title: PySpark on Databricks: Reading a CSV file copied from the Azure Blob Storage results in java.io.FileNotFoundException
body: <p>I am running Azure Databricks 4.3 (includes Apache Spark 2.3.1, Scala 2.11).</p>
  
  <p>I copied a <code>CSV</code> file from Azure Blob Storage into Databricks cluster using <code>dbutils.fs.cp</code> into disk by adding <code>file:</code> to the absolute <code>local_path</code>:</p>
  
  <pre><code>copy_to = "file:" + local_path
  dbutils.fs.cp(blob_storage_path, copy_to)
  </code></pre>
  
  <p>When I then try to read the file using the same path with <code>file:</code> added in front:</p>
  
  <pre><code>csv_spark_df = sqlContext.read.format('csv').options(header='true', inferSchema='true').load(copy_to)
  </code></pre>
  
  <p>I am getting an error message denoting that the given path does not exist:</p>
  
  <pre><code>java.io.FileNotFoundException: File file:/&lt;local_path&gt;
  </code></pre>
  
  <p>When I mount the Azure Blob Storage container, as described below, then I can read the file correctly with Spark using the same snippet above, using the absolute <code>local_path</code> of the file in the mounted directory:</p>
  
  <p><a href="https://docs.databricks.com/spark/latest/data-sources/azure/azure-storage.html#mount-azure-blob-storage-containers-with-dbfs" rel="nofollow noreferrer">https://docs.databricks.com/spark/latest/data-sources/azure/azure-storage.html#mount-azure-blob-storage-containers-with-dbfs</a></p>
  
  <p>Is it at all possible to read the <code>CSV</code> file that was copied from the Azure Blob Storage or is the solution using mounting of the Azure Blob Storage container the preferred one anyway?</p>