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datahub | The Metadata Platform for the Modern Data Stack

 by   linkedin Java Version: v0.8.28 License: Apache-2.0

 by   linkedin Java Version: v0.8.28 License: Apache-2.0

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kandi X-RAY | datahub Summary

datahub is a Java library typically used in Big Data, Kafka, Spark applications. datahub has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install datahub' or download it from GitHub, PyPI.
DataHub is an open-source metadata platform for the modern data stack. Read about the architectures of different metadata systems and why DataHub excels here. Also read our LinkedIn Engineering blog post, check out our Strata presentation and watch our Crunch Conference Talk. You should also visit DataHub Architecture to get a better understanding of how DataHub is implemented and DataHub Onboarding Guide to understand how to extend DataHub for your own use cases.
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  • datahub has a medium active ecosystem.
  • It has 4881 star(s) with 1332 fork(s). There are 221 watchers for this library.
  • There were 10 major release(s) in the last 12 months.
  • There are 141 open issues and 824 have been closed. On average issues are closed in 9 days. There are 37 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of datahub is v0.8.28
datahub Support
Best in #Java
Average in #Java
datahub Support
Best in #Java
Average in #Java

quality kandi Quality

  • datahub has no bugs reported.
datahub Quality
Best in #Java
Average in #Java
datahub Quality
Best in #Java
Average in #Java

securitySecurity

  • datahub has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
datahub Security
Best in #Java
Average in #Java
datahub Security
Best in #Java
Average in #Java

license License

  • datahub is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
datahub License
Best in #Java
Average in #Java
datahub License
Best in #Java
Average in #Java

buildReuse

  • datahub releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions are available. Examples and code snippets are not available.
datahub Reuse
Best in #Java
Average in #Java
datahub Reuse
Best in #Java
Average in #Java
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datahub Key Features

Check out DataHub's Features & Roadmap.

Convert JSON to a newline-delimited list of all the things tagged "name" with jq

copy iconCopydownload iconDownload
$ jq -r '.[].name' world_cities.json
Newport Beach
Nipomo
Norco
North Glendale
North Highlands

How to append data in single cell while writing data of multiple for loop in a csv through Pandas?

copy iconCopydownload iconDownload
pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])
import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])
c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)
pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])
import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])
c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)
pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])
import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])
c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)

Running a custom JavaScript task in the backend Marklogic server

copy iconCopydownload iconDownload
task getCollections(type: com.marklogic.gradle.task.MarkLogicTask) {
  doLast {
    def client = getAppConfig().newDatabaseClient()
    String request = """
      cts.collections().toArray().join("; ")
    """;

    try {
      String result
      result = client.newServerEval().javascript(request).evalAs(String.class);
      if (result != null) {
        println result
      }
    } finally {
      client.release()
    }
  }
}

How can I make an interactive world map in dash-leaflet?

copy iconCopydownload iconDownload
import dash_leaflet as dl
from dash import Dash

# An url pointing to the data that you want to show.
url = "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"
# Create example app.
app = Dash()
app.layout = dl.Map(children=[dl.TileLayer(), dl.GeoJSON(url=url)],
                    style={'width': '100%', 'height': '50vh', 'margin': "auto", "display": "block"})

if __name__ == '__main__':
    app.run_server()

datahub 505 HTTP Version Not Supported

copy iconCopydownload iconDownload
location /somelocation/ {
      proxy_pass http://localhost:8080/;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection 'upgrade';
      proxy_set_header Host $host;
      proxy_cache_bypass $http_upgrade;
  }

Why am I unable to import this CSV file?

copy iconCopydownload iconDownload
import pandas as pd
import pandas as pd
pip install pandas
import pandas as pd
pip install pandas

Converting geojson into sf for a clotopleth map

copy iconCopydownload iconDownload
library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

sf <- geojsonsf::geojson_sf(geojson)
sf <- sf::st_read(geojson)
library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

sf <- geojsonsf::geojson_sf(geojson)
sf <- sf::st_read(geojson)
library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

sf <- geojsonsf::geojson_sf(geojson)
sf <- sf::st_read(geojson)

Get country name from ISO code in Javascript

copy iconCopydownload iconDownload
var getCountryNames = new Intl.DisplayNames(['en'], {type: 'region'});
console.log(getCountryNames.of('AL'));  // "Albania"

