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Overview of data provisioning in SAP HANA with the following tools: o Flat file Details, Configuration & Transaction. Exam.: Sample Questions.: PDF Link. Search. Home · HA - SAP HANA Introduction(Col99). HA - SAP HANA Introduction(Col99). March 5, | Author: asadshoaib | Category: N/A. Implementation and Modeling classroom SAP HANA. HA HA FI SAP Finland. SAP HANA. OHA HA Sap Hana Sap Press | Free PDF.


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HA SAP HANA Introduction.. COURSE OUTLINE. Course Version: Course Lesson: Overview of Data Provisioning in SAP HANA. 9. Lesson. HA - SAP HANA Introduction(Col99) - Ebook download as PDF File .pdf) or read book online. SAP HANA HA For Any SAP / IBM / Oracle - Materials Purchase Visit: salelive.info OR Contact Via Email Directly At: [email protected] SAP HANA.

Skip to main content. Log In Sign Up. Vikram Chow. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company.

For data that is accessed frequently, we call this hot data. Data that is accessed less frequently is called warm data. Data that is rarely accessed often retained only for legal purposes is called cold data. For now, we will focus on hot and warm data. Quite simply, any data that is accessed by any application always comes from memory. So this means that if the table is sitting in the persistent layer, the moment it is needed, the table is then automatically loaded to memory.

Column tables can be partitioned and SAP HANA is smart enough to know only to load the required columns and partitions to memory and leave the unwanted columns and partitions in the persistent layer. Delta Merge Updating and inserting data into a compressed and 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, and 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 you can set. If this is true then 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. Refer to training course HA to learn more about delta merge.

Multi Tenancy With multi-tenancy there is a strong separation of business data and also 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 multi-tenancy platform is that we can host multiple applications on one single SAP HANA infrastructure and share common resources in order to simplify and reduce costs. Multi tenancy is the basis for cost-efficient cloud computing. You can skip this step if you have already logged on. Locate the table M A R A by using a filter on the ta b le s node. Open the definition of table M ARA and identify whether the table is row or column store.

Identify the key columns of table M AR A. Identify the number of records loaded to the table and also the storage used by the main and delta areas. Preview the data of table M A R A. Use one of the two following options: Why is there no delta storage value for this table and why are there no partitions available? There is no delta storage value for this table because this is a row table and delta storage is only relevant for column tables.

There are no partitions available because this is a row table and only column tables have partitions. The table list is now filtered and displays the table M ARA in the filtered list.

Notice the icon for the table represents column store as this is a column store table. This screen shows the table structure with all columns, their data types, and length.

This information is provided in the top-right corner of the screen. Identify the key columns of table M A R A. Preview the data of table M AR A. This is called a savepoint.

The frequency of savepoints is configurable and really depends on how frequently the database changes due to updates, inserts, and deletes. But savepoints take place every few minutes, what happens if the power goes off after we have added some new records and we did not yet reach the next savepoint?

Do we lose this data? Between savepoints, every committed transaction is also saved to a log area. This log area is often based on flash memory SSD to ensure ultra fast access. So we capture every update to the database. This ensures the system is exactly where it was when we lost the power.

This all happens automatically in the background. We call this scale-out. Scale out is often used to spread the processing load across multiple servers in order to improve performance. Scale-out is also used to provide redundant servers that are on stand-by in case active servers fail.

If a server fails, SAP HANA can automatically swap out to a standby server in order to ensure downtime is minimized or even eliminated. 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 can also be on hot standby. In this case, the standby server continuously replays the database log so that the databases are always in sync and ready to go. In this case there is almost no downtime when switching to the standby server. This approach would be necessary for a mission critical operation where down-time would be harmful to the business. SAP HANA simply uses the savepoints and logs, described earlier, to bring the standby server up to date with the very latest data.

Authorization — for each role or user, grant and revoke access to business data, database objects, system actions, development objects, projects and more. Encryption — encryption services to ensure your data is stored securely and also allow you to set up encryption for secure communication between SAP HANA components. Open any perspective where the view is available and look for the node S e c u rity. S y s te m s From this node, open the S e c u rity C o n sole to view the system settings and policies.

Locate the user or role node and double click to manage authorizations. Where is SDI used? What is XS? Learning Assessment 5. True False 7. What is a perspective? Learning Assessment What are advantages of column store tables? A Data footprint is automatically reduced through compression B Only the column required are actually loaded to memory C Columns can be partitioned D Aggregates can be created Row store tables are more efficient when there is lots of repeating data values in columns D e te rm in e w h e th e r th is s ta te m e n t is tru e o r false.

True False To maintain good read performance in a constantly changing database, which two components are used? Why do we still need a persistent layer? What are the two storage components used to restore the database in case of power failure? What is scale out? Learning Assessment - Answers B To hold the delta store for newly arrived records. D To store data that is frequently used. B Use of remote servers to store archived data that is rarely used.

