Accessing data with Spark
There are two general methods to access data with Spark, with various benefits and disadvantages, depending on how the data is managed. The two methods are Direct Reader and JDBC.
- JDBC: Use in the following cases:
- Use JDBC connections when you have fine-grained access.
- If the scale of data sent over the wire is on the order of tens of thousands of rows of data.
- Direct Reader: Use in the following cases:
- Your data has table-level access control, and does not have row-level security or column level masking (fine-grained access.)
For both methods, add the Python or R code, as described below, in the Session where you want to utilize the data, and update the code with the data location information.
In addition, check with the Administrator that you have the correct permissions to access the data lake. You will need a role that has read access only. For more information, see -link-.
How to obtain the Data Lake directory location
- In the CDP home page, select Management Console.
- In Environments, select the Environment you are using.
- In the tabbed section, select Cloud Storage.
- Choose the location where your data is stored.
- For managed data tables, copy the location shown by Hive Metastore Warehouse.
- For external unmanaged data tables, copy the location shown by Hive Metastore External Warehouse
- Paste the location into the script in line 9. If you are using AWS, the location starts
s3:, and if you are using Azure, it starts with
abfs:. If you are using a different location in the data lake, the default path is shown by Hbase Root.
Check for an updated version of the jar file
This jar file name may need to be updated with the correct version number.
- Click Terminal Access, and run:
- Check the file name and version number of the jar file.
Set up a JDBC Connection
When using a JDBC connection, you read through a virtual warehouse that has Hive or Impala installed. You need to obtain the JDBC connection string, and paste it into the script in your session.
- In CDW, go to the Hive database containing your data.
- From the kebab menu, click Copy JDBC URL.
- Paste it into the script in your session.
- You also have to enter your user name and password in the script. You should set up environmental variables to store these values, instead of hardcoding them in the script.