HA100 SPS09 PDF
HA - SAP HANA – Introduction - Download as PDF File .pdf), Text File .txt) or read online. Get an overview of SAP HANA SPS09 and in-memory computing. pdf. HA SAP HANA Introduction.. PARTICIPANT HANDBOOK .. Customers running SAP HANA SPS09 and lower must first upgrade to SAP HANA myavr.info SAP HANA myavr.info Note: Up to and including SAP HANA SPS09 the maximum number of partitions was 1, per column table.
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Ha Sap Hana Introduction - [PDF] [EPUB] Ha Sap Hana Introduction SAP HANA SPS Content Key concepts of SAP HANA Working. platform, which is best suited for performing real-time ha - sap hana – introduction - sap hana sps09 content key concepts of sap hana working with sap hana. sap hana an introduction pdf - studderpromotional - ha - sap hana – introduction sap hana sps09 content key concepts of sap hana working with sap hana.
View online or download Teka HA- Manual. These are SAP certification books. Please suggest where to get it.
Could you please let me know what the price of this pdf. Acrobat Reader is required. Stability and biocompatibility of hydroxyapatite — titania nanocomposites formed by two step. Download PDF Download. A catalog download is here.
X or later format. Download sap hana 2. Course based on software release. In that case they are called stateless or side-effect free , as they don't alter any data in the database. However, procedures can also be used to update, insert, and delete data. These procedures are called stateful, and they are not allowed when called from calculation views.
Stateful procedures are more likely to be used by developers building applications. Security Considerations in Modeling Figure Security Considerations in Modeling Unless you grant access to users, they will not be able to view any data from your calculation views. There are two levels of security, as follows: The first level of access required is to the actual database objects. In this case we mean the calculation view and the source tables and functions that are included in the calculation view.
This privilege is granted to the user, or more likely, to the role the user is assigned to. They then need to have access at the row business data level. This is achieved by defining an analytic privilege and assigning it to the user, or the role to which the user is assigned. It describes for each calculation view the conditions for data access.
The conditions can be simple such as User 1 can see company A. The conditions can also be more complex such as User 1 can see company A and B but only between — A user can have multiple analytic privileges. Analytic privilege logic can also be written using SQLScript in procedures. This means that report developers and application builders can easily consume live business information without being concerned about the underlying table complexities.
They are compiled into column views that are technically no different from the column views created from customer calculation views. They expose the data at the most granular level. However, the reporting tools are able to aggregate the data as required.
It is not a licensed product. This makes setting up replication easier. Alternate Deployment Scenarios The alternative is to enter all tables manually. Note that with a side car deployment, the data exposed by the SAP HANA Live view is only as up-to-date as the last replication of data from the source tables.
No data movement takes place, so there is zero redundancy. This is true whether they are created by SAP, partners, or customers. This means that you are able to provide popup filters to the users, to make personal data selections. As well as the technical definition of the data model and consumption model, CDS allows you to fully describe the business semantics right in the model.
The semantics can be consumed by various applications and provide additional information about the data. For example, you can describe the correct way to aggregate a specific measure, or to identify certain fields as belonging to a customer address.
With CDS you not only expose tables for analytic purposes, but you can also use CDS in many other use cases, including search applications and planning applications.
However, the technical implementation is different, and the syntax of the language is different also. It is used to define the data model used only by ABAP applications. This provides great flexibility, which means no more locking in data modeling logic in the application, and no more locking the data modeling logic in the database.
Missed Opportunities with Information Silos Many organizations already rely on spatial data processing and use specialist applications alongside, but separate from, their business process applications. For example, a gas storage tank is leaking and an emergency repair order is raised in the SAP ERP system by a coordinator.
Next, the coordinator uses a separate geo-based application and manually enters the asset number of the tank.
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The tank shows up on a map and the coordinator then uses another application to look up the nearest engineer, who is then dispatched. Missed Opportunities with Information Silos It would be more efficient if, at the time of generating the repair order, the ERP application was able to locate the tank.
Analytical Processing with SAP HANA who has enough working hours left to complete the repair, and provide useful geographic information to the engineer to describe how to best reach the tank. The application could then provide information about other equipment in the close vicinity that is due an inspection soon. This prevents having to make separate visits.
This scenario could be possible if the core business processes were integrated with spatial data and analysis. Beyond business applications, there are exciting use cases for spatial analysis in the sports environment.
SAP have developed a series of applications that provide deep analysis of player performance. For example, in golf, by adding a sensor to the ball and pin, we can create a graphical history to illustrate the improvements in accuracy of the shot. These types of applications are already in use by major sports organizations around the world.
There are many applications that could be dramatically enhanced with the integration of spatial data.
Be careful to use the correct term spatial and not geographic or geo. Spatial processing in SAP HANA is not limited to just geographical scenarios, but can handle anything regarding the three dimensions of space. Spatial data is data that describes the position, shape, and orientation of objects in a defined space. Spatial data is represented as geometries in the form of points, line strings, and polygons.
Take for example, a map of a state, representing the union of polygons representing zip code regions. This allows you to store information such as geographic locations, routing information, and shape data.
You can then use methods and constructors to access and manipulate the spatial data. Customers can now combine satellite images, and extract metadata from the images, and use them in their spatial applications.
Already, customers are developing new applications to analyze and combine data relating to environmental conditions. Calculation View: Spatial Join Operations Calculation views now support spatial operations. These operations could include calculating the distance between geometries and determining the union or intersection of multiple objects.
These calculations are performed using predicates such as intersects, contains, and crosses. Spatial processing is covered in more detail in the course, HA This can occur in different areas, inside or outside your company. You may then need to extract meaningful information from your unstructured data, to be able to analyze it properly. You can search through huge amounts of unstructured data quickly and identify key entities such as company names, product names, and sentiments of customers.