C Use of commodity servers that are used in high volume steaming applications. Create Calculation View — Dimension 93 Exercise 4: The role of the database in a traditional application is to provide data.

The application sends down S e le c t statements to individual tables in the database, and often, many tables are involved. The raw data is sent from the database to the application.

The application then begins to process the data by combining it, aggregating it, and performing calculations. It may be possible for the database to take on some of these basic tasks but largely, the database is asked to do nothing more complex than supply raw data to the application, which does all the hard work.

Therefore, we can find ourselves moving a lot of raw data between the database and the application. We move data to the processing layer, making the application code complex. It has to deal with the data processing tasks as well as manage all of the process flow control, business logic, User Interface Ul operations, integrating data from multiple sources, and so on. Modeling in the Database With SAP HANA, we can build a modeling layer on top of the database tables, to provide data to the application in a ready-to-go, processed form.

This is efficient in the following ways: 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 the applications can pass variables down to the view, for example, a response to a filter value from a business user.

Many of the views can also call procedures that have input parameters. Information Views can consume other Information Views. This encourages a high degree of modularization and of reuse. These include textual, spatial, and predictive functions. Although the attribute and analytic views have been available since the first release of SAP HANA, they have become less important as newer releases of SAP HANA delivered more powerful calculation views that slowly took over all the functionality of the other two views.

Since SPS12, attribute and analytic views should be avoided. This means modeling is simpler with only one type of view to consider. Migration tools are available so that customers can easily convert the attribute and analytic views to calculation views. We will cover attribute and analytic views later to ensure you develop some basic skills and awareness of these. For now, let's focus on the current recommendation by SAP, which is to always model with only the calculation views.

Choosing the Correct Type of Information View By selecting various combination of settings, you can define three basic behaviors of a calculation view: Dimension 2. Cube without star schema 3. Cube with star schema Modeling a Dimension Let's start with a dimension as this is the most likely to be created first.

The purpose of a dimension type of calculation view is 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 although it is most likely to be found as a component in another calculation view of the type CUBE when creating star schemas.

Dimension type calculation views do not contain measures, they only contain attributes.

This means that without measures, aggregation is not possible. Reporting tools cannot directly access calculation views of type dimension. Only SQL access is allowed. It might 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 and must be modeled only as an attribute. This means 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.

Modeling Dimensions You then proceed to define the source tables, the joins, the filters and columns that are to be exposed. It is also possible to define additional derived attributes, for example, a new column to generate a weight category based on a ranges of weights using an i f expression.

Finally, you are able to rename any columns to be more meaningful for the calling application. Remember, the column names originate from the source tables, and these names can be user unfriendly. Modeling a Cube - Data category: Modeling a Cube Now let's move on to the next type of calculation view. This is of the type Cube and is used to define a data set made up of attributes and measures that can be used in a flexible slide and dice format.

This is not a star schema as there are no dimensions defined we will cover that in a moment but simply a data set based on one or more transaction tables that can be queried using any combination of attributes and measures to create an aggregated data set.

Reporting tools can directly access this type of calculation view as well as access via SQL. Do not set the Star Join flag. This will be used later in the third and final calculation view type. You will then select the table, or tables that 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. Now select the columns from the tables that are to be exposed, you can optionally set filters and define additional calculated columns. The last step is to rename any columns to provide meaningful names to the user.

Modeling a Star Schema Now comes the final type of calculation view type: The key reason for adding the DIMENSION views is that you are then able to request aggregations of any measures in the fact table by any combinations of attributes, not just those attributes from the fact table, but also attributes from any dimensions.

This increases the analysis possibilities significantly. It can include attributes and measures. It is used to present aggregated views of the data set in the most efficient way. Select the transaction tables and create joins to combine the transaction tables. Then choose the columns to expose and set any filters and create any calculated columns. What you are doing up to this point is to form a fact table that will be used as the hub of the star schema.

Creating Information Models The last step is to improve the names of any columns by using the rename function in the semantic node. There are some limitations when using the Web Workbench. For example, only calculation views — of any type — can be created and maintained in the Web Workbench whereas attribute, analytical and as well as calculation views can be maintained in the SAP HANA Studio. As calculation views are the most important of all the views, and, in fact, may be the only type of view you will ever create, then working with the Web Workbench would be absolutely fine.

You access this view from the Help menu option. However, you cannot work with attribute or analytic views. Creating Information Models Studio. Calculation views created with one interface can be accessed with the other. These views will be joined later to the Sales fact data in another calculation view. Database Schema: Training Column Tables: In this exercise, when values include , replace these characters with your own student number.

Create a package called s t u d e n t. Add all fields to the output. Connect the Projection nodes. Add all columns to the output of the final Projection node. Save and activate the new view, then check the Job Log view to view the status of the activation job. Connect the Join node to the Projection node. Leave all other entries with default values and press OK. You should now see two Projection nodes, one on top of the other.

