In the recent years, companies face various kinds of challenges like the absence of business alignment, insufficient data processing, and high expenses while managing big data. SAP HANA Vora address these challenges by making decisions precise through drill-down and interactive analysis across Hadoop and corporate data.
The core features of SAP HANA Vora:
- In-memory inquiry engine which functions on Apache Spark framework
- An open development edge
- Compiled queries for faster processing through Hadoop Distributed File System nodes
- Improved Spark SQL semantic include hierarchies to allow drill down and OLAP analysis
- Maintenance for all Hadoop allocations
- Improved mashup application programming module for easy access to enterprise application facts for machine learning assignments
- NUMA awareness
- Support for ORC, Parquet, and HDFS
- Hadoop and HANA mash-ups
It is a different process to implement Vora for customer and developer. It is supported by Apache Spark framework – a popular framework presently on the marketplace for examining multiple data streams. SAP HANA Vora allows customers to employ abstract mechanisms in spite of the velocity and variety of data. It allows the users to compose a function that depends on different data repositories and data streams and obtain the results.
How is SAP HANA different from SAP HANA Vora?
Vora is systemized to work coherently with HANA and offer close to close in memory computing through Hadoop and SAP HANA systems. Both are independent products employed for different kinds of use cases. It does not need another platform to function.
Who will make use of SAP HANA Vora?
SAP HANA Vora remains useful to people in following positions:
Software developers: They can install a query engine inside applications that can extend Hadoop and enterprise systems employing familiar programming tools.
Data scientists: These professionals find patterns by attempting new modeling techniques by combining Hadoop and business data and at the same time without repeating prints within data lakes.
Business Analysts: They can do root cause examination by interactive queries across both Hadoop and business data to understand the business context better.