You may also have additional search requirements, like ranking your search results or bringing back a snippet of text that surrounds the found word, to put it into context.
This function offers the possibility of searching for approximate matches to a given criterion. For example you could search for the string Walldorf and find all exact and approximate matches to a degree that you can specify in your search.
These matches could include results like Walldorf , Wadlorf , Valldorf , and Wahldorff. In general, a Fuzzy Search will search for all criterion matching the given one, either exactly, or differing by missing, added, or mistyped characters. The behavior is similar to that of a web search engine; that is, you do not want to see only exact matches of your search criterion. An example of this is linguistic markup, which entails identifying the various parts of a speech verbs, nouns, adjectives, and so on.
It also allows you to identify entities locations, persons, and dates in an unstructured text. This stores known words and expressions that customers use to express their feelings about products and services. It can be used in sentiment analysis to help organizations quickly respond to customer feedback on social media and other places where textual feedback is found.
Customers can also create their own custom dictionaries to include words and expressions used in their organizations. They process both historical and current data, using powerful algorithms to generate probable outcomes. Traditionally, predictive analysis was not possible for most organizations due the following reasons: SAP HANA plays a key role in Data Science, providing the platform for in-memory data management and processing, especially in the provision of sophisticated algorithms.
It also provides the ability to easily reach and consume Big Data. Internet of Things IoT scenarios often require embedded analytical processes to provide automated, real-time decision making and machine learning.
There are many use cases for predictive analysis. It is also possible to import more algorithms from the huge, public R library and to develop your own custom algorithms in the R language. R is a statistical scripting language, which is heavily used in academic and scientific institutions. It is becoming more widely used, especially in the commercial sector, and data scientists are often fluent in this language.
The list of built-in algorithms is growing all the time. There are algorithms for all types of analysis and they can be grouped into families. Some of these families are as follows: This is a graphical definition of the data sources, data preparation steps, the algorithms, and the output.
Explaining Predictive Modeling Figure Create a Predictive Model The flowgraph generates an object that can be scheduled, triggered by an event, or embedded in a calculation view.
Examples of use cases include logistics and transportation, utility networks, and social networks. The basic idea behind graph modeling is that it allows a modeler to define a series of entities nodes and link them in a network.
This network represents how they relate to each other. Graph models can indicate flow direction between entities. This means that additional meaning can be added to the network and traces can be made. Imagine a complex supply chain mapped using a graph, where all manufacturers, suppliers, distributors, customers, and consumers are represented with information stored along the connections.
The benefit to this form of modeling is that it makes it easy to develop applications that can traverse huge graphs at speed. As a result you can ask questions such as the following: Graph processing allows you to discover hidden patterns and relationships in your data, and all in real time. Describing Graph Processing Graph Model: Example Figure Graph Model: Example The graph model in the figure, Graph Model: Example , is one that most people can relate to.
However, there are many other interesting examples, including the following: It is possible to use standard SQL data definitions and query syntax to create and process a similar model.
However, it would be extremely complex in the definition of the model and the SQL needed for the querying of the graph, plus processing times could be challenging. SAP HANA Graph provides tools for graph definition and language for graph processing, to ensure that model development is more natural and simplified. It also guarantees that the processing is flexible, and of course, optimized for in-memory processing.
Vertices are stored in tables and represent the members of the graph. These are the nodes. The edges are also stored in tables and describe the lines between the members. Along each line, you can store connection information such as distance, preference, strength, and so on. You then create a Graph Workspace that refers to the vertices and edge tables. The Graph Workspace simply creates the metadata that defines the graph model and how the vertices and edge tables relate.
No actual business data is stored in a Graph Workspace. It is a declarative language, similar to SQL. X A To push data-intensive processing up to the application to obtain the best performance X B To develop reuseable data processing logic in the database X C To simplify applications 2.
What are valid types of calculation view? What do we implement to restrict access to specific data rows of a calculation view? Learning Assessment 5. Learning Assessment 9. What must I create to develop a predictive model?
X A To push data-intensive processing up to the application to obtain the best performance X B To develop reuseable data processing logic in the database X C To simplify applications You are correct! We push data intensive processing down to the database to obtain the best performance. Yes, core modeling in HANA encourages reuse of processing logic.
Yes, we simplify applications when we don't have to worry about developing code to carry out data processing tasks. Dimension with star schema is the only type that is not valid.
HA240 SAP HANA 2.0 SPS02
Functions can only produce one tabular output whereas Procedures can produce multiple tabular outputs. SQL permissions only secure the database object, not data values. Analytic Privilege is used to define security around rows data.
A State, being an area, would best represented with a polygon spatial type. These can be used to describe any type of shape. Points are great for identifying entities that do not occupy visual space, such as customer or hotel. Lines are great for representing something continuous that has a start and end such as a road or river.
X A To extract the sentiment from customer feedback on social media X B To extract common entities such as company, country, currencies, and so on, found in documents X C To identify close matches in words and expressions, to catch misspellings You are correct! Refer to HA, Unit 3 Lesson 5 for more details.
Graph Processing is not related to charts and dashboards, but is used to model and process data that is best described using a network. Examples would be supply chains or social networks where many entities are highly connected. This means that you can decide whether you need to actually physically capture and store a copy of the data in HANA, or if you just need to access remote data.
You need to decide which data acquisition tools are required. There are many to choose from. You also need to decide where the data will reside. There are multiple options for data location in HANA. You must pay special attention to the architecture of your data management solution in SAP HANA and design this well for optimum performance at the lowest cost, especially for high volume data scenarios.
In this case, no additional data acquisition tools are needed. However, it is important to understand what happens when SAP HANA is deployed as a standalone database, for example, as a data mart with analytics running on top. Data acquisition is not just another term for data loading.
