SAP HANA

Overview

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Where to Starthttp://www.bestsaponlinetraining.com/

Introduction

• SAP HANA is an In Memory Computing (IMC) platform that moves transactional processing from a physical drive into memory, eliminating the Input/Output (IO) performance lag that comes with reading and writing data to a hard drive.

• In 2008, teams from SAP SE, working with Hasso Plattner Institute and Stanford University, demonstrated an application architecture for real-time analytics and aggregation, mentioned as High-Performance Analytic Appliance (or "HAsso's New Architecture" :-)

• Initial GA of SAP HANA in November 2010.

• SAP HANA SPS 11 (December 2015) marks the tenth SAP HANA support package stack of new features, enhancements and innovations released on a half yearly release cycle

Columnar In-Memory Store

• Column-oriented contiguous memory table store with highly efficient compression methods, such as run-length encoding, cluster coding and dictionary coding.

• With dictionary encoding, columns are stored as sequences of bit-coded integers. That means that a check for equality can be executed on the integers; for example, during scans or join operations. This is much faster than comparing, for example, string values.

• Columnar storage, in many cases, eliminates the need for additional index structures. Storing data in columns is functionally similar to having a built-in index for each column

Compression• The compression achieved by the column storage structure of the

denormalized data performs better than taking the time to join the separate tables in the row engine and thus negates the gains of a traditional normalized structure.

• This is especially promising for SAP business applications as they have many columns containing only a few distinct values compared to the number of rows. Examples are country codes or status codes as well as foreign keys.

• In column stores a compression factor of 10 can be achieved compared to traditional row-oriented database systems.

What is SQLScript• The SAP HANA database has its own scripting language named SQLScript. • SQLScript embeds data-intensive application logic into the database by

using side-effect free functions that are parallelizable over multiple processors.– Classical applications tend to offload only very limited functionality into the database

using SQL. This results in extensive copying of data from and to the database.– ABAP or JAVA programs slowly iterate over huge data loops which follow a one tuple

at a time semantics (for example looping over rows in a table) and are hard to optimize and parallelize.

• SQLScript should be used in cases where other modeling constructs of SAP HANA, for example analytic views or attribute views are not sufficient.

Predictive Analytics Library (PAL)• Predictive Analysis is "... the process of discovering meaningful new correlations, patterns

and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. " (Gartner Group)

• The PAL contains a number of universal predictive algorithms (Clustering, Classification, Association, and Time Series functions) that can execute directly against the data in HANA.

• The idea behind PAL is to make it very straightforward to execute with a few lines of SQLScript.

• As a user of SAP HANA Cloud Platform you get immediate access to PAL without any installation and configuration.

• In most cases you need to identify your Input Table, populate a Parameter Table (setting up the parameters of the function), and identify an Output Table, and let HANA and PAL do the rest.

Multitenant Database Containers

• A multiple-container system always has exactly one system database, used for central system administration, and any number of multitenant isolated database containers, also called tenant databases.

• All the databases in a multiple-container system share the same installation of database system software, the same computing resources, and the same system administration. However, each database is self-contained and fully isolated.

• By running multiple tenant databases in one SAP HANA system and managing them as one, you can lower capital expenditure with better utilization of system resources and operating expenditure with simplified database maintenance (e.g. DEV+QAS)

Hot Standby

• Hot Standby functionality helps you regain faster access to data after a data center failure, by enabling applications to fail-over to a fully up-to-date standby database.

• A hot standby system consists of a master database and one or more standby databases that are updated at regular intervals.The primary and the standby databases as kept synchronized by continuously replaying logs.

• Master and standby databases together form a cluster that, outwardly, behaves like a single database. If the master database fails, one of the standby databases automatically becomes the new master database.

• The main advantage of a hot standby system is the short downtime when errors occur (much shorter than the time it would take to restart the master database, for example).

