changelog
<http://fabfile.org/changelog.html>`_ of the original Fabric.

For more information, please see the Fabric website or execute ``fab --help``.