While this is possible, data acquisition also includes other approaches, such as data streaming and data virtualization. Describing Data Management Figure Types of Data Acquisition When you consider data acquisition, think about the following key aspects: Data can arrive at different time frequencies, ranging from real-time and hourly, to weekly, or yearly.
It could also be driven by occasional events, like when a vending machine runs out of stock and transmits a request for a refill. Transformation is represented on the vertical axis of the figure, Types of Data Acquisition. During provisioning, data can be transformed. This transformation could be done to align billing codes from different systems, convert currencies, look up missing zip codes, or calculate rebate values.
Sometimes data from different sources must be loaded at the same time, for technical or business integration logic to be applied. An example of this is when data needs to be harmonized into a single stream from different sales order entry systems.
This enables you to meet the needs of all applications that require data at any speed and of any type. These applications consume data at different rates from continuous real- time sensor data, to periodic batch loads of bulk data. You can decide, based on the importance of the data and performance expectations, exactly where data should reside, in or even outside HANA.
You can choose between memory, disk, or external data archives. You can decide to implement Extended Storage or perhaps to implement multistore tables that cross tiers.
You can classify your data by temperatures and assign the various temperatures to physical storage options in HANA. Even though there is now vast amounts of memory available, it does not make sense to store old, rarely accessed data in memory. Data has a lifecycle and older data should be moved away from memory hot , down to disk warm , and eventually to archive cold. The main reason for this is to ensure that you have the best possible performance for the data that is most used.
You do not want to clog memory with huge amounts of old data that never gets used. Data Temperatures Figure This is an important calculation that can ensure that you get the right performance at the right price. You implement cheaper tiers such as disk and archives for the older data. You also need to think about SLAs; that is, the performance requirement for different types of data. There is so much data acquired today that you need the think carefully about where to store this data.
The storage for this is HANA memory. The storage for this is disk, either via Extended Storage or inactive data data that has been displaced from memory and pushed to disk. It is possible to classify and move data across the storage tiers automatically. Usually, it is the job of the application to control this based on usage patterns. For example in BW, you classify PSA as disk warm store, as this data is only needed periodically and should not occupy memory hot.
It is also possible to control the movement of data across tiers under the control of HANA. For example, prior to a job running you can execute a command to load data to memory from disk. Then, once the job has ended, unload the data back to disk to clear out memory. It is used to support the development of a data management solution.
With this tool, you are able to develop a logical database and data management model and then turn it into a physical model. You can then share this with your teams to consider the impact, and collaborate to improve the design. Once the design is agreed, the tool can automatically generate the physical objects in SAP HANA, saving you the manual effort of doing this. The lesson will help the learner to understand the various scenarios for data provisioning.
Customers require a cost-efficient, easy-to-deploy solution, to get real-time data visibility across their fragmented data sources. Examples of these include operational reporting, monitoring, predictive analysis, and transactional applications.
Smart Data Access SDA is the name of the built-in tool set that provides an extensive catalog of adaptors to connect to remote sources.
SDA can figure out on-the-fly whether a query should be pushed down to the remote data source for processing, or whether the raw data should be fetched, and the query then runs in SAP HANA. SDA always uses the approach that offers the best performance. Once the one-time connection to the remote source is established by IT, the application developers do not need to concern themselves with the technicalities of where the data is coming from.
SDA supports a modern data-federation strategy, where movement of data is minimized, and global access is enabled to data stored in local repositories. SDA can be utilized in the following situations: The Glue of the Platform You can now create a fast and flexible data warehouse without expensive ETL or massive storage, security, and privacy risks.
You can build big data applications with fast and secure query access to data, while minimizing unnecessary data transfers and data redundancy. You can bring social media data and critical enterprise information together, giving comprehensive insight into customer behavior and sentiment.
Although we are focusing here on the use of SDA to read data from remote sources, it should also be noted that SDA is able to write data to the remote source also. A virtual table can easily be spotted in the database catalog as it has a small connector symbol added to the table icon.
It does this by determining if any query operations could be faster if they were pushed down to the remote source rather than processing in SAP HANA. The following are some of the benefits of SDA: Virtual tables also cache their results so that identical queries do not have to fetch the same data again. For the up-to-date list of adaptors, check SAP Note Data Replication Data replication typically means ensuring that data created in one system is duplicated to one or more target systems.
It is usually done in real time and is managed record by record. However, replication does not always happen in real time. Replication can also take place periodically, for example, every five minutes.
Periodic replication is used when it is not essential that data is always synchronized in real time. Typically, with replication, no transformation takes place on the data as it travels to the target system so that we have an identical copy of the data in all systems.
Replication involves the physical moving and loading of data, and not simply exposing the data sources as virtual tables. The following are examples, which illustrate data replication: This means real-time dashboards can be developed to show the live sales pool. This can trigger replenishments when stocks are low. There are many different technical implementation approaches that support replication, ranging from the use of database logs to the use of database triggers.
It is essential that the source or target system has some way of knowing that data has changed, so that replication can be kicked off. For example, live dashboards can be kept up-to-date with real time transaction data. SLT has been used for many years as a data transfer tool in landscape transformation scenarios company acquisitions where data needs to be consolidated, or split.
Many of these enhancements help to improve the throughput of data as well as the monitoring of the data movement. SLT is a trigger-based data provisioning tool. This means that SLT sets database triggers on any table that you want to replicate from. When the database table is changed in any way insert, update, or delete , the trigger is automatically fired.
SLT can perform the following types of data movement: This is not replication, but a bulk copy. This tool is also used for data migration, which is typically a one-time event, so this feature is very important. Describing Data Acquisition Tools SLT performs an initial full load of all data, and then immediately sets up a real-time replication of data from the source to the target system.