SAP HANA Cloud Connector• Link between on-demand applications in SAP HANA Cloud Platform

and existing on-premise systems

SAP HANA Cloud Connector

• It is used to implement hybrid scenarios, in which cloud applications require point-to-point integration with existing services or applications in the customer network

• You can use the Cloud connector in business critical enterprise scenarios – The tool takes care to automatically re-establish broken connections,

provides audit logging of the inbound traffic and configuration changes, and can be run in a high-availability setup

SAP HANA Cockpit

SAP HANA Cloud Platform Outage

SAP HANA StudioSAP HANA Studio is a Java-based client tool that allows developers and administrators to create models and manage the SAP HANA database

Export SAP Data to Local File

library(RSAP)con <- RSAPConnect(ashost="sapprd", sysnr="05",client="420", user="***", passwd="***", lang="EN",trace="1", lcheck="1")

kna1 <- RSAPReadTable(con, "KNA1", fields=list('KUNNR', 'NAME1'))names(kna1)=c("Customer","Name")write.csv(kna1, '/home/tomislav/sap/customers.csv')

vbak <- RSAPReadTable(con, "VBAK", options=list("ERDAT >= '20150101' ", " AND AUART = 'TA' "), fields=list('VBELN', 'ERDAT','ERNAM','VKORG','VKBUR', 'VKGRP', 'KUNNR', 'NETWR', 'WAERK'))names(vbak)=c("SalesDocNum","Created","CreatedBy","SalesOrg","SalesOffice", "SalesGroup","SoldToParty","NetValue","DocCurrency")write.csv(vbak, '/home/tomislav/sap/vbak.csv')

RSAPClose(con)

A little help from R package RSAP which implements SAP RFC connectivity for R using the SAP NetWeaver RFC SDK (NW RFC SDK).

Import SAP HANA Content Data from Local FileFile Import

Manage Table Definition and Data Mappings

Modifying Tables

ALTER TABLE "VBAK" ALTER („SalesOrg" VARCHAR(10));ALTER TABLE "VBAK" ADD ("DateCreated" DATE);UPDATE "VBAK" SET "DateCreated" = TO_DATE ("Created", 'YYYYMMDD');ALTER TABLE "VBAK" ADD ("Quarter" CHAR(7));UPDATE "VBAK " SET "Quarter" = QUARTER ("DateCreated");

SELECT SCHEMA_NAME FROM "HCP"."HCP_DEV_METADATA"SET SCHEMA "<your NEO_xxx schema>";

SAP HANA Information Modeling

• SAP HANA Information Modeling which also known as SAP HANA Data Modeling is the heart of HANA application development.

• You can create modeling views on top of database tables and implement business logic to create a meaningful report.

• These modeling views can be consumed via Java or HTML based applications or SAP HANA native applications.

• You can also use SAP tools like SAP Lumira or Analysis Office to directly connect to HANA and report modeling views. It is also possible to use 3rd party tools like MS-Excel to connect to HANA and create your report.

SAP HANA Information Views• Modeling SAP HANA Information Views are important for successfully

exploiting the power of SAP HANA. These views are classified as– Attribute Views– Analytic Views– Calculation Views

Framework for Modeling within HANA

SAP HANA Modeling Best Practices

Attribute Views

• Attribute views are dimensions, BW characteristics or master data.

• Generally attribute views represent master data (like Product, Employee, Business Partner)– however technically there is no restriction to make attribute

views on transaction data

• Highly re-used and shared in Analytic (can be linked to fact tables) and Calculation Views

Attribute Views

Time Dimension Attribute View

• Using 'Generate Time Data' option, the two sys tables are loaded with data for the period entered:– For Gregorian type Time Dimension the data is stored in _SYS_BI.M_TIME_DIMENSION. – For Fiscal type Time Dimension data is stored in _SYS_BI.M_FISCAL_CALENDAR.