The Status should read Completed successfully. You should see three records. You should see eight records. You will now combine the two views you created earlier that represent the dimensions with a sales transaction table to create a star schema that can be used later for multi-dimensional analysis. Business Example You have been asked create a multi-dimensional star schema to allow users to slice and dice product sales data for US customers.

It should be possible to present all sales figures aggregated by any combination of customer and product attributes. Before you start, preview the sales fact table to explore the fields you will use in your star schema. In this exercise, when you see , replace these characters with your own two digit student number. Column name Add as aggregate column? Connect the Aggregation node to the Star Join node.

In the S e m a n tic node, assign the correct column type to each column in the output. Save and activate the new calculation view, then check the Job Log view to view the status of the activation job. Now add a calculated column to the view to indicate whether delivery is free of chargeable based on the customer location.

Use the data in the following table: Creating Information Models We built these views using a graphical approach. Did you notice that no coding was required? But in fact, there is a way to build calculation views using SQLScript code. We call these types of views scripted calculation views. These types of calculation views allow the developer more freedom to use standard SQL and SQLScript functions and have more control over the data flow logic.

The outcome is largely the same as if you were creating the calculation view using the graphical approach — you produce a data set based on original database tables by joining, filtering, aggregating measures over any number of attributes. The view can then be consumed directly by a reporting tool or via SQL. You can certainly have measure and attributes. It is based on standard SQL but includes many extra functions to allow the developer to include conditional flow control logic such as If, Then, Else and While.

This provides a similar level of control that is found in application programming code such as ABAP. An example: This is not possible in standard SQL. This can be done manually where you type a column name and its data type and length if applicable. Since SPS11 you can also have the system define the columns automatically by referring to an existing table or view.

To write the script you must select the Script View node in the scenario pane. The script editor now appears in the centre of the screen. Here you describe what you would like to happen using the language SQLScript. SQLScript is based on standard SQL but includes additional keywords and functions which have been developed by SAP to extend the capabilities of standard SQL by allowing more of a procedural style of coding the logic, plus the use of additional data types.

Make sure you align the output columns with the column in the script using the same names. Finally, rename any columns in the semantic node and add any other semantic information, then activate and preview.

The reason for this is that an SQL table function offer far more flexibility in how it can be used as an input sources in other views compared to a scripted calculation view. An SQL table function can be used to accomplish exactly the same as the scripted calculation view. It is good to know that it is possible to have the system easily convert any scripted calculation view to an SQL table function using a provided conversion tool. This tool is found in the scripted calculation view editor in the top right of the screen.

SQL table functions are covered in the HA course. W hat is an Attribute View? Attribute Views Overview Analytic View Analytic views are used to develop star schemas where a central fact table is surrounded by dimensions to create a multidimensional data set used for OLAP reporting. However, calculation views of type CUBE has almost completely taken over this type of view and contain almost all functionality. The only functionality that has not yet been included in calculation views of type CUBE, is the temporal join.

Temporal joins provide are joins between the fact table and the dimensions but also include extra date parameters so the fact row can be joined to the correct dimension row based on a specific date. Temporal joins are heavily used in data warehousing when dimensions contain a lot of historical attributes that need to be carefully joined to the facts at precise dates.

Analytic Views - The multidimensional Model Note: Up to an including SPS11 temporal joins were not supported by calculation views, only by analytic views. Now in SPS12 temporal joins are supported in calculation views.

This means that there is now no reason at all to continue developing analytic views and calculation views should be used as all analytic view functionaliity has been made available in calculation views. P ro ced u res Procedures define reusable data processing functions. This means that you can avoid developing overly complex calculation views.

You simply define a procedure and call this from the calculation view. A procedure always has one or more output parameters but a procedure can also have one or more input parameters. For example, you could create a procedure that calculates the tax for each item sold. We would simply define the input parameter as a tax percentage within the procedure, and then define the output parameters as the columns we want to generate including the new column for calculated tax.

Creating a Procedure The actual procedure is written in SQLScript and reads the input parameters and then writes to the output parameters. Procedures can be called from within calculation views or even standalone via SQL. Procedures can also call other procedures. Procedures used within modeling are mostly used as read only. In that case they are called stateless as they don't alter any data in the database.

But procedures can also be used to update, insert and delete data. These are called stateful, but these type of procedures are not allowed when called from calculation views.

Stateful procedures are more likely to be used by developers who build applications. D ecision T a b le A decision table can be used to store business rules that can be called by any calculation view or procedure.

Modelers and developers frequently use decision tables to ensure they build the most efficient objects. The benefit of using decision tables is that you do not have to lock in any business rules into a calculation view or procedure that might need to be changed often.