This replication is trigger-based, meaning that a DB trigger is defined in the source database on each table marked for replication. Each time a data modification is done to a source table, the trigger captures this event and SLT transports this change to the target database. Some data provisioning tools are able to replicate from the application level using business views for example, BW data sources. This means that you need to know the names of the source tables you wish to replicate from.
When we think about replication we usually assume that data is moved unchanged from the source to the target. However, in some cases, you may need to apply some transformation to the data. Although SLT is not a heavyweight data transformation tool, it is certainly possible to modify the data during the transfer process.
The types of possible modifications are as follows: The filters can be set on multiple columns. ABAP is used to develop the transformation logic. So, this is a crucial skill to have on any SLT project where transformations will be made. Any transformation applied to data as it is being replicated will have an effect on the time it takes for the data to arrive at the target. For this reason, only light transformations should be implemented. Writing data transfer rules for complex integration and cleaning can get complicated.
There are better SAP data provisioning tools to use in those situations. Here, you can choose from a number of options to stop and start data movement jobs. This is done without geographical distance limitation to meet demanding requirements in the enterprise such as guaranteed data delivery, real-time business intelligence, and zero operational downtime.
SAP Replication Server facilitates this by non-intrusively handling data at the source and target database level, while ensuring high performance and transactional integrity. You will find this solution used in many financial institutions where systems must be completely in step, in real time, with robust recovery options in case of failure.
The Changed Data Capture CDC is not done against the data volumes of the source database tables, but instead by reading directly from the database log. A database log is a history of all actions executed by the database management system.
A log is often used in the recovery of databases after a crash. When replayed, all updates to the database can be re-created. This log-based approach reduces the workload that the replication process usually brings to the source database, thus enhancing the availability of this system.
Example of this could include a field engineer in a remote location with a poor signal, or perhaps if the application should not be continually connected due to connection costs. These applications sync with the central database, either periodically at set times or triggered by an event. In all remote data sync applications, the remote data sync server is the key to the synchronization process.
During synchronization, the remote data sync client at the remote site can upload database changes that were made to the remote database since the previous synchronization. On receiving this data, the remote data sync server updates the consolidated database, and then downloads changes from the consolidated database to the remote database. It remembers the exact sequence of updates from all remote clients. Imagine for example if 1, field engineers were withdrawing the same spare part, while at the same time other remote works were replenishing the same spare part.
It could be easy for the stock balances to get messed up in fast, bidirectional data traffic. This process is popular with data warehouses, such as SAP BW where there are many data sources that need merging.
Most of these options require the installation and setup of additional software and hardware components, which sit between the data source and SAP HANA. These components cover a broad range of capabilities, including extracting data, combining sources, and cleansing, loading, or exposing the data to SAP HANA.
Describing Data Acquisition Tools The inclusion of an additional data provisioning tier adds to the complexity of the overall solution. With EIM, we have removed the external data provisioning tier. Running data provisioning tasks inside SAP HANA also means that we take advantage of the high performance, in- memory processing for data acquisition tasks. Components of EIM Figure When building any data provisioning job, the developer is able to freely include any of the capabilities from either component.
SDI is the key component that takes care of data acquisition and integration, whereas SDQ can add additional steps to the job to enhance and clean the data. Flowgraphs are graphical representations of a data provisioning job. They contain a sequence of nodes and flow lines that represent the steps in the flow. Developers create jobs by dragging and dropping the nodes to a canvas to create the flowgraph.
SAP Data Services has been around for many years and is deeply embedded in the distributed landscapes of many customers. It provides very sophisticated data integration and harmonization features, as well as country-specific data cleansing tools.
SAP Data Services excels at managing complex delta loading to data warehouses with auto-recovery mechanisms built-in to restart jobs if they fail. SAP Data Services can also quarantine data that does not pass quality checks for more intensive processing.
This tool provides extensive data quality and data profiling dashboards so business users can monitor and measure data quality.
The tool also exposes the data cleansing rules to business users who can create and adjust cleaning rules without the need for IT involvement. Enterprise-wide data lineage is also a capability of Information Steward so that the origins of data can be traced from reports.
However, for now SAP Data Services remains a good choice for many customers who need a fully loaded ETL solution with sophisticated features for a complex data landscape. Data Streaming Data streaming is the transfer and processing of continuous data from a source to a target. Data streaming often involves high-speed, high-volume data transfers from multiple streams in parallel.
Sources of streaming data can range from simple sensors to complex business systems. The opportunities for the development of innovate applications are enormous.
Individual events may not be significant by themselves, which makes it difficult to discern when consequential events do occur. You could have thousands of sensors reporting statuses every few seconds, and most of that information is uninteresting. However, when something is starting to go wrong, you want to know as soon as possible, so that you can act before a small trend becomes a significant problem.
Describing Data Acquisition Tools Figure This enables you to react in real time using alerts, notifications, and immediate responses to continually-changing conditions. Often a single stream offers little useful information but when we combine multiple streams triggered by different events, we can begin to develop real insight. The wizard supports either csv files or Excel workbooks. Process Flow: The process is as follows: Choose the flat file to import.
Confirm or adjust the suggested field mapping and column types. Preview the output and if you are happy, execute the load. It is possible to edit these suggestions. All records are loaded. Existing records are never overwritten — so make sure the new records don't create key violations. Big Data Integration Many organizations have embraced Big Data, collecting and storing staggering amounts of data of all types sourced from sensors, web logs, social media traffic, communication logs, and more.
Unlike traditional business data, Big Data is usually stored in an unstructured way and so it is difficult for us to define detailed semantics on this data so that it can easily be integrated with existing analytics.
Organizations usually have to implement additional specialized tooling on top of their Big Data in order to add a semantic layer to add meaning and structure to the data and also provide query capabilities. These tools can be complex and are often used by only a small number of highly trained analysts. This often means that Big Data analysis becomes siloed from mainstream BI. We accept that Big Data and enterprise data are typically stored separately.