• When a time type AV is created, it is based on one of these two tables. Here in the view, certain attributes can be measured, like that of BW info-objects 0CALDAY, 0CALMONTH, 0YEAR and this becomes a time dimension that can be used in a Analytical View with a join on the Fact table.

• Time Dimension Attribute Views are very often used while defining the Analytical View.

Analytic Views

• Analytic views are star schemas or fact tables surrounded by dimensions, calculations or restricted measures.

• In the language on SAP BW analytical views can be roughly compared with Info Cubes or Info Sets.

• Analytic views are typically defined on at least one fact table that contains transactional data along with number of tables or attribute views.

• Analytic views leverage the computing power of SAP HANA to calculate aggregate data, e. g., the number of bikes sold per country, or the maximum power consumed per month.

• It is specifically designed to execute star schema querieshttp://www.bestsaponlinetraining.com/

Analytic Views

Analytic Views

SQL ExamplesFind sales groups in which more than 90% of sales orders originate from a single customer:

select av1."SalesGroup",av1."Name",sum(av1."NetValueHRK") from "_SYS_BIC"."s0014459590trial.inst1/AN_SO1" av1 ANALYTIC VIEW group by av1."SalesGroup",av1."Name" having sum(av1."NetValueHRK")>0.9*(select sum(av2."NetValueHRK") from "_SYS_BIC"."s0014459590trial.inst1/AN_SO1" av2 where av2."SalesGroup"=av1."SalesGroup")

Customers with all quartals sales:

select * from ( select av1."Name",av1."Quarter", count(av1."Quarter") over (partition by av1."Name") as "Cnt", count(av1."Quarter") as "Quantity",sum(av1."NetValueHRK") as "NetValueHRK" from "_SYS_BIC"."s0014459590trial.inst1/AN_SO1" av1 group by av1."Name",av1."Quarter" )where "Cnt"=4

Calculation Views• Calculation views are composite views used on top of analytical and

attribute views.

• Executed by Calculation Engine (CE) in the background.

• It can perform complex calculations not possible with other views.

• It can be defined as either graphical views or scripted views depending on how they are created. – Graphical views can be modeled using the graphical modeling features of

the SAP HANA Modeler.

– Scripted views are created as sequences of SQL statements.• When SAP released HANA into the market, SAP recommended using CE functions

to create SQLScript calculation views.• From SAP HANA SP8 SAP recommends using SQL rather than CE functions in

SQLScript calculation views

Calculation Engine(CE) FunctionsDirectly implemented in the Sap HANA CALC engine can significantly accelerate the execution of your SQL query. (http://www.bestsaponlinetraining.com/)

SQLScript Calculation Views

• When SAP released HANA into the market, SAP recommended using CE functions to create SQL script calculation views as they performed better than SQL.

• This is no longer the case. From SAP HANA SP8 SAP recommends using SQL rather than CE functions– The execution of CE functions currently is bound to processing within the calculation

engine and does not allow a possibility to use alternative execution engines, such as L native execution.

– As most Calculation Engine Functions are converted internally and treated as SQL operations, the conversion requires multiple layers of optimizations. This can be avoided by direct SQL use.

SQLScript Calculation Views

SQLScript Calculation Views

SQLScript Calculation Views

SQL Select from Calculation View

SQL from Calculated View ExampleCustomers with all quartals sales:

select "SalesOrg","Name","Quarter","Cnt","Quantity","NetValueHRK"from "_SYS_BIC"."s0014459590trial.inst1/SO3" CALCULATED VIEWwhere "Cnt"=4;

By creating calculated view we simplified end usage SQL statement. Compare with analytic view SQL:

select * from ( select av1."Name",av1."Quarter", count(av1."Quarter") over (partition by av1."Name") as "Cnt", count(av1."Quarter") as "Quantity",sum(av1."NetValueHRK") as "NetValueHRK" from "_SYS_BIC"."s0014459590trial.inst1/AN_SO1" av1 ANALYTIC VIEW group by av1."Name",av1."Quarter" )where "Cnt"=4

SAP HANA General Modeling Principles

SAP HANA General Modeling Principles

SAP HANA General Modeling Principles

SAP HANA General Modeling Principles

SAP recommends using graphical calculation views for modeling any analytic use cases.

