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Note that the vignette will not execute that code chunk: if you want to run with live data, you'll have to do it yourself separately. Given the size, if you're running this locally and don't have a fast connection, feel free to grab only a year or two of data. If you don't have the taxi data downloaded, the vignette will still run and will yield previously cached output for reference. To be explicit about which version is running, let's check whether we're running with live data:.

Because dplyr is not necessary for many Arrow workflows, it is an optional Suggests dependency. So, to work with Datasets, we need to load both arrow and dplyr. The partitioning argument lets us specify how the file paths provide information about how the dataset is chunked into different files. Our files in this example have file paths like. Code Revisions 1 Stars 5 Forks 1. Embed What would you like to do?

Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Spark - Parquet files. Contents Exit focus mode. Is this page helpful? Please rate your experience Yes No. Any additional feedback? Submit and view feedback for This product This page. View all page feedback. In this article. We first load the data into a DataFrame and strip off data without a date:. We can start by writing the data for For simplicity, we reduce the number of partitions to 2.

We get records, as we expected, and a few of them are for inspections of type B :. Select your Data Lake linked service. If necessary, add a parameter, change the compression type, or modify the schema.

The last step of this tutorial is to create a pipeline to move information between your database and your Data Lake. Then, configure your source by selecting the table or using the query.

By not selecting the table in the dataset, you will have more flexibility to re-use the same dataset across different tables without creating 1 dataset per table.

Next, you need to define the dataset for your Data Lake that you have previously created. You are ready to trigger your pipeline. In the following scenario, you manually trigger the execution of the activity, but you can also define a schedule that executes the activity automatically. Query with Serverless Azure Synapse Analytics.