However, the analysis should not be separated, and business analysts should be able to consume any data and not be concerned over whether the data is Big or not.
Big Data and enterprise data are separate SAP Voraenables analysts to consume Big Data and enterprise data as one, using their favorite drill-down, slice and dice, query tools. First, a Big Data framework consists of a data storage component. Hadoop provides massive data storage capabilities across cheap, everyday servers that can easily be scaled to provide staggering storage capacity.
However, Hadoop does not provide the data processing capabilities and that is where Apache Spark comes in. It is also not able to integrate enterprise data and Big Data. This is where SAP Vora comes in. SAP Vora also allows you to create precompiled queries that are ready to go, to enable fast execution. This provides a better understanding of the business context and helps to provide complete insight in one work flow. Altiscale was one of the first companies to offer Hadoop as-a-service in the cloud, and was acquired by SAP in Cell phone service improvement — Analyze instances of poor cellular service, such as dropped calls or poor audio, by drilling from billing data to detailed call log data.
Describing Data Acquisition Tools 3. Fraud detection— Detect anomalies and rogue trades by analyzing historical trends and current data simultaneously. Airline maintenance planning — Combine aircraft sensor data, collected in-flight, with flight schedule and staffing data to optimally plan aircraft downtime. Targeted network maintenance and upgrades — Analyze the impact of cable network congestion on churn, and identify which network upgrades will produce greater incremental revenue.
This does not encourage good performance. X True X False 2. What are features of SDA? X A The new table always has a 1: X B The supported file types for upload are. X C When loading new data in a table that already contains data, the new data is appended to the existing data.
SAP Enterprise Architecture Designer is a tool used to design and build a logical data model, and then, optionally, generate a corresponding physical data model: SDA does not support cleansing or merging of multiple data sources.
You cannot transform data during the load, but you can ignore fields and even add empty ones. Data is never overwritten. Renaming columns and changing data types is not allowed when loading to existing tables. The key services include not only database services, but also application processing services and data management services. Examples Figure This flagship application covers all core business processes and applications for lines of business LoBs.
Take for example, a huge table where only the recent data in the table needs to support a strict SLA. The older data does not have to meet the SLA. They would either all be in memory or all be on disk in extended storage. Now you have the best of both worlds. This keeps the two HANAs in sync. The reason for doing this is to provide a hot, continuous backup to use in case of primary system failure. You can easily switch to the secondary system to continue with almost no disruption. Many customers have implemented this hot-standby solution.
It does this by providing a better balance of workloads, as read-intense workloads can now instead be read from the secondary system.
Applications can also use implicit hint-based routing to determine where the read should take place. This means that you do not hard code the routing, but allow the application to determine this dynamically. Delta Merge Updating and inserting data into a compressed, sorted column store table is a costly activity. This is because each column has to be uncompressed, the new records are inserted and then recompressed again. Thus, the whole table is reorganized each time. For this reason, SAP has separated these tables into a Main Store read-optimized, sorted columns and Delta Store write-optimized, non-sorted columns or rows.
There is a regular automated database activity that merges the delta stores into the main store. This activity is called Delta Merge. Queries always run against both main and delta storage simultaneously. The main storage is the largest one, but because its data is compressed and sorted, it is also the fastest one.
Delta storage is very fast for insert, but much slower for read queries, and therefore kept relatively small by running the delta merge frequently.
The delta merge can be triggered based on conditions that can be set. If so, the delta merge is triggered. Delta merge can also be triggered by an application. Staying on top of the delta merge is critical to maintaining good performance of SAP HANA and the administrator is responsible for this.
This is called multitenancy. Multitenancy With multitenancy, there is a strong separation of business data and users who must be kept apart. Each tenant has its own isolated database. Business users would have no idea that they are sharing a system with others running different applications.
The system layer is used to manage the system-wide settings and cross-tenant operations, such as backups.
The benefit of a multitenancy platform is that we can host multiple applications on one single SAP HANA infrastructure and share common resources to simplify and reduce costs. Multitenancy is the basis for cost-efficient cloud computing. Database artifacts and source code would sit in a giant single system-wide repository that was organized by packages.
From there, development artifacts would be activated, and the generated run-time database objects and applications would be placed in database schemas and in the XS engine run-time.
Only one version of the activated objects could exist, which meant you could not deploy an updated version of an application whilst still retaining the original version. Each application that was developed could only have one deployment target.
You would have to create two separate applications. Customers are encouraged to move over to the new architecture to take advantage of many new features. Eventually, XS classic will no longer be relevant and will not be the focus of any more development by SAP.
XSA is decoupled from the database to provide more flexibility and ease of scalability. Application source code is no longer stored in the internal repository of HANA, but in an industry standard, external repository called Git. This allows much improved version control and collaboration. Git manages source objects across multiple instances of HANA. This includes the generation of deployment containers, which are used to store all run-time objects for an entire application.
With this new architecture you can build multitarget applications MTAs. This means that one development project can be deployed multiple times to on-premise or cloud applications. It also means that you can run different versions of the same applications simultaneously without having to have separate copies of the development projects.
High Availability: It stores this snapshot on the disk layer in an area called the data volume. This is called a savepoint. The frequency of savepoints is configurable and it depends on how frequently the database changes due to updates, inserts, and deletes. It is possible to collect many savepoints over time so that a restore can take place from any point in time. However, it is important to develop a mechanism to ensure that no data is lost, even between savepoints.
To do this, every committed transaction is recorded and saved to a log area. Thus, every update to the database since the last savepoint is captured. Power Interruption When power is restored, SAP HANA automatically reads the last savepoint and then reapplies the committed transactions from the log since the savepoint. This ensures that the system is exactly where it was at the precise moment you lost the power.
