Subject: SAP-HANA-Studio
Category: SAP
Predictive analytics is transforming the way businesses anticipate trends, customer behavior, and operational outcomes. SAP HANA, with its in-memory computing capabilities, offers a powerful platform to develop and deploy predictive models directly where data resides. Using SAP HANA Studio, developers and data scientists can build, train, and operationalize predictive models efficiently without moving data out of the HANA environment.
This article explores how to implement predictive models in SAP HANA using SAP HANA Studio, covering key concepts, tools, and practical steps.
SAP HANA’s real-time data processing, combined with its advanced analytics libraries, enables organizations to:
PAL is a collection of pre-built algorithms including classification, regression, clustering, time-series forecasting, and more. It provides stored procedures that can be invoked within SAP HANA Studio using SQLScript.
APL provides a higher-level interface with automated workflows for model training, validation, and deployment. It is especially useful for users who want to implement predictive models without deep coding.
Developers use SQL Console to call PAL and APL functions, execute predictive model training, and manage predictive workflows.
Enables creation of calculation views, which can serve as input data sources or output targets for predictive models.
-- Step 1: Prepare data using calculation views (done in SAP HANA Studio GUI)
-- Step 2 & 3: Train a decision tree model
CALL _SYS_AFL.PAL_DECISION_TREE(
:input_table => 'TRAINING_DATA',
:dependent_variable => 'TARGET_COLUMN',
:independent_variables => 'FEATURE1, FEATURE2, FEATURE3',
:result_table => 'MODEL_RESULT'
);
This stored procedure trains a decision tree on the TRAINING_DATA table and stores the model details in MODEL_RESULT.
Implementing predictive models in SAP HANA using SAP HANA Studio empowers organizations to harness real-time insights from their enterprise data. By utilizing SAP HANA’s predictive libraries, developers and analysts can build scalable, high-performance predictive applications tightly integrated with transactional systems. This approach reduces latency, improves decision-making speed, and drives business value across diverse industries.