– Migrate Analytic Views and Attribute Views to Graphical Calculation Views– Migrate Script-based Calculation Views to Table Functions and Graphical Calculation

Viewshttp://www.bestsaponlinetraining.com/

Security• Select Rights for Activated Modeled Views:

CALL "HCP"."HCP_GRANT_SELECT_ON_ACTIVATED_OBJECTS"

• Define Role(s): /inst1/roles/model_access.hdbrole

role s0014459590trial.inst1.roles::model_access { sql object s0014459590trial.inst1:SO_CV.calculationview : SELECT; sql object s0014459590trial.inst1:SO1.calculationview : SELECT; sql object s0014459590trial.inst1:SO2.calculationview : SELECT; sql object s0014459590trial.inst1:AN_SO1.analyticview : SELECT; sql object s0014459590trial.inst1:AT_CUSTOMER.attributeview : SELECT; application privilege: s0014459590trial.inst1::Execute; }

• Grant/revoke roles to users:call "HCP"."HCP_GRANT_ROLE_TO_USER"

('s0014459590trial.inst1.roles::model_access', 'S0200323456');call "HCP"."HCP_REVOKE_ROLE_FROM_USER"

('s0014459590trial.inst1.roles::model_access', 'S0200323456')

call "HCP"."HCP_GRANT_TO_PUBLIC_ROLE"('s0014459590trial.inst1.roles::model_access')call "HCP"."HCP_REVOKE_FROM_PUBLIC_ROLE"('s0014459590trial.inst1.roles::model_access')

XSJS Database Interface• Simple and easy to use JavaScript interface that accepts and returns JavaScript

variables and JSON objects.• Push all SQL processing into the Index Servervar productId = $.request.parameters.get("ProductId");    productId = typeof productId !== 'undefined' ? productId : 'HT-1000'  var conn = $.hdb.getConnection();  var query = 'SELECT * FROM "SAP_HANA_EPM_NEXT"."sap.hana.democontent.epmNext.data::EPM.Purchase.Item"' +              ' WHERE "PRODUCT.PRODUCTID" = ?';  var rs = conn.executeQuery(query,productId);  var body = '';  for(var i = 0; i < rs.length; i++){     if(rs[i]["GROSSAMOUNT"] >= 500){    body += rs[i]["HEADER.PURCHASEORDERID"] + "\t" + rs[i]["PURCHASEORDERITEM"] + "\t" +    rs[i]["PRODUCT.PRODUCTID"] + "\t" + rs[i]["GROSSAMOUNT"] + "\n";     }  }  $.response.setBody(body);  $.response.contentType = 'application/vnd.ms-excel; charset=utf-16le';  $.response.headers.set('Content-Disposition',    'attachment; filename=Excel.xls');  $.response.status = $.net.http.OK;  

• Most of the processing in an XSJS service was just to convert the Result Set to JSON so it can be passed to the client side.

var connection = $.hdb.getConnection();  var results = connection.executeQuery(    'SELECT * FROM "sap.hana.democontent.epmNext.data::EPM.MasterData.Employees" ' +  'WHERE LOGINNAME <> ?',  'EPM_USER');  $.response.setBody(JSON.stringify(results));  

SAP HANA MDX interface• SAP HANA Analytic and Calculation Views flagged for multidimensional reporting

function as a virtual-modeled cubes and expose your data to be consumed by BI-clients

• Beside the well-known BI Client consumption via SQL interfaces, these SAP HANA Views are also automatically surfaced through the SAP HANA MDX interface and can thus be consumed as seamlessly by MDX-speaking BI clients.

