Administration and monitoring
Simplify enterprise-wide database administration and monitoring with new configuration options when copying, downloading, or importing data, and database grouping and filtering options in the SAP HANA database explorer.
Capture and replay
Be more efficient and effective to detect, analyze, or verify potential issues before applying changes or upgrades by jump-starting to a certain replay point-in-time and running a real-time analysis for replay blocking situations.
High availability and disaster recovery
Optimize multi-target replication with automatic re-registering of secondary sites and avoid instance restarts if log positions allow. Benefit from consolidated status info for the cluster manager in case of multiple failures in a cluster.
Backup and recovery
- Increase efficiency and reduce TCO of backup and recovery with full backup compression, including data, log, and backup catalog backups, and backup and recovery of configuration parameters.
- Increase backup continuity through failover of BACKINT-based log backups into a staging area when a third-party backup tool is unavailable.
- Schedule back-ups via a system-wide scheduler across multiple tenants in a multi-tenant database container (MDC) configuration and continue automatically after a disk full situation.
- Optimize your data volumes through a permanent incremental data volume reclaim housekeeping job for SAP HANA internal snapshots, safeguarding that data volumes will not exceed predefined thresholds.
- Use the full bandwidth of your underlying infrastructure through configurable file IO management, safeguarding optimized throughput, and balanced IO requests.
- Speed up SAP HANA crash and shutdown behavior by heavily parallelizing its execution and freeing memory before entering the kernel.
- Enforce enterprise-wide SAP authorizations and advanced protection of sensitive data from power users and database administrators and reduce TCO with automatic setup of transport layer security/secure socket layer (TLS/SSL) certificates for new SAP HANA database installations.
- Benefit from simplified handling of user privileges via multiple user mappings for security assertion markup layer (SAML) and JSON Web Token (JWT) authentications.
- Ensure data privacy by applying simplified and enhanced data masking semantics.
- Add public keys to certificate collections where JWT is managed via public/private key pairs.
Native storage extension
- Use the SAP HANA native storage extension advisor more simply with a built-in procedure, obtain statistics on advisor runs and compare them with historical runs, prioritize recommendations by confidence levels, and exclude database objects from recommendations using filter rules.
- Automate the data load balancing of your scale-out environment including SAP BW/4HANA deployments via table redistribution, now also supporting SAP HANA native storage extension tables and partitions.
- Reduce operating costs and optimize your in-memory footprint by configuring statistics server tables to move rarely used monitoring data into SAP HANA native storage extension.
Performance optimization and partitioning
- Gain higher flexibility and visibility for your workloads and enable intuitive workload management by setting up the hierarchical workload classes with integrated admission control, and monitor workload class statistics and admission control statistics in real-time.
- Increase flexibility and reduce efforts when using table partitioning by running partitioning operations, such as moving partitions or heterogeneous repartitioning, in an online mode.
- Change the datatype of partition columns without data move, and truncate partitions based on partition ID.
- Increase transparency through analyzing the history of partitioning operations and selection statistics.
Smart data access
- Benefit from increased flexibility and reduced efforts when using SAP HANA remote sources through the support for newer versions of Microsoft SQL Server and Oracle databases, ODBC drivers for SAP IQ and SAP Adaptive Server Enterprise, Cloudera, Hortonworks, and Cloudera Data Platform, Private Cloud Base.
- Seamlessly and dynamically extend on-premises SAP HANA system data and objects via real-time log-based replication to SAP HANA Cloud in a hybrid scenario without a database migration.
Smart data integration
- Benefit from performance, connectivity, security, and integration improvements and stay current with industry standards with various adapter enhancements and certifications.
- Leverage log transferring language (LTL) technology for changing data capture on an SAP Adaptive Server Enterprise (ASE) source with ASE and ASE ECC JAVA adapters.
- Connect to Oracle 19c databases with the new trigger-based adapter, replacing old log-based replication.
- Optimize end-to-end performance measurement through extended monitoring capabilities, such as latency ticket management, measuring throughput, volume analysis, history configuration, and volume of records applied by remote source.
- Increase the performance of queries and reduce resource consumption with enhanced features for calculation view modeling. Generate different types of modeled SQL hierarchy views, remove technical hierarchies for multidimensional expression (MDX) queries to speed-up deployment, indicate the role of a calculation view by setting the flag “end-user view”, and use expressions when defining non-equi joins.
- Improve efficiency with SQL warnings that are now displayed in the SQL Console, a new layout in the SAP HANA database explorer, and an additional database wizard for the SAP HANA cockpit and database objects in the SAP HANA database infrastructure (HDI) service layer.
Python / R Client
- Increase productivity when working with Python and R machine learning clients through support for multi-statement SQL, spatial and graph functions, the DocStore file format, as well as index, sort, and join/unjoin method support for the DataFrame object.
- PAL procedures come with productivity improvements and continuous updates on feature coverage and visualization extensions.
Predictive analytics and machine learning
Create and embed intelligence in the next generation of advanced applications and address new real-time machine-learning use cases through enhancements in unified regression, classification, forecasting, spectral clustering, time series analysis including Bayesian (probabilistic, BCPD, BSTS), intermittent, volatile (GARCH), multivariate (Vector ARIMA), and Additive Model, as well as Unified Exponential Smoothing incl. massive parallel forecasting, model interpretability, and explainability enhancements.
Benefit from enhanced flexibility through the option to define spatial columns in tables without specifying a Spatial Reference Identifier (SRID), the support of the Esri JSON data format, and a generator for rectangular grids.