For example, the target sales for each sales region. This hard coded condition would need to be maintained in each calculation view. So a decision table can be used to store all business rules in a central place. Maintenance of the rules is done in one place and the interface is very simple. In fact you can even maintain and upload the rules from an Excel spreadsheet.

Security Considerations for Modelers Unless you grant access to users, they will see no data from your information views. A modeler needs to be aware of the different levels of security. Here is a short overview covering the basics. Security Considerations in Modeling The first level of access that is required is to the actual information view and also the source tables that are included in the view. This is achieved by granting a SELECT privilege on the view of table to the user or the role to which the user is assigned.

Just because a user has access to the view does not mean they will see any data. Now they need to have access at the row level. This is achieved by defining an analytic privilege and then assigning this to the user or the role to which the user is assigned. Security is a detailed topic and is covered in course HA SAP HANA Live Architecture Due to the high degree of normalization of the database schemas found in SAP Business Suite applications, tables can appear complex, fragmented, and are often difficult to consume without a thorough understanding of each schema.

J Uses joins to combine re-use views to a consumption query view t Adds calculated attributes along the way Adds input parameters and variables too Mostly graphical type calculation models Figure They are compiled into column views that are technically no different from the column views created from custom information views.

The reporting tools however are able to aggregate the data as required. This makes setting up replication much easier. 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.

This means you are able to provide pop-up filters to the users to make personal data selections. As well as the technical definition of the data model and consumption model, CDS allows us to fully describe the business semantics in the models so that the application code does not have to take this on.

For example, we can describe the correct way to aggregate a measure, or we can include the business user text labels for attributes so they are ready for consumption in different contexts, such as sales, purchasing, and so on. There are two types of CDS: With CDS we not only expose tables for analytic purposes, but we can also use CDS in many other use cases such as search applications and planning applications. CDS allows the developer to define the data model at the same level as the application.

This means that the developer does not need to go to the database to create tables or views. They use CDS to describe the database objects they need using source documents. Then, they activate the CDS document so database objects are then created.

Because the database objects are described using source code, the source code can be stored alongside the application code and transported as one.

If the developer were to create the database objects and models outside CDS, they would have to manage the transports separately from the application code, which can get pretty messy and introduces additional risk. But the technical implementation is a little different.

This means that once you create your data model, you can de-couple it from the database and move it to another one. Therefore, CDS allows us to define the data model and consumption model as a layer between the database and the application logic.

This provides great flexibility, no more locking in data modeling logic in the application, and no more locking the data modeling logic in the database. CDS Editors Note: The Missed Opportunities with Information Silos Many organizations already rely on spatial data processing and use specialist applications alongside, but separately 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.

The tank shows up on a map, the coordinator then uses another application to look up the nearest engineer who is then dispatched. It would be more efficient if, at the time of generating the repair order, the ERP application was also able to locate the tank, identify and dispatch the nearest qualified engineer 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.

HA100 - SAP HANA Introduction(Col99)

Modeling and Data Processing with SAP HANA provide information of other equipment in the close vicinity that is due an inspection soon, to save having to make separate visits.

This would be possible if the core business processes were integrated with spatial data and analysis. 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 to do with 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. For example, a map of a state, representing the union of polygons representing zip code regions. You can then use methods and constructors to access and manipulate the spatial data. Contains Tfmpc. Calculation View - Spatial Join Operations Calculation Views now support spatial operations like 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 course HA You may need then to extract meaningful information out of your data, to be able to analyze it properly. For example, when looking for duplicate master data where mistyped names are common, or you may need to perform linguistic searches.

For example, you search for the string computed and you would like to see also compute, computing, and computes in your search results. You may also have additional search requirements, like ranking your search results or search one string at once over all the fields of a table.

For example you could search for the string Waiidorf and find all exact and approximate to a degree that you can specify in your search matches, like Waiidorf,wadiorf,Vaiidorf , and Wahidorf f. In general, a Fuzzy Search will look for all strings matching the given one exactly or differing by missing, added, or mistyped characters. The behavior is similar to what you experience when you search for a Web page on a search engine: For example, it allows linguistic markup, like for example identifying the part of a speech verbs, nouns, adjectives, and so on.

It also allows you to identify entities locations, persons, and dates in an unstructured text. Predictive Analysis is a new breed of advanced analytical applications that provide insight by processing large amounts of data, historical and current, using powerful algorithms to generate probable outcomes.

Traditionally, predictive analysis was out of reach of most organisations due to many reason including: There are many use cases for predictive analysis. Here are just a few: Explaining Predictive Modeling lealthcare CRM Sales Identify revenue forecast based on customer Predict likelihood of disease to begin early opportunities and pipeline execution. Banking lities Identify key behaviors of customers likely to Forecast demand and usage for seasonal leave the bank: CRM Marketing ernment Identify potential leads among existing Predict community movement and trends that customers and intelligently market to them affect taxing districts: But it is also possible to import more algorithms from the public R library and also to develop your own custom algorithms in the R language.

