As enterprises increasingly harness big data alongside traditional structured data, efficient querying becomes essential to unlock insights across complex data landscapes. SAP Vora, an in-memory distributed computing engine built on Apache Spark, empowers organizations to perform interactive analytics on large datasets stored in Hadoop and other distributed file systems. Central to this capability is the use of SQL for Vora — a powerful query language interface designed to bridge big data processing with familiar SQL syntax.
This article delves into the essentials of SQL for Vora, illustrating how SAP professionals and data engineers can leverage SQL to query, analyze, and integrate data in SAP Vora environments.
SQL for Vora is an extended SQL dialect that supports querying both structured and semi-structured data within the SAP Vora ecosystem. It enables users to write declarative queries against data stored in Hadoop Distributed File System (HDFS), Apache Parquet files, and traditional SAP HANA tables through a unified interface.
By combining the familiarity of SQL with the distributed computing power of Apache Spark, SQL for Vora allows users to:
SQL for Vora supports standard ANSI SQL constructs such as SELECT, JOIN, GROUP BY, ORDER BY, and WINDOW FUNCTIONS. Additionally, it offers extensions for:
One of the hallmark features is the ability to execute federated queries that combine data from SAP HANA’s in-memory tables and SAP Vora’s distributed big data environment. This integration avoids costly data duplication and enables real-time analytics across diverse data sources.
SQL for Vora can query structured tables, as well as semi-structured formats such as JSON, Parquet, and ORC, which are commonly used in big data scenarios.
The SQL engine leverages Apache Spark’s distributed processing and SAP Vora’s in-memory caching to accelerate query execution on massive datasets.
Here is a simple example of querying a table named sales_data stored in the Hadoop environment:
SELECT product_id, SUM(quantity) AS total_quantity, AVG(price) AS avg_price
FROM sales_data
WHERE sales_date >= '2025-01-01'
GROUP BY product_id
ORDER BY total_quantity DESC
LIMIT 10;
This query retrieves the top 10 products by total quantity sold since the beginning of 2025, showcasing typical aggregation and filtering operations.
SQL for Vora supports complex joins between tables in Vora and SAP HANA. For example, joining customer master data stored in SAP HANA with large transaction logs in Hadoop:
SELECT c.customer_name, SUM(t.amount) AS total_spent
FROM hana.customer_master c
JOIN vora.transaction_logs t ON c.customer_id = t.customer_id
WHERE t.transaction_date BETWEEN '2025-01-01' AND '2025-05-31'
GROUP BY c.customer_name
ORDER BY total_spent DESC;
This federated query enables enterprises to combine detailed transaction data with trusted master data for richer analytics.
For semi-structured JSON data stored in Vora, queries can extract nested attributes:
SELECT device_id, sensor_data.temperature, sensor_data.humidity
FROM iot_data
WHERE sensor_data.temperature > 30;
Here, sensor_data is a JSON column, and SQL for Vora allows direct access to nested fields.
Users can interact with SQL for Vora through multiple tools, including:
SQL for Vora bridges the gap between big data processing and enterprise analytics by providing a familiar yet powerful query language. It enables SAP professionals to unlock insights across hybrid data landscapes, combining SAP HANA’s in-memory speed with Hadoop’s scalability. Mastery of SQL for Vora is essential for data engineers and analysts aiming to leverage SAP Vora’s full potential for next-generation analytics.