How do I search and replace using a loop data in 2 data frame?

copy iconCopydownload iconDownload
readURL = "https://storage.googleapis.com/covid19-open-data/v2/vaccinations.csv" 
Frame = read.csv(readURL)


readCountryURL = "https://pkgstore.datahub.io/core/country-list/data_csv/data/d7c9d7cfb42cb69f4422dec222dbbaa8/data_csv.csv"
country <- read.csv(readCountryURL, na.strings = '')


library(stringr)
Frame$country <- str_replace_all(str_remove(Frame$key, '_.*'), 
                                 setNames(country$Name, country$Code))

How to send a &quot;string array&quot; as HTTP post data from Python?

copy iconCopydownload iconDownload
postData = {'meteringPointIds': "1234"}
postData = {
    "meteringPoints": {
        "meteringPoint": [
            "1234"
        ]
    }
}
postData = {
    "meteringPointIds": {
        "meteringPoints": {
            "meteringPoint": [
                "1234"
            ]
        }
    }
}
postData = {
    "meteringPoints": {
        "meteringPoint": [
            "1234"
        ]
    }
}
postData = {
    "meteringPointIds": {
        "meteringPoints": {
            "meteringPoint": [
                "1234"
            ]
        }
    }
}

Community Discussions

Trending Discussions on datahub
  • Convert JSON to a newline-delimited list of all the things tagged &quot;name&quot; with jq
  • How to append data in single cell while writing data of multiple for loop in a csv through Pandas?
  • Running a custom JavaScript task in the backend Marklogic server
  • How to assign multiple inputs and outputs to app.callback with hover_feature or click_feature in dash-leaflet?
  • How can I make an interactive world map in dash-leaflet?
  • Mongodb: ignore large documents ( BSON &gt; 16 MB) during collection.aggregate()
  • datahub 505 HTTP Version Not Supported
  • Why am I unable to import this CSV file?
  • Converting geojson into sf for a clotopleth map
  • Get country name from ISO code in Javascript
Trending Discussions on datahub

QUESTION

Convert JSON to a newline-delimited list of all the things tagged &quot;name&quot; with jq

Asked 2022-Mar-17 at 21:00

I'm trying to make a .txt list of cities from a dataset of cities with more than 15000 people. The JSON is structured like this:

[
  {
    "country": "United States",
    "geonameid": 5376890,
    "name": "Newport Beach",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377100,
    "name": "Nipomo",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377199,
    "name": "Norco",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377613,
    "name": "North Glendale",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377640,
    "name": "North Highlands",
    "subcountry": "California"
  }
]

I want to take this and make it into a newline-delimited list of all the things tagged "name" like this:

Newport Beach
Nipomo
Norco
North Glendale
North Highlands

I tried to do this on the command line using a tool I found called jq, which I though would work by writing something like

cat world_cities.json | jq '.name' > cities_list.txt

but I got an error that said "jq: error (at :0): Cannot index array with string "name"". Of course, I'm sure I'm not fully understanding how jq is supposed to work, and I don't have very much experience parsing JSON, but I'm having trouble finding an answer to my specific problem online. Does anyone know what I can potentially do to achieve what I'm trying to achieve from the command line?

If you want to see the whole dataset I'm trying to parse, you can find it here: https://pkgstore.datahub.io/core/world-cities/world-cities_json/data/5b3dd46ad10990bca47b04b4739a02ba/world-cities_json.json

ANSWER

Answered 2022-Mar-17 at 21:00
$ jq -r '.[].name' world_cities.json
Newport Beach
Nipomo
Norco
North Glendale
North Highlands

The data is surrounded by an array. You can access the elements with .[]: . is the array itself and [] gives its elements. Then .name gives you each element's name.

Finally, use -r to have jq output raw strings without quotes.

Source https://stackoverflow.com/questions/71518883

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install datahub

Please follow the DataHub Quickstart Guide to get a copy of DataHub up & running locally using Docker. As the guide assumes some basic knowledge of Docker, we'd recommend you to go through the "Hello World" example of A Docker Tutorial for Beginners if Docker is completely foreign to you.

Support

We have documentation available at https://datahubproject.io/docs/.

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