R is a statistical scripting language 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. There are algorithms for all type of analysis and they can be grouped into families. For example:. Predictive Analysis is a detailed subject. For example, logistics and transportation, utility networks, and social networks. The basic idea behind graph modeling is that it allows a modeler to easily define a series of entities nodes and link them in a network that represents how they relate to each other.

Graph models can indicate flow direction between entities so 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, consumers are all represented with information stored along the connections.

But why would you want to define such models? The benefit is that it is easy to develop applications that can traverse huge graphs at speed so you can ask questions such as: How many hours has the product traveled between two specified points in the network? Where are all the possible points of origin of this product? Describe the entire journey of a product by listing all of the stop off points.

Graph processing allows us to discover hidden patterns and relationships in our data and all in real time. Example Graph Model The example in the figure, Example Graph Model, is one that most people can relate to, but there are many other interesting examples such as: Medical — create a network of patients, conditions, treatments, and outcomes for re-use in diagnosis and planning treatments of other patients.

Social network — using popular social media portals, find your customers and their friends, friends of friends, and likes or dislikes to create marketing opportunities.

Although it is possible to use standard SQL data definitions and query syntax to create and process a similar model, it would be extremely complex both in the definition of the model and also 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 in order to ensure that model development is more natural and simplified and 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 we 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 very similar to SQL and contains special keywords to allow easy graph query questions to be formulated such as, how far?

Where is the strongest connection? SAP recommends to use which of these types of view? Access to specific rows of a view is restricted by what? A Words that are closely matched in spelling B Words that are closely matched in meaning 8. Which open source language do you use to create custom predictive algorithms? Learning Assessment - Answers 5. This is true when running transactional business processes that require data maintenance such as creating a purchase order or amending an employee salary.

So in this case, we don't need to worry about setting up the provisioning of data to SAP HANA because the applications take care of that. What is Data Provisioning? Where will the data come from? So the act of provisioning this additional external data is simply referred to as data provisioning, and there are some great tools to help you implement this. You might have thought that data provisioning is just another term for data loading.

Not quite. Whilst this is certainly possible, data provisioning also includes other approaches such as data streaming and data virtualization. So data provisioning is an umbrella term used to describe the techniques and tools used to make data available to SAP HANA applications.

Transformation vertical axis — During provisioning, data can be transformed. For example, to align billing codes from different systems, convert currencies, lookup missing zip codes, calculate rebate values. For example, when data needs to be harmonized into a single stream from different sales order entry systems. SAP HANA allows any combination of provisioning frequency with any degree of transformation in order to meet the needs of all applications that requires data at any speed and of any type.

But replication is a specific method of data loading. Replication typically means ensuring that data created in one system is duplicated to one or multiple target systems, usually in real time, and often managed record by record. But replication doesn't always happen in real-time. Replication can also take place periodically, for example every 5 minutes, especially 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, the data is unchanged.

Replication involves the physical moving of data and not simply virtualizing the data sources. The following are some examples illustrating this data provisioning method: There are many different technical implementation approaches that support replication ranging from the use of database logs to the use of database triggers.

What is essential is that the source or target system has some way of knowing that data has changed so a replication can be kicked off. SAP Replication Server is a sophisticated transactional data movement product that moves and synchronizes data across the enterprise, without geographical distance limitation, to meet demanding requirements in the enterprise such as guaranteed data delivery, real-time business intelligence, and zero operational downtime.

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 the database log. A database log is a history of all actions executed by the database management system and is often used in the recovery of databases after a crash. When replayed it 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.

So you would think this appears to be the perfect solution for all your replication needs, right? Describing Data Provisioning Tools Note: Remote Data Sync Use Cases SAP HANA remote data sync is useful when applications cannot remain continually connected to a central database due to connection problems, for example, a field engineer in remote location with a poor signal, or perhaps the application should not be continually connected due to connection costs.

These applications sync with the central database either periodically at set times or this could be triggered by an event. In all remote data sync applications, the remote data sync server is the key to the synchronization process. Synchronization typically begins when a remote data sync remote site opens a connection to a remote data sync server. 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.

For example, it remembers the exact sequence of updates from all remote clients. Imagine if field engineers were withdrawing the same spare part and at the same time other remote works were replenishing the same spare part.

For example, live dashboards can be kept up-to-date with real time transaction data. SAP LT Replication Server 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 and also the monitoring of the data movement. This means that SLT sets database triggers on any table that we would like to replicate from. When the database table is changed in any way insert, update, delete the trigger is automatically fired and SLT hears the trigger and fetches the data from the source system and transfers it to SAP HANA. 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.

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 datasources. This means you need to know the names of the source tables you wish to replicate from. When we think about replication we usually assume data is moved unchanged from the source to the target. But 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 modification are as follows: The filters can be set on multiple columns. ABAP is used to develop the transformation logic and so this is clearly a crucial skill to have on any SLT project where transformation will be made.