• The Multidimensional Expressions Language (MDX) inherently makes use of Hierarchies for Navigation, Filtering and Aggregation and hence is naturally suited for querying SAP HANA modeled virtual CUBE structures.

• SAP HANA supports the MDX query language since the beginning, its OLAP and MDX capability is built right in and included in the core system. Also, HANA supports the latest MDX query language specification – what we call MDX 2005.

BW

BW 7.5 comes in two flavors: – SAP BW 7.5 powered by SAP HANA

For many customers it has become the entry point for their SAP HANA roadmap.Customers will benefit from phantastic performance improvements for data load,

query runtime and planning functions .

– SAP BW 7.5 edition for SAP HANARun a BW 7.5 on HANA system only with new and optimized SAP HANA objects.If you want to move to this version of BW 7.5 you have to migrate all your old legacy objects that you might have in your current BW on HANA system.

SAP NetWeaver BW powered by SAP HANA

• For organizations that have years of experience and knowledge invested in the SAP NetWeaver BW platform, SAP NetWeaver BW powered by SAP HANA will prove to be the most straightforward and cost-effective SAP HANA-based solution available.

• Organizations will experience very few process or procedure changes with this solution. This is due to the fact that primarily only the underlying relational database that powers SAP NetWeaver BW 7.3 will change.

• However, there are specific optimizations whereby DataStore objects (DSOs) and InfoCubes can be converted to in-memory optimized versions. Under the covers, there are also several optimizations within the code that effectively push down processes that were previously handled at the application layer to the SAP HANA database. The net result is a substantial reduction in database storage requirements and query response times.

SAP HANA as Data Mart• A data mart is an industry term for a repository of data gathered from

operational data originating in transactional systems, designed to serve as a basis for analytics and reporting.

• As new data is added into the relevant tables in the transactional system’s database, copies of those records are automatically transferred immediately into SAP HANA using replication technology. These replicated tables become the basis for specialized views that are created for analytics purposes.

• The SAP Landscape Transformation Replication Server (SLT) is an SAP NetWeaver ABAP-based tool that provides real-time data replication. In addition, a log-based SAP Replication Server (SRS) can also be used to provide real-time data replication for an SAP Business Suite system.

SAP S/4HANA• SAP S/4HANA is shorthand for SAP Business Suite 4 SAP HANA • The next-generation business suite built on SAP HANA platform with the most

modern, role-based user experience (UX): SAP Fiori.

Move to SAP S/4HANA

SAP HANA Live

• Previously known as SHAF – SAP HANA Analytic Foundation

• Provides SAP-delivered content (similar in concept like SAP BW content), in form of SAP HANA calculation views for real-time operational reporting.

• The calculation views spans across majority of ECC modules (FI, CO, MM, PP, SD, PS, CRM, GTS, AM and GRC).

• Currently more than 2000 views are delivered in HANA Live Package.

• With SAP HANA Live all customers have to do is edit existing Virtual models/Views provided by SAP or create new HANA Models or Views to support new development in less time and no need to know ABAP.

SAP HANA Live vs SAP BW (powered by HANA)

SAP HANA Live vs SAP BW (powered by HANA)

A closer look at HANA Live in HANA Studio

SAP HANA Live BrowserQuickly and easily search, browse, tag and consume HANA analytical content views using an internet web browser.

SAP HANA Live Browser

BI• HANA modeling does not create persistent cubes, but rather allows developers to define metadata

that produces CUBE like features. HANA will use this metadata and its calculation engines to retrieve data from the base in-memory columnar tables at lightning fast speeds.

• It is no more needed to replicate data or to compute an OLAP cube, so it helps delivering real-time BI (being able to take decisions a few minutes / seconds after a transaction has been done) directly on the lowest level of granularity, i.e. the transactional line items.

• Single source of truth: Analytical and transactional reporting based on the same source. Reports are now updated in real time because they read the transactional data directly – instead of a copy in a data warehouse.