This all happens automatically in the background. Even though a complete reload of memory from the persistent layer is fast, it is also possible to identify the most important tables so that they are loaded to memory ahead of the tables that are less important.
This allows you to restore great performance as quickly as possible. This is called scale-out. Scale-out is often used to spread the processing load across multiple servers, which improves performance. Scale-out is also used to provide redundant servers that are on standby in case active servers fail. A standby server can be on warm standby, which means that it is in a near-ready state and does not need to be started from cold. Standby Servers Standby servers can also be on hot standby.
In this case, the primary server replicates the database log in real-time to a secondary server. This secondary server continuously replays the database log so that the databases are always in sync.
This means that there is almost no downtime when switching to the standby server, as it is identical to the primary server at all times.
This approach would be necessary for a mission-critical operation where down-time would be harmful to the business. Describing High Availability Note: This means that not only is the secondary server used in case the primary server fails, but the secondary server is also used to offload all read-intense work away from the primary server to balance the workloads.
For standby servers that are not running in hot standby mode, SAP HANA uses the savepoints and logs, described earlier, to load the standby server with the latest data. This means that more time is taken to bring the servers up than hot standby.
This is done to create an easy to maintain authorization concept that is reusable. Roles can inherit other roles to form a hierarchy.
There are many role templates provided by SAP that align to well-known developer and administrator job roles. This determines whether users have access to business data, database objects, system actions, development objects, projects, and more. It is still possible to perform many of the security-related tasks, such as creating a user or role, or setting passwords, in Studio. Where is SDI used? Choose the correct answer. What is XS?
Learning Assessment 8. What are advantages of column store tables? Row store tables are more efficient when there is a lot of repeating data values in columns Determine whether this statement is true or false. X True X False Why do we still need a persistent layer?
X A To store data that has been unloaded from memory X B To hold the delta store for newly-arrived records X C To enable full database recovery if we have a power failure X D To store data that is frequently used What is a multistore table?
What are the two storage components used to restore the database in case of power failure? What is scale-out? XS does not cleanse data, it is not an interface, and it does not handle excessive data loads — XS is an lightweight application server.
Learning Assessment - Answers 4. XSA uses Cloud Foundry architecture. Micros-service architecture is supported by XSA. Tables are found in the schemas, which are located under the catalog node. Learning Assessment - Answers 7. A HTML file is not a modeling object, it is used to generate a browser page.
Column store tables are optimized for read-intense analytical processing, not transactional processing where read and write is needed. They also use automatic compression to reduce the footprint and we only need to load columns that are required into memory.
Also, you can partition column tables. Learning Assessment - Answers Column tables are more efficient when there is lots of repeating data values in columns. This is because we can remove all duplicates of each value and in place use binary integers for a smaller footprint and faster processing.
X A To store data that has been unloaded from memory X B To hold the delta store for newly-arrived records X C To enable full database recovery if we have a power failure X D To store data that is frequently used You are correct! The persistent layer is needed to off-load low priority data from memory to disk when memory is full. It is also used to store periodic snapshots of memory in case we lose power and need to recover memory from the last saved snapshot.
The secondary read-only HANA can then be used for any read-intense tasks, thereby off-loading this work from the primary HANA to balance the workloads and increase overall performance. A multi-target application MTA is built with XS Advanced and allows single development projects to be deployed to on premise or cloud.
The two components used are data volume, to store the savepoints that periodically capture snapshots of memory, and log area, to store the log that replays all committed transactions since the last savepoint. X A Use of standby servers in the event of hardware failure X B Use of remote servers to store archived data that is rarely used X C Use of commodity servers that are used in high volume steaming applications X D Use of multiple servers to spread processing and improve performance You are correct!
Scale-out is the use of standby servers that we can switch over to in case of primary server failure. It is also the use of multiple servers to help spread the workload. Modeling in the Application In a traditional application, the role of the database is to simply provide raw data.
There is usually very little, or even no data processing in the database. The raw data is sent from the database directly to the application. The application then begins to process the data by combining it, aggregating it, and performing calculations in order to generate something meaningful.
We can find ourselves moving a lot of raw data between the database and the application. When we move raw data to the application layer we make the application code very complex.
This means we can first process the raw data and turn it into something meaningful in the database before passing it on to the application. With SAP HANA, we build calculation views to combine data from multiple tables and apply filters, conditions, calculations, and aggregations. Therefore, instead of the application processing the raw data, the application calls the required calculation views and the processing is pushed down to SAP HANA.
This is efficient in the following ways: We only move the results of the data processing to the application. Reuse of Models In traditional applications, there is a high degree of redundancy in the application code. Developers find themselves continually creating the same code to process data. When dealing with highly normalized database models, such as those used with SAP Business Suite, there can be many individual tables that need to be called and combined with joins.
These joins can often be pushed down to most databases. This means that the applications can pass variables down to the calculation views a response to a filter value that came from a business user. Many of the calculation views can also call procedures that have input parameters. Calculation views can consume other calculation views. This encourages a high degree of modularization and reuse. Core Modeling Versus Advanced Modeling The term, core modeling sometimes called view modeling , refers to the development of models that handle common analytical functions.
These functions include filtering, aggregation, calculations, and so on. When we develop models to handle advanced analytical scenarios, such as predictive, spatial, textual, and graph, we refer to this type of modeling as advanced modeling. For now we will focus on core modeling. Each of these view types had its own unique features, and typically, all three view types were required. However, since the calculation view has inherited all the features of the two other views, we no longer develop attribute or analytic views.
