santhosh0000000 / sap-hive

Java program that uses Apache Spark to connect to a SAP HANA database, retrieve data from a specific table, and then write this data to a Hive table. This is a common pattern in big data processing pipelines

This Java program connects to a SAP HANA database using the Apache Spark SQL DataFrames API, retrieves data from a specified table, and then writes this data to a Hive table.

Here's a detailed explanation of the code:

1. Importing Libraries The code imports the necessary classes for working with Spark's Dataset, Row, SaveMode, and SparkSession.

2. Defining Connection Parameters The connection parameters for the SAP database are defined, including the host, port, username, password, schema, and table name. The JDBC URL is constructed using these parameters.

3. Building the SQL Query A SQL query is created to select all records from the specified schema and table in the SAP database. This query will be used to load data into a Spark DataFrame.

4. Creating the Spark Session A SparkSession is created using the SparkSession.builder() method. Here's what's happening in the configuration:

appName("SparkConnector"): Sets the application name. master("local[*]"): Specifies that the code should run locally, using all available cores. enableHiveSupport(): Enables Hive support, allowing interaction with Hive tables. config("spark.sql.warehouse.dir", "hdfs://..."): Sets the Hive warehouse directory in HDFS. 5. Loading Data from the SAP Database Data is loaded from the SAP database into a Spark DataFrame (Dataset) using the JDBC connection parameters and the SQL query created earlier. The spark.read() method is used to configure and load the data.

6. Writing Data to a Hive Table The DataFrame is then written to a Hive table with the name EXP.DEM_final_0122. The SaveMode.Overwrite option is used, meaning that if the table already exists, its contents will be overwritten with the new data.

7. Stopping the Spark Session Finally, the Spark session is stopped using spark.stop(), releasing resources associated with the session.

Considerations Database Credentials: Similar to the previous code snippet, this code includes sensitive information such as the database username and password. It is best to keep these in a secure configuration file or environment variables. Error Handling: The code doesn't include any error handling, so any issues during execution (e.g., connection failures, SQL errors) would lead to unhandled exceptions. Dependency: This code assumes that the necessary JDBC driver for SAP is available in the classpath. Hive Integration: The code assumes that Hive is properly configured and integrated with Spark, including access to the specified warehouse directory in HDFS.