Any transformation applied to data as it is being replicated will have an affect on the time it takes for the data to arrive at the target. For this reason, only light transformations should be implemented.

Plus, writing data transfer rules for complex integration and cleaning can get very 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. They have asked you to replicate the data to an SAP HANA system so that dashboards can be produced to show the real time position of flight bookings. You need to learn the steps to replicate data. Add a new record to the source table. You can any valid data values for the field entries.

A dialog displays indicating fifteen records have been found. If you closed the table then you need to re-open it to see the new record. Load ETL is the process of extracting data from source systems, and applying transformations on the data before loading to a target. Most of these options require the installation and setup of additional software and hardware components that sit between the data source and SAP HANA.

These components cover a broad range of capabilities, for example, extract data, combine sources, cleanse, and load or expose the data to SAP HANA.

A S t HadOOp. Iw adala Figure This means that no additional tools and their associated hardware are required. We have removed the data provisioning extra tier.

Describing Data Provisioning Tools a serious look at this solution as it may provide a more simplified and complete solution for their data provisioning requirements. The following are the components of EIM: 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 and contain a sequence of nodes that represent the steps in the flow. Developers create jobs by dragging and dropping the nodes to a canvas in order to create the flowgraph. Single transform deals with Person names and titles, phone, email, Firm, Address information In Data Services, it is in 2 transforms Consolidate available configuration options: SAP Data Services has been around for some time and is very well established within the distributed landscapes of many customers.

SAP Data Services usually processes all data in its own engines and send the output to the target systems. Data Virtualization Customers have to deal with complex system landscapes across different locations, storing huge amount of data in different formats and on different platforms. Customers require a cost-efficient, easy-to-deploy solution to get real-time data visibility across their fragmented data sources, for example, operational reporting, monitoring, predictive analysis, and transactional applications.

Smart Data Access SDA — Is the name of the built-in tool set that provides the catalog of adaptors to connect to remote sources plus the federated query processing, which means that queries can be pushed down to the source databases.

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: You can create a fast and flexible data warehouse without expensive ETL, massive storage, security and privacy risks.

Gartner calls this a logical data warehouse. 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 visibility into customer behavior and sentiment.

A virtual table can easily be spotted in the database catalog as it has a small connector symbol added to the table icon. The following list outlines some of the benefits of Smart Data Access: Data Streaming Data streaming is the transfer and processing of continuous data from a source to a target. This often involves very high speed and high volume data transfer and also from multiple streams in parallel.

Sources of streaming data can range from very simple sensors to complex business systems. Data Provisioning in SAP HANA In today's highly connected digital world, data streaming is an essential enabler of real-time information to feed applications and dashboards.

The opportunities for the development of innovate applications are enormous. Enterprises today are flooded with streams of messages as things happen. Individual events may not be significant by themselves, but how do you know when something of significance has occurred?

You might have thousands of sensors reporting status every few seconds - and most of that information is uninteresting.

But when something is starting to go wrong, you want to know as soon as possible, so that you can take action before a small trend becomes a big problem. With SAP HANA smart data streaming, we can capture data millions of events per second arriving continuously from devices and applications, act on this new information as soon as it arrives, and react in real-time using alerts, notifications and immediate responses to continually changing conditions. In order to understand how Smart Data Streaming works, we need to explain the difference between a regular database query and a continuous query.

Database Queries Continuous Queries Step 2: Step 1: Wait for data to arrive. As it arrives, it flows through the Define the continuous queries to produce continuous immediate results queries and the dataflow Figure Describing Data Provisioning Tools e. Especially in proof-of-concept or sandbox testing projects, you can waste lots of time trying to connect SAP HANA to various source systems.

By the time you have put a server into the data centre and connected it to the source system, the time allocated for your project will be finished already. Perhaps you have a source system that is only able to generate flat files for interfacing and so this might be the only way to provision data to SAP HANA.

Process Flow: Select the target HANA database. Select the flat file to import. Select the existing HANA target table to load to, or choose to automatically create a new table. Confirm or adjust the suggested field mapping and column types.

Preview the output and if you are happy, execute the load. You will make copy of the file stores. Copy the stores. The following table shows you the credentials to access the network folder: Field Value User hanastudent Password hanareadonly 2. Define the table definition and data mappings.

Find the new imported table and preview its content. Consequently the architecture of those systems was designed with a focus on optimizing disk access, e. Introduction and Positioning HA Figure Computer Architecture is Changing Computer architecture has changed in recent years. Now multi-core CPUs multiple CPUs on one chip or in one package are standard, with fast communication between processor cores enabling parallel processing.

Main memory is no-longer a limited resource, modern servers can have 2TB of system memory and this allows complete databases to be held in RAM.