In fact these types of views can be migrated to calculation views using the supplied tools. The calculation view can now do it all, which means we no longer have to be concerned about which view type to use. Calculation View Creation When you create a calculation view, you choose various combinations of settings. These settings define four basic types of calculation view. Choosing the Correct Calculation View Settings The settings are chosen when you first create the calculation view. The four types are as follows: Modeling Dimensions Dimensions are most likely to be created first.
The purpose of a dimension type of calculation view is to define a list of related attributes, such as material, material color, weight, and price. This list can be directly consumed by an application using SQL. However, it is most likely to be found as a component in another calculation view of the type Cube when creating star schemas.
They can only contain attributes. Without measures, aggregation is not possible. Only direct SQL access is allowed. It can be helpful to think of calculation views of type dimension as master data views. You would not model transaction data using dimension calculation views as no measures can be defined, and measures are for modeling with transactional data.
Be careful not to confuse measures with attributes that are of a numerical data type, such as integer or decimal. A numeric field can be included in this dimension calculation view but it cannot be modeled as a measure it must be modeled only as an attribute. This means that there is no aggregation behavior possible.
For example, you could include weight but you cannot sum this. The output will appear as a list of all weights. You then proceed to define the source tables, the joins, the filters, and the columns that are to be exposed. It is also possible to define additional derived attributes.
An example of this could be a new column to generate a weight category based on a range of weights, using an IF expression. You are then able to rename any columns to be more meaningful.
Remember that the column names originate from the database tables, and these names can often be meaningless to developers and business users. Modeling Cubes The next type of calculation view is the type Cube. This type is used to define a dataset comprised of attributes and measures that can be used in a flexible slice and dice format.
These tables can be queried using any combination of the attributes and measures that they include, to create either a line level or an aggregated dataset. Reporting tools can directly access this type of calculation view. They can also be accessed via SQL.
Do not select the Star Join flag.
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This is used later, in the next calculation view type. You then select the table, or tables, which are to be included in the model. Typically, you choose a transaction table so that you have columns from which you can define attributes and measures. It is possible to include more than one table. For example, you may need to include a header and a line item table to form the complete picture of a sales transaction. In this case, you simply join the tables using a JOIN node.
Then, select the columns from the tables that are to be exposed. You can optionally set filters and define additional calculated columns. Then, rename any columns to provide meaningful names to the developer and business user. Modeling Star Schemas The next type of calculation view is the Cube type, but with an additional setting — star schema. This is again based on the cube type of calculation view, but with one or more dimension calculation views joined to the model.
Adding the dimension views enables you to request aggregations of any measures in the fact table by any combination of attributes. You are not limited to just those attributes from the fact table, but also attributes from any dimensions. This significantly increases the analysis possibilities. This type of calculation view follows the same rules as the cube type. It is consumed directly by reporting tools as well as SQL, and it can include attributes and measures.
It is used to present aggregated views of the dataset in the most efficient way. Then, choose the columns to expose, set any filters, and create any calculated columns. What you are doing up to this point is forming a fact table that will be used as the hub of the star schema.
There would be some gains but traditional databases and applications were designed around old, restricted hardware architecture. Put simply, the business software needed to catch up with advances in hardware technology, and so a complete rewrite of the platform was required.
The platform is the software side of the equation that was built entirely by SAP. For example, imagine an application that allows a project manager to quickly check all team members have competed their time sheets. This could easily be developed as a web application where only a web browser and SAP HANA is required, no application server is needed. Everything the developer needs at design time is there, and what is needed at run time is also there.
However, it is not enough to simply store these new data types, we need to be able to build applications that can process and integrate this data with traditional data types, such as business transactions. It has built in high availability functions that keep the database running and ensure mission critical applications are never down. Further data footprint reductions are achieved because, we removed unnecessary tables and indexes.
We also reduce the in-memory data footprint by implementing data aging strategies. The benefit of this is that data that is used less frequently can be moved automatically from HOT to WARM store so we are not filling memory with data that is less useful. However, this data is still available whenever it is needed.
Technically we could do that, but it would not be efficient. Most business applications refer to only a small subset of data for their day to day running, and that is typically the most recently created data.
We also use temperatures as an easy way to describe where data fits on the scale of usefulness. Active or hot data is the data that is very recent, or perhaps data that, although old, is the focus of a current analysis and is being processed.
Passive data, usually called warm data, is useful data but less used. Cold data is rarely, if ever, used. In traditional systems data was either hot in the database or cold archived outside the database. There were usually never multiple temperatures of data due to the limitations of the technology at that time. Big Data is a term often used, and this refers to the staggering amounts of data that is being collected, especially by machines, sensors, social media, and so on.
In recent years, solutions have been developed for the storage of this type of data. One of the most popular solutions is called Hadoop. Hadoop is not a relational database, and its key role is to provide data storage and access to systems that require the data.
Hadoop and other Big Data solutions should be considered in the overall planning for data management. Push Down Processing to SAP HANA In the past, the key job of the database layer was to listen out for requests for data from the application server and then send that data to the application server for processing. Once the data had been processed the results would be sent back down to the database layer for storage.
This is done quickly using in-memory. Detailed data was summarized into higher level layers of aggregates to help system performance.
On top of aggregates, we built more aggregates and special versions of the database tables to support special applications. As well as storing the extra copies of data, we also had to build application code to maintain extra tables and keep it up to date. A backup to these extra tables was also required, so even the IT operations were impacted. In addition to aggregates, we have another inefficiency that we need to remove. Database indexes improve access speed because they are based on common access paths to data.
But they need to be constantly dropped and rebuilt each time the tables are updated. So again, more code is needed to manage this process. The traditional data model is complex, and this causes the application code to be complex. With a complex data model and complex code, integration with other applications and also enhancements are difficult, and simply not agile enough for today's fast moving environment.
We do not need pre-built aggregates. SAP HANA organizes data using column stores, which means that indexes are usually not needed - they can still be created but offer little improvement.