• SAP HANA has a 6x smaller footprint on disk and 1.5x-2x smaller footprint in-memory and the traditional pre aggregation of transactional data can be completely avoided. As a byproduct backup/restore runs 5 times faster and systems for development and testing are much smaller.

• Even if HANA is not yet fully ready for this function, it is clear in 2012 SAP will propose business functions (like planning, treasure management, inventory management …) that will use HANA to deliver new and high business value.

SAP S/4HANA Finance

• SAP S/4HANA Finance is a comprehensive set of financial management and accounting solutions, covering financial planning and analysis, accounting and financial close, treasury and financial risk management, collaborative finance operations, and enterprise risk and compliance management powered by the SAP HANA in-memory database.

• Operational data in real time: SAP HANA virtual data providers (SAP HANA Live) show real-time data directly from the operational systems and support multiple user interface technologies (SAP BusinessObjects™ Analysis software and SAP Business Explorer (SAP BEx) tools, SAP Crystal Reports® software, and non-SAP business intelligence tools).

• Faster close: SAP HANA accelerates period-end processes such as material ledger costing (faster access to material movements), cost allocations (optimized cost-driver determinations), or top-down distributions (optimized cost-driver determination).

Road Map and Outlook

• SAP intends to use the power of SAP HANA to replace all persistent accounting data aggregates and instead run all reporting from a single set of data on the line-item level – “single source of truth” for financial management.

• Financial and management accounting is harmonized in line with international reporting requirements structuring cost of goods sold (COGS) reporting by account and dimension in profitability analysis (COPA).

• This simplified data model reduces or eliminates reconciliation effort and makes it easier to extend the SAP ERP Financials solution because SAP HANA will generate accounting totals on the fly.

Innovation in Detail

• The key to better planning, budgeting, and forecasting is the embedding of SAP solutions for enterprise performance management in SAP ERP on the level of operational data.

• Virtual information providers (SAP HANA Live) deliver a real-time, redundancy-free, and replication-free view of SAP ERP data to EPM technology integrated in SAP NetWeaver (embedded business warehouse, or BW).

• Planning capabilities that used to be only available in BW are now available on operational data.

• Front-end tools provided by SAP solutions for EPM as well as business intelligence (BI) tools are now available for planning on operational data.

Road Map and Outlook

• The next generation of financials on SAP HANA is expected to deliver an integrated planning model across general ledger, profit centers, cost centers, internal orders, projects, and profitability dimensions.

• It will also cover investment planning, activity planning and product cost planning, and cash and liquidity planning.

• After further optimization of the underlying planning engine, planning algorithms can run completely in SAP HANA. This reduces time-consuming round trips between application server and the SAP HANA database.

• The integrated planning model avoids replication and transformation between subplans. The SAP HANA database enables this radical simplification and acceleration of planning cycles to drive frequent planning iterations on more variants.

• A new “rolling forecast” planning application that continuously compares plans and actuals based on financial exposures. Based on history and variant simulation, we will be able to predict financial KPIs to support business decisions.

Receivables Management

• The main goal of the receivables managers is to keep overdue receivables and DSO low and to minimize credit defaults and bad-debt write-offs.– Receivables managers monitor these activities on a daily basis, typically with a handful of key indicators

such as the total amount of overdue receivables. – They have to rely on yesterday’s data because the key figures are calculated in batch reports or come

from data warehouses. – Aggregation routines limit drill-down capabilities and data investigations. Even worse: confronting

customers with outdated claims always hurts the business relationship.

• Real-time data is key to analyzing customers’ payment behavior and responding to disruptions early and systematically.

• Real-time information also avoids unnecessary activities like dunning a customer who has just paid the bill.

SAP Receivables Manager Dashboard• SAP Receivables Manager visualizes trends and exceptions. • The receivables manager can drill down to customer segments that match the respective

key figures. • Further drill-down leads to the individual customer and ultimately to a single invoice.