Currently server processors have up to 64 cores, and cores will soon be available. With the increasing number of cores, CPUs are able to process increased data per time interval. Historically it was mainly used for analytics and data warehousing where aggregate functions play an important role.

Using column stores in OLTP applications requires a balanced approach to insertion and indexing of column data to minimize cache misses. It is also possible to alter an existing table from columnar to row-based and vice versa. Fast — SW Side Optimization for Memory Conceptually, a database table is a two-dimensional data structure with cells organized in rows and columns.

Computer memory however is organized as a linear structure. To store a table in linear memory, two options exist, a row-oriented storage stores a table as a sequence of records, each of which contain the fields of one row.

On the other hand, in a column store the entries of a column are stored in contiguous memory locations. Key Facts: Parallel Processing Data is only partially blocked, so parallel processing is possible. Therefore, individual columns can be processed by different cores. For example: They permits to respond quickly to expectations through SAP delivered content.

Rapid Deployment Solutions Hint: Additional use cases can be find on http: A side-car scenario could be a first step to answer to urgent and important business need. CO-PA accelerator is an example of a side-car scenario. Agile Data Marts Data Warehouse environments are typically in-flexible against change.

Customers have reported that there may be only two or four slots during a year, where models are allowed to be changed. Businesses however are exposed to constant change. Sales regions change, product bundling may change, and cost centers are subject to almost constant change. Sometimes it may be interesting to simulate these changes before they take place.

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In these cases it is helpful to have an environment in which models may be adapted easily without impacting the data models in production. These may be benchmarking scenarios in which internal data like sales data is benchmarked against market indices obtained from external agencies. Such data is typically not loaded into the central data warehouse, but rather gets used by analysts or managers in local environments. A star schema consists of one fact table, and a set of dimension tables currently SAP HANA can not support versioned master data that would require a temporal join.

STEP 2 Create attribute views for the different dimension tables. STEP 3 Create an analytic view by selecting the fact table as data foundation currently SAP HANA supports only those models which host all key figures in one fact table , and join the attribute views to the data foundation.

In contrast to previous examples, operational data marts are not based on analytic de-normalized data models, but rather run directly on top of the operational data. The operational data gets transformed in a way that is suitable to answer a query, right when that query hits the system. The transformed data never gets persisted. There are four options for loading the data: Data Services provide standard ETL for loading the tables at defined moments in time 3.

STEP 3 Create an analytic views incorporating the attribute views. Why would you want an operational data mart? Classic DWH environments are complex and may require significant effort to model operational data marts. This performance hit is caused by the heavy workload from joins on many small tables, like it is the standard in highly normalized data models. The replication guarantees that the data in memory reflects on the last transactions which took place. And finally, the in memory computing provides very fast results even on high volumes of detailed data, still providing the option to touch on every detail which is contained in the data.

An optimized mix of global resources, remote support, and onsite consulting give you access to experts who can help you implement your solution on time and on budget.

Each of these solutions include educational material and training scripts for the functionality that matter most users start right away, without the delays associated with customized training.

SAP Rapid Deployment solutions bring it all together preconfigured software, fixed-scope implementation services, and the materials you need for a successful implementation With these solutions, you receive the best of traditional and subscription licensing models, so you will know the cost and scope of your solution up front.

These complete solutions enable the flexibility to accommodate future growth. Because SAP Consulting uses preconfigured content, you as a business get what you need to run your business out of the box, delivered quickly. SAP consultants only install what you need so that you can start faster with what's more important and expand as you need later.

The preconfigured content allows the project to be quick and lean because of the clearly defined scope, the knowledge transfer to users and the fast-track methodology. There are the four individual accelerators: With the base package of this rapid deployment solution you can choose up to 5 reports to be implemented. If you need additional reports you can add as many as needed.

Whereas sales managers use sales analytics to get instant overview information regarding the various performance indicators for their sales teams, the sales representatives focus on detailed checks of the results of their sales activities. Flexible Reporting Figure When should you schedule the kickoff workshop — and how much later should you plan scoping and refinement? What about user acceptance testing? End-user training? These solutions include the content to take the guesswork out of scheduling, substituting transparency and predictability that inspire key stakeholders to get on board and put their support behind your project.

Deployment Options Figure Deployment Options Lesson Summary You should now be able to: Exercise 1: Look and Feel Unit 2: It can be installed on a local client PC. Perspectives are predefined UI-layouts or views for several application uses. The Documentation Overview links to the current available documentation. It is possible to integrate several systems into one Studio.

Here you can choose Adding system and Folders. Reading the Cheat Sheets can provide you information on how to create a new folder or add a new system into the navigator view.

To get the imported landscape working you need to insert the passwords for all connections again. This can be done by right clicking on the instance, and amending the password in Database Logon User Figure Here all the systems which have been registered manual, or via import are listed. We will take a closer look to the tree structure each system has. The physical tables are located in the Navigation Tree under the Default Catalog node.

