SAP HANA Vora is a Newly introduced SAP solution for analyzing large data in memory on the Hadoop platform which is running on an in-memory computing engine. SAP HANA Vora is an interactive big data analysis engine from SAP that connects to the Apache Spark and Hadoop system to improve the accessibility and usability of big data from Hadoop. In the final analysis, companies can use data analytics, KPI, Services to improve their results.
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What Is SAP HANA Vora?
SAP HANA Vora is a powerful in-memory computing platform designed to effortlessly interact with Apache Hadoop and Apache Spark and extend the capabilities of the SAP HANA platform by processing enormous volumes of structured and unstructured data in real-time analysis. Businesses may harness the full power of big data analytics by adopting this software, allowing for more precise and timely decision-making.
Introduction for SAP HANA Vora
Data scientists and analysts use data analytics tools and companies use them in their decisions making. Data analysis will assist businesses in better understanding their client’s business & areas of improvement, evaluating their advertising campaigns, personalising content for marketing, developing content strategies, and developing new products. Hence Big Data analytics showcases several insights to grow and to provide an edge over their competitors.
SAP Vora supports a wide range of data types including graph data, relational data and JSON, and time series, A specialized engine manages each type of data with internal data structures and algorithms that can natively support and efficiently process it.
We can load relation information into the main memory and then quickly access the code using query processing. There are various engines that processed the remaining data for subsequent analysis.
- The relational disk engine handles large data sets that cannot be fit into the main memory.
- The time series engine can compress time series data using different compression techniques. It also provides algorithms such as cross-correlation or histogram computation for the compressed data.
- Graph engine allows you to perform common operations on graph data. It is particularly well-suited for complex read-only queries on large graphs. The document store supports rich query processing of JSON data
Before we start a deep dive on HANA Vora, We need to understand the concepts Big data, HADOOP & Apache Spark
What is Big Data
Mobile sensing, air (remote sensing), cameras, microphones, RFID readers and networks of wireless sensors, social media, and archived data. Enterprise data is usually stored in costly hardware, and large data in the less expensive distributed commodity hardware is stored.
What is HADOOP
Distributed computing open-source software. HADOOP does the following when you want to save huge volumes of data in a distributed landscape. HADOOP supports you in creating a distributed environment through the combination of multiple landscape systems. HADOOP assists in distributing data and load processing to various scenarios. HADOOP works only on one layer above Operating System, using Hadoop Distributed File System. Distributed computing (HDFS). H
HADOOP, therefore, handles files for the data. In most cases, when it is stored in an unstructured file format, data can not be processed easily. So to structure data we need some software. We always organize data files using software like MySQL, ORACLE, DB2, and so on in our traditional systems. In the same way, we need some software to structure HDFS files,
HANA VORA helps resolve both problems and bridges the corporate big data gap. Corporate data is data from current business transactions like orders for sale, purchase orders, etc.
What is Apache Spark
In layman’s language, it’s in-memory data processing & its very quick data processing capacities. Its support Multiprogramming languages like Scala, Python, and Java support the Apache Spark and Vora system. The Scala language used in Apache Spark is currently the most common. Vora will expand Apache Spark by providing additional business features and the best possible integration with SAP HANA, enabling cross-consumption reporting and advanced analysis, using live corporate data from an organization.
Spark offers further advanced feasibility for the machine learning algorithms related to Spark Streaming and Machine Learning (MLlib).
Challenges in data analysis.
- Major challenges we have faced as soon as we must have BIG data
- Distributed data is stored in a complicated analysis environment in which the query results are not good every time
- It will be very demanding for the reports requiring the combination of business and big data because of the different landscapes of both data.
What is HANA VORA
HANA Vora uses the in-memory database of HANA that can be processed in real-time and then adds a layer in the analysis to handle Hadoop data. This allows Vora to collect huge amounts of data stored in Hadoop so that developers and data analysts can immediately access the aggregated data and make context-aware decisions.
To handle specific business scenarios for the digital enterprise, SAP developed SAP Vora from SAP HANA. In September 2015, SAP HANA Vora was released on-premises and in the cloud. Hadoop offers less cost storage for vast amounts of data, but acceptance lagged in the company initially because the data in a data lake is unstructured and difficult to handle.
To enable OLAP-style memory analysis of the combined data via the Apache Spark structured query language (SQL) interface, SAP HANA Vora builds structured data hierarchies for the Hadoop datasets and integrates them with HANA data.
Why HANA VORA
For example, by rapidly detecting transaction and Client History Anomalies, a financial institution may reduce risk and fraud by better analyzing network traffic patterns to prevent bottlenecks and improve service quality (QoS), or a financial institution might be allowed to mitigate fraud; By analyzing materials bill (BOM), manufacturing data and sensor data, the manufacture could improve its product recall process.
SAP HANA Vora is a memory query engine that connects to the execution framework of Apache Spark to provide enhanced Hadoop interactive analysis.
SAP Vora Engine Architecture
SAP Vora supported various data types, including relation data, graph data, JSON document collections, and time series. A specialized engine manages each data type with tailored internal data and algorithms to support this data type natively and efficiently.
HANA Vora allows the loading of relational data into main memory for quick access via query processing, and code generation using different compression techniques while providing algorithms such as cross-correlation or histogram on the compressed data. Graphical operations on the data and is especially suitable to handle very large charts for complex read-only analytical queries
SAP Vora can load data from externally distributed stores, such as SAP BW, ERP & Non SAP Sources like IoT, Social Media, log & Remote sensors. Data is either stored in the memory or indexed and stored on hard discs. Allow batch data processing, Analyzing & transformation of complex logic prepare data before query execution & Represent in a visual format
SAP HANA Vora : Use Case Studies
Real-Time Supply Chain Optimization
Improving supply chains is crucial to staying ahead of the competition in today’s highly competitive corporate environment. SAP HANA Vora’s real-time analytics capabilities enable enterprises to monitor and analyze supply chain data in real time. Businesses can make use of this improved data processing power to quickly identify problems, optimize operations, and make informed decisions to improve supply chain efficiency and performance.
Customer Insights and Customization
Recognizing client preferences and behaviours is critical for creating personalised experiences and establishing long-term relationships. SAP HANA Vora enables organizations to analyze massive amounts of client data in real time, yielding insights that drive marketing strategies, personalized recommendations, and overall better customer experiences. These advanced analytics give companies a competitive advantage while also enhancing customer loyalty.
FAQ’s about Terminology use in the articles
What is SAP Leonardo used for
SAP Leonardo allows businesses to automate parts of the analysis process and the business decisions that result to obtain dynamic insight using Intelligent technologies such as machine learning
What is HDFS
HDFS means Hadoop Distributed File System
What is MLlib
MLlib refer to Machine Learning Library (MLlib), Content useful machine learning algorithms for Spark
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