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How the SAP HANA Platform Works with Big Data

Learn how the SAP HANA platform can work with big data and find out about the use cases of these technologies for streamlining business processes.

SAP HANA: How It Works

The core of SAP HANA is a database component that can process large amounts of data using in-memory technology and is based on the SQL language tool. The fundamental element of the SAP HANA DBMS (Database Management System) is the relational data model, but it is also possible to access data using the WIPE “graph” query language.

The algorithm of choosing the appropriate query language is flexible due to the architectural capabilities of SAP HANA. It boils down to the use of a single representation of data in the in-memory storage. Therefore, the user can access data using various semantic structures while leveraging a single copy of the data in the DBMS memory.

The classic approach adopted in a number of other open source DBMS environments differs from the above because it implies the use of at least two data repositories and the separation of the storage method of graph structures and relational tables.

You can use the data processing engine to create a new semantic tier for working with information at the data manipulation level. Moreover, a single copy of the source data will be applied, which significantly enhances the capabilities of the SAP HANA platform for addressing tasks where data presentation in the form of graph structures is required.

The in-memory technology in the SAP HANA DBMS allows for storing and processing data in memory using unique algorithms developed by SAP and based on the Intel x86 platform. SAP now supports the IBM Power platform for SAP HANA. The uniqueness and high speed of processing data queries enables you to store and execute them efficiently. They are compressed in RAM.

Owing to the data processing algorithm built into SAP HANA, it became possible to implement the unified table approach, which provides high-speed reading and writing of data to a column storage table. Therefore, one of the main advantages of SAP HANA is its ability to perform analytical queries directly upon transactional data that is added in real time. Meanwhile, the system automatically provides transparent access to data stored using various architectures. Thus, new data in the table is immediately available for analysis without preliminary processing.

Architecturally, SAP HANA supports a configuration where one or more computation nodes are used as part of a single DBMS instance (Scale-out, see here). This configuration is particularly relevant for processing large data arrays in real time. The processing of an SQL query in SAP HANA occurs simultaneously over the entire volume of data, regardless of its location.

Unlike Hadoop Spark and Hadoop Hive, the SAP HANA platform delivers a faster and simpler mechanism for loading data and executing queries for a large amount of structured data using the SQL language.

When processing large arrays of unstructured data (for example, video or photographic materials), it is recommended to utilize a combo of SAP HANA and Hadoop Spark via the HANA Vora tool. This is a compact version of the in-memory DBMS integrated into Hadoop Spark.

The SAP HANA platform documentation also suggests using different options when choosing a programming language to create applications within the framework of the new “bring your own language” concept. The built-in SAP HANA XS advanced application server enables you to create independent application containers based on JavaScript (Google V8 and Node.JS engines), Java (Tomcat Java), Python, Ruby, and C ++.

One of the noteworthy use cases of this platform is in the area of machine learning for image recognition and classification tasks based on an image database using Hadoop. Another is streaming data using the SAP HANA Smart Data Streaming component.

When implementing video algorithms in SAP HANA, it’s possible to use the popular Caffe, Theano, Torch, and Tensorflow packages and transfer previously developed applications without making changes to containers based on the HANA XS Advanced or Hadoop Spark environment.

HANA is often considered as just one more database by the IT team who as a result ignore new use cases and modern cyber risks. To be able to successfully use SAP HANA, all staff members should be informed about organizational transformations and associated dangers. SAP has created a special security guide which focuses on HANA security topics.

Examples of SAP HANA Use Cases for Processing Big Data in Moving Object Control Systems:

Digital Warehouse Based on SAP HANA

An important task for large distribution companies is to manage the loading and unloading of goods, as well as order routing and preparation for shipment. You can quickly plan and adjust the process of preparing goods for shipment and avoid problems with idle time of goods in stock as long as you are able to accurately track goods and trucks, as well as monitor and control the loading and unloading workflow.

The “digital warehouse” model based on SAP HANA, combined with a component for Smart Data Streaming, helps collect location details and information on the availability of instruments for loading and unloading goods, while also facilitating personnel management through timely plan adjustments. The use of appropriate sensors enables you to collect information about the condition of the shipping tape and the workplaces of your staff, and helps you monitor the status of locations for loading and unloading the goods.

In ordinary warehouses, errors may occur during the order picking process due to human factors. To minimize this, the “digital warehouse” uses built-in capabilities of SAP HANA to identify special tags coming in the form of QR codes. The labels enable you to automatically determine order picking details and keep track of product items based on the order code and information about it from SAP ERP.

By using SAP HANA and its ability to analyze information in real time, companies can build a real-time warehouse management system that will take into account the changes made to plans when processing goods and placing orders. This will reduce product downtime and ensure optimization of the staff management routine.

Additionally, you can leverage SAP HANA in conjunction with predictive analytics tools to build data analysis based on statistics reflecting the work performed to streamline the warehouse operation process.

Digital Car Parking

One of the important tasks in managing urban traffic is to keep track of available parking spaces for monitoring the load of the urban parking ecosystem. Specialized sensors installed in parking lots can track the number of vacant and occupied spots. The monitoring system based on SAP HANA Smart Data Streaming enables you to monitor the status of these sensors in real time and manage the map of parking spots.

Furthermore, the use of DVRs can help monitor compliance with the terms of paid parking by collecting information about vehicle number plates and monitoring the status of particular parking instances.

Digital System for Product Delivery Quality Control

It is extremely important for large urban delivery networks to manage and track the delivery of goods in an efficient way. Under the circumstances of limited delivery time and a bevy of orders being handled in large cities, it is critical to ensure timely response to changes made to orders and plan the delivery of goods, while keeping in mind that customers’ requirements may change.

The integration of the SAP HANA Smart Data Streaming system helps process several million product delivery requests per minute and then adjust the delivery schedules in real time using specially crafted tools.

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