SAP Vora extends Apache Spark’s capabilities by providing in-memory, distributed analytics on big data in enterprise environments. To unlock the full potential of SAP Vora, developers often build custom applications that ingest, process, and analyze data using familiar programming languages. SAP Vora supports development with popular languages such as Python, R, and Java, enabling data scientists, analysts, and developers to integrate big data analytics into their workflows.
This article explores how to develop applications with SAP Vora using Python, R, and Java, highlighting integration approaches, APIs, and best practices.
SAP Vora’s tight integration with Apache Spark makes it accessible through Spark’s language APIs:
These languages enable flexible data processing, interactive analytics, and application development tailored to diverse user skill sets.
Python developers leverage PySpark, Spark’s Python API, to interact with Vora data sources and perform distributed computations.
Example of reading from and writing to Vora using PySpark:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("VoraPythonApp") \
.getOrCreate()
# Read Vora table
vora_df = spark.read.format("com.sap.vora") \
.option("tableName", "sales_data") \
.load()
# Perform transformations
result_df = vora_df.groupBy("region").sum("sales_amount")
# Write back to Vora
result_df.write.format("com.sap.vora") \
.option("tableName", "regional_sales_summary") \
.mode("overwrite") \
.save()
R users can interact with SAP Vora through SparkR or the more user-friendly sparklyr package, which provides a dplyr interface for Spark.
Example using sparklyr:
library(sparklyr)
library(dplyr)
sc <- spark_connect(master = "yarn")
# Read Vora table
vora_tbl <- tbl(sc, sql("SELECT * FROM sales_data"))
# Aggregate data
summary_tbl <- vora_tbl %>%
group_by(region) %>%
summarise(total_sales = sum(sales_amount))
# Write summary back to Vora
spark_write_table(summary_tbl, "regional_sales_summary", mode = "overwrite")
Java developers use Spark’s native API to build robust, production-grade applications interfacing with SAP Vora.
Example Java snippet to read and write Vora tables:
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
public class VoraJavaApp {
public static void main(String[] args) {
SparkSession spark = SparkSession.builder()
.appName("VoraJavaApp")
.getOrCreate();
// Read Vora table
Dataset<Row> voraDF = spark.read()
.format("com.sap.vora")
.option("tableName", "sales_data")
.load();
// Perform aggregation
Dataset<Row> resultDF = voraDF.groupBy("region")
.sum("sales_amount");
// Write results back to Vora
resultDF.write()
.format("com.sap.vora")
.option("tableName", "regional_sales_summary")
.mode("overwrite")
.save();
spark.stop();
}
}
SAP Vora empowers developers to build scalable, high-performance applications on big data by leveraging familiar programming languages like Python, R, and Java. Whether for data science, real-time analytics, or enterprise application development, the integration of Vora with Apache Spark’s APIs provides a flexible and powerful platform.
By adopting language-specific tools and best practices, organizations can accelerate their analytics initiatives and unlock actionable insights from complex, large-scale data.