Expand this node and one will find a list of schemas. Schemas are used to categorize tables according to customer defined groupings. During metadata import one defines which schema to hold created tables.

Different schemas can be useful for grouping tables into categories that have meaning to users. This simplifies the process of identifying which tables to use when defining Information Models. One model can incorporate tables from multiple schemas. The schemas do not limit your modeling capabilities.

All the information models that will be created in the modeler will result in database views. System Monitor There is an integrated System Monitor which gives you an administration view about the system landscape.

When you use the System Monitor button all systems which are listed in the navigator tree are listed by default in the system monitor overview. You get the most important information about your systems.

Which information is shown can be configured by right clicking in the System Monitor view selecting Configure Table It is also possible to configure which systems will be shown in system overview by right clicking and selecting System Filter.

On the right top edge you find the fast perspective switch. The Quick launch of the modeler is open by default for the first instance in the navigation tree. If you want to change to another system, click on Select System. In the center of the screen you see a quick launch tab that allows the user to quickly jump to various sections including tools to: Note that there are two main sections in the navigation tree.

The Default Catalog node navigates to the physical tables, views, etc. Navigator View — Models The Content node displays the data from a data modeling perspective. Here the user will create: A collection of displayed views combined with their placement within the screen builds a perspective. Reset your perspectives will restore the screen to the default layout.

Development Perspective From the development perspective it is possible to check in and out development objects, connecting to a repository Figure Look and Feel Use Note: You can fill out the details below, as given by the instructor, and use these for all exercises during this course: You will complete the exercise by creating your own Information Package.

Procedure Note: Logging in via WTS is not required for all training locations i. Germany is using VDI 1. Logon to the WTS landscape: Remote Desktop into Server 3. In the next screen you have to choose one of the available remote desktop servers.

Type the name of the Server specified by your instructor. Remote Desktop Connection 4. In the Remote Desktop Connection dialog box enter user name and password given by the instructor. Logon 5. Open Administration Console Figure Overview Screen 7. Register a new System. Register a New System 8. Enter your credentials as given by your instructor.

Abcd Hint: If prompted for a security fallback for your password, click No. Change Initial Password The first customization you should do is now to adjust the default setting of the default client in the studio preferences. Studio Preferences Next, expand the Modeler node in the tree and click on Default Model Parameters.

You will now set your default client. The overall perspective setup can be maintained via the Window menu on top. For the next step, open the Administrator Console Perspective Figure Administration Console To easily switch between different perspectives you can click on the toolbar on top on the corresponding button. Show View 1 In the Show View dialog box you can choose which view you want to add to your perspective. The Content node is only visible inside the Modeler perspective.

Navigator Tree The Catalog If you do not see folders for all the types of Information Models, this may be because none are created. Content Folder Security Folder The Navigator tree can be customized via a small dropdown icon. Customize Navigation Tree 1 Using this functionality you can decide what you want to see as folders in the Navigator.

Customize Navigator Tree 2 Reset Perspective The Administration Console reflects the pre-delivered Administration Perspective. Pre-Delivered Administration Perspective Select Delivery Units from the Setup section. Delivery Unit Click on Create. Delivery Unit 3 Remember to replace XX with your assigned student number.

The other entries can be left blank. Delivery Unit 4 Verify that your Delivery unit is created. Delivery Unit 5 Create a new package.

Make sure you are in the Modeler Perspective Figure New Package 1 Enter studentXX for the package name and description. New Package 2 As a result you will see the following folder structure created automatically for you under the newly created package. Exercise 2: Persistence Layer Unit 3: Architecture HA Lesson: Architecture Figure The Transaction Manager is the component that coordinates transactions, controls transactional isolation and keeps track of running and closed transactions.

The client requests are analyzed and executed by the set of components summarized as Request Processing and Execution Control. Once a session is established, database clients typically use SQL statements to communicate with Request Processing and Execution Control.

For analytical applications the multidimensional query language MDX is supported in addition. Data manipulation statements are executed by the SQL Processor itself. Other types of requests are delegated to other components. For example, Data definition statements, such as definitions of relational tables, columns, views, indexes and Procedures are dispatched to the Metadata Manager. Architecture HA Figure SQLScript is used to write database stored procedures.

Procedure calls are forwarded to the Stored Procedure processor. Solutions depend on HW partner technology. Failover uses a cold standby node and gets triggered automatically. Landscape Up to 3 master name-servers can be defined. During startup one server gets elected as active master. The active master assigns a volume to each starting index server or no volume in case of standby servers.

Master name-server failure In case of a master name-server failure, another of the remaining name-servers will become active master. Index-server failure The master name-server detects an index-server failure and executes the failover. During the failover the master name-server assigns the volume of the failed index-server to the standby server.

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