As well as removing the aggregates and indexes from the database, we can also remove huge amounts of application code that deals with aggregates and indexes. We are left with a simplified core data model and also simplified application code. Choice of Configurations For on-premise deployments, SAP HANA is delivered as a brand new, all-in-the-box application where all software and hardware is provided and fully configured by certified partners.
There are many different configuration options available to suit all sizes of organization. Many customers already have hardware components and also software licenses that they would like to re-purpose and so this flexible approach ensures implementation costs are kept to a minimum.
This restriction does not apply for non-production installations, for example, development, sandbox. The following versions of Linux are supported: Flexible Deployment Options Cloud On Premise Hybrid Run all applications in Run all applications on Leverage right deployment option the cloud premise that meets business priorities Figure On-premise means the entire solution, the software, network, hardware is installed and managed by the customer.
A cloud deployment is managed by SAP and other hosting partners and this means customers do not have to be concerned with managing the infrastructure, they can simply get on with using and developing applications with SAP HANA. Another possibility is a hybrid approach where a combination of on-premise and cloud is used. SAP HANA is capable of handling any type of application from analytical, transactional, consumer facing, back office, real-time, predictive, and cloud and more.
SAP HANA is Central to SAP's Strategy With a single, scalable platform powering all applications, customers have an opportunity to simplify their landscapes and also to develop new, innovative applications that cover all data sources and data types. The real value in the virtual data models is the business semantics added by SAP. Raw database tables are combined and filters and calculations added to expose business views ready for immediate consumption with no additional modeling needed.
So instead of having to refer to multiple raw tables in your reporting tool, creating joins and unions manually, applying filters to add meaning to the data, you simply call a view from the virtual data model and the data is exposed.
Whilst they are different technical approaches, they both deliver the same outcome, a virtual data model that exposes live operational data for analytics.
This could be achieved in a variety of ways using standard SAP data replication tools. Connect loT with Core Business Processes Traditional business systems are simply not ready to support the massive growth in device connectivity that is proposed by the Internet of Things loT.
Imagine having access to detailed machine data a few clicks away from a business transaction. Let's consider this scenario: A customer is disputing an item on their invoice and complains that the paint we supplied is too lumpy.
So we drill down from the invoice, discover the actual line that relates to the paint problem, we drill down to the batch that we supplied, then we drill down to the shop floor data to check the recipe for the paint was correct. But wait, when we drill down to examine the data generated from the paint mixing machine we see it did report overheating problems between 2.
We now need to talk to the engineers on the shop floor to find out why this was not detected and get back to the customer with a fast solution. Sport Analytics — Provide fans with real time in game statistics in order to fully engage them. The NBA is already up and running with this, and many other sports bodies and teams have similar platforms. These include the following: SAP manages the entire solution.
Customers just provide the business users! There are also many ready built applications from SAP and partners that are powered by SAP HANA and are available in the cloud and can be used standalone or integrated with existing applications. You can develop Java applications just like for any application server. You can also easily run your existing Java applications on the platform.
It is not public and is for dedicated customers and their applications. You can consider HEC as an extension to a corporate network. So, customers pay for what they need and do not have to worry about procuring expensive hardware, software and skills to run their SAP HANA powered applications.
Just bring your business users and any devices. True or false? Learning Assessment - Answers 8. SAP HANA uses a row and column store database and the physical storage can be either in-memory, on disk, or a combination of both.
There are a large number of engines available. The Application Function Library AFL is a repository of ready made common business functions and predictive algorithms that developers can use in their applications. EIM is optional and is only installed if required. The recent addition of EIM means that customers no longer need to install and use these additional components for loading. Customers simplify their landscapes by using the built-in EIM capabilities.
SDA enables the management of data at different temperatures. SAP NetWeaver is still required to provide the business layer, the flow logic and the connectivity and orchestration with other applications. Of course, data has to be acquired and you may use the built-in EIM components or external data provisioning tools as mentioned earlier, in addition to remote sources. This component is optionally used to support light, web based applications where a full application server and all its capabilities would be overkill.
XS provides all the application services you need to access the required data from with SAP HANA's database, call the data processing engines and also the application logic.
Evolution of the XS Engine Figure This new version is called XS Advanced and provides even more application services, employs open standards, and is capable of supporting larger and more complex applications written in many more languages. Classic XS is tied to the database server and so it was not possible to scale up the XS component separately. With XS Advanced it is possible to scale only that component, so more power can be given to the application processor and the database remains unaffected.
All new development objects are now created in the new XS Advanced architecture. XS Classic does not use Cloud Foundry, so customers with XS classic do not have the resources to develop a single application for use in the cloud and also on-premise. XS Advanced also uses Cloud Foundry architecture and so applications can be written once and deployed either on-premise or in cloud with no redevelopment. This means applications are divided up into small chunks to allow the developer to choose the development language.
It also means that it is possible to configure each part of application to consume more or less resources as needed. XS Advanced is built on a micro services architecture. For many people, it is the only interface they need. It is installed locally and is based on Eclipse and is developed in Java. See separate lesson later for details. The host and instance this pair of details identifies the exact target system 2. You can optionally give each connection a description so it is easy to identify to purpose of each system when the list of connections becomes long.
It is possible to export the list of connections to a file so these can be imported by others so they do not have to manually define the connections.There are many different configuration options available to suit all sizes of organization. This means that often, less data passes between the database and application layer.
Describing Data Acquisition Tools Figure They can only contain attributes. This is again based on the cube type of calculation view, but with one or more dimension calculation views joined to the model. Describing High Availability 70 Lesson: M o d e le r The Q u ic k V iew is a practical entry point, dedicated to the modeler perspective.
Roles can inherit other roles to form a hierarchy.