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Yandex Query
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In this article:

  • Get ready to work
  • Connect to the data source
  • Run the query
  • Check the result
  • Useful links
  1. Getting started
  2. Batch processing

Analytical processing of Yandex Object Storage data

Written by
Yandex Cloud
Updated at July 7, 2026
View in Markdown
  • Get ready to work
  • Connect to the data source
  • Run the query
  • Check the result
  • Useful links

In this example, you will run analytical processing on New York City taxi ride data. The data for this example has been pre-loaded in the Yandex Object Storage bucket in Parquet files.

The output will be a histogram showing the frequency distribution of ride duration by number of rides.

To run this example:

  1. Get ready to work.
  2. Connect to the data source.
  3. Run the query.
  4. Check the result.

Note

Yandex Cloud provides the New York City taxi trips dataset as is. Yandex Cloud makes no express or implied representations, warranties, or conditions pertaining to your use of the specified dataset. To the extent permitted by your local law, Yandex Cloud shall not be liable for any loss or damage, including direct, indirect, consequential, special, incidental, or punitive, resulting from your use of the dataset.

NYC Taxi and Limousine Commission (TLC):

The data was collected and provided to the NYC Taxi and Limousine Commission (TLC) by technology providers authorized under the Taxicab & Livery Passenger Enhancement Programs (TPEP/LPEP). The taxi trip data was not created by the TLC, and the TLC makes no representations whatsoever about the accuracy of this data.

Please review the dataset’s original source and its terms of use.

Get ready to workGet ready to work

  1. Log in to the management console or sign up if you have not already. If you have not signed up yet, navigate to the management console and follow the instructions.
  2. On the Yandex Cloud Billing page, make sure you have an ACTIVE or TRIAL_ACTIVE billing account. If you do not have a billing account yet, create one.
  3. If you do not have a folder yet, create one.

Connect to the data sourceConnect to the data source

  1. In the management console, select the folder where you want to create a connection.

  2. Navigate to Yandex Query.

  3. In the left-hand panel, select Tutorial.

  4. Under Create infrastructure for tutorial, click Create connection.

    This will open the create connection page. Check the default settings; do not change them.

  5. Click Create.

    This will open the create data binding page. Check the default settings; do not change them.

  6. Click Create.

Run the queryRun the query

  1. In the query editor within the Query interface, click New analytics query.

  2. Enter the query text in the text field:

    $data =
    SELECT
        *
    FROM
        `tutorial-analytics`;
    
    $ride_time =
    SELECT
        DateTime::ToMinutes(tpep_dropoff_datetime-tpep_pickup_datetime) AS ride_time
    FROM
        $data;
    
    SELECT
        Histogram::Print(histogram(ride_time))
    FROM
        $ride_time;
    
  3. Click Run.

Check the resultCheck the result

Once executed, the query will return the distribution of taxi ride duration by number of rides.

Kind: AdaptiveWard Bins: 100 WeightsSum: 140151844.000 Min: -531231.000 Max: 43648.000
░░░░░░░░░░░░░░░░░░░░░░░░ P:   -5706.500 F:       4.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:   -4177.000 F:       3.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:   -2905.625 F:       8.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:   -1156.556 F:       9.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:     -43.545 F:    1685.000
█████████░░░░░░░░░░░░░░░░ P:       0.523 F: 3205072.000
███████████░░░░░░░░░░░░░░ P:       2.000 F: 3974384.000
█████████████████░░░░░░░░ P:       3.000 F: 6216464.000
██████████████████████░░░ P:       4.000 F: 7799899.000
████████████████████████░ P:       5.000 F: 8431504.000
█████████████████████████ P:       6.000 F: 8637705.000
████████████████████████░ P:       7.000 F: 8461147.000
███████████████████████░░ P:       8.000 F: 8122270.000
██████████████████████░░░ P:       9.000 F: 7643893.000
████████████████████░░░░░ P:      10.000 F: 7143245.000
██████████████████░░░░░░░ P:      11.000 F: 6549030.000
█████████████████░░░░░░░░ P:      12.000 F: 6013493.000
███████████████░░░░░░░░░░ P:      13.000 F: 5452450.000
██████████████░░░░░░░░░░░ P:      14.000 F: 4955050.000
████████████░░░░░░░░░░░░░ P:      15.000 F: 4470485.000
███████████░░░░░░░░░░░░░░ P:      16.000 F: 4047062.000
███████████████████░░░░░░ P:      17.474 F: 6886725.000
████████████████░░░░░░░░░ P:      19.475 F: 5569891.000
█████████████░░░░░░░░░░░░ P:      21.474 F: 4499806.000
██████████░░░░░░░░░░░░░░░ P:      23.475 F: 3646437.000
████████░░░░░░░░░░░░░░░░░ P:      25.475 F: 2962072.000
██████░░░░░░░░░░░░░░░░░░░ P:      27.476 F: 2414497.000
█████░░░░░░░░░░░░░░░░░░░░ P:      29.476 F: 1962886.000
████░░░░░░░░░░░░░░░░░░░░░ P:      31.535 F: 1676489.000
███░░░░░░░░░░░░░░░░░░░░░░ P:      33.542 F: 1301808.000
████░░░░░░░░░░░░░░░░░░░░░ P:      35.855 F: 1408697.000
███░░░░░░░░░░░░░░░░░░░░░░ P:      38.569 F: 1206848.000
███░░░░░░░░░░░░░░░░░░░░░░ P:      41.900 F: 1264922.000
██░░░░░░░░░░░░░░░░░░░░░░░ P:      45.386 F:  745821.000
█░░░░░░░░░░░░░░░░░░░░░░░░ P:      48.358 F:  597152.000
█░░░░░░░░░░░░░░░░░░░░░░░░ P:      51.440 F:  521645.000
█░░░░░░░░░░░░░░░░░░░░░░░░ P:      54.776 F:  442015.000
█░░░░░░░░░░░░░░░░░░░░░░░░ P:      58.505 F:  443528.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:      62.515 F:  344650.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:      67.911 F:  308517.000
░░░░░░░░░░░░░░░░░░░░░░░░░ P:     115.984 F:   22039.000

Useful linksUseful links

  • Named expressions in YQL
  • HISTOGRAM. Built-in YQL functions
  • SQL syntax
  • Batch processing

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© 2026 Direct Cursus Technology L.L.C.