What are the kinds of lookup? Free Essay Example

You can configure the Lookup transformation to perform different types of lookups. You can configure the transformation to be connected or unconnected, cached or uncached: Connected or unconnected. Connected and unconnected transformations receive input and send output in different ways. Cached or uncached. Sometimes you can improve session performance by caching the lookup table. If you cache the lookup table, you can choose to use a dynamic or static cache. By default, the lookup cache remains static and does not change during the session.

With a dynamic cache, the Informatica Server inserts or updates rows in the cache during the session. When you cache the target table as the lookup, you can look up values in the target and insert them if they do not exist, or update them if they do

informatica: Persistent cache and non persistent.

PERSISTANT CACHEIf you want to save and reuse the cache files, you can configure the transformation to use a persistent cache. Use a persistent cache when you know the lookup table does not change between session runs.

The first time the Informatica Server runs a session using a persistent lookup cache, it saves the cache files to disk instead of deleting them. The next time the Informatica Server runs the session, it builds the memory cache from the cache files. If the lookup table changes occasionally, you can override session properties to recache the lookup from the database. NONPERSISTANT CACHEBy default, the Informatica Server uses a non-persistent cache when you enable caching in a Lookup transformation.

The Informatica Server deletes the cache files at the end of a session. The next time you run the session, the Informatica Server builds the memory cache from the database

informatica: Dynamic cache?

You might want to configure the transformation to use a dynamic cache when the target table is also the lookup table. When you use a dynamic cache, the Informatica Server updates the lookup cache as it passes rows to the target. The Informatica Server builds the cache when it processes the first lookup request. It queries the cache based on the lookup condition for each row that passes into the transformation. When the Informatica Server reads a row from the source, it updates the lookup cache by performing one of the following actions: Inserts the row into the cache. Updates the row in the cache. Makes no change to the cache.

informatica: Difference b/w filter and source qualifier?

You can use the Source Qualifier to perform the following tasks: Join data originating from the same source database. You can join two or more tables with primary-foreign key relationships by linking the sources to one Source Qualifier. Filter records when the Informatica Server reads source data. If you include a filter condition, the Informatica Server adds a WHERE clause to the default query. Specify an outer join rather than the default inner join. If you include a user-defined join, the Informatica Server replaces the join information specified by the metadata in the SQL query.

Specify sorted ports. If you specify a number for sorted ports, the Informatica Server adds an ORDER BY clause to the default SQL query. Select only distinct values from the source. If you choose Select Distinct, the Informatica Server adds a SELECT DISTINCT statement to the default SQL query. Create a custom query to issue a special SELECT statement for the Informatica Server to read source data. For example, you might use a custom query to perform aggregate calculations or execute a stored procedure.

informatica:What is Data Transformation Manager Process? How many Threads it creates to process data, explain each thread in brief. When the workflow reaches a session, the Load Manager starts the DTM process. The DTM process is the process associated with the session task. The Load Manager creates one DTM process for each session in the workflow. The DTM process performs the following tasks: Reads session information from the repository. Expands the server and session variables and parameters. Creates the session log file. Validates source and target code pages. Verifies connection object permissions. Runs pre-session shell commands, stored procedures and SQL. Creates and runs mapping, reader, writer, and transformation threads to extract, transform, and load data.

Runs post-session stored procedures, SQL, and shell commands. Sends post-session email. The DTM allocates process memory for the session and divides it into buffers. This is also known as buffer memory. The default memory allocation is 12,000,000 bytes. The DTM uses multiple threads to process data. The main DTM thread is called the master thread. The master thread creates and manages other threads. The master thread for a session can create mapping, pre-session, post-session, reader, transformation, and writer threads. Mapping Thread -One thread for each session. Fetches session and mapping information. Compiles the mapping. Cleans up after session execution. Pre- and Post-Session Threads- One thread each to perform pre- and post-session operations. Reader Thread -One thread for each partition for each source pipeline. Reads from sources. Relational sources use relational reader threads, and file sources use file reader threads .

Transformation Thread -One or more transformation threads for each partition. Processes data according to the transformation logic in the mapping. Writer Thread- One thread for each partition, if a target exists in the source pipeline. Writes to targets. Relational targets use relational writer threads, and file targets use file writer threads.

What are indicator files?

informatica: What are indicator files? Ans: .If you use a flat file as a target, you can configure the Informatica Server to create an indicator file for target row type information. For each target row, the indicator file contains a number to indicate whether the row was marked for insert, update, delete, or reject. The Informatica Server names this file target_name.ind and stores it in the same directory as the target file. to configure it – go to INFORMATICA SERVER SETUP-CONFUGRATION TAB-CLICK ON INDICATOR FILE SETTINGS.

informatica: Suppose session is configured with commit interval of 10,000 rows and source has 50,000 rows explain the commit points for Source-based c Suppose session is configured with commit interval of 10,000 rows and source has 50,000 rows explain the commit points for Source-based commit & Target-based commit. Assume appropriate value wherever required. a)For example, a session is configured with target-based commit interval of 10,000. The writer buffers fill every 7,500 rows. When the Informatica Server reaches the commit interval of 10,000, it continues processing data until the writer buffer is filled. The second buffer fills at 15,000 rows, and the Informatica Server issues a commit to the target. If the session completes successfully, the Informatica Server issues commits after 15,000, 22,500, 30,000, and 40,000 rows.

b)The Informatica Server might commit less rows to the target than the number of rows produced by the active source. For example, you have a source-based commit session that passes 10,000 rows through an active source, and 3,000 rows are dropped due to transformation logic. The Informatica Server issues a commit to the target when the 7,000 remaining rows reach the target. The number of rows held in the writer buffers does not affect the commit point for a source-based commit session. For example, you have a source-based commit session that passes 10,000 rows through an active source. When those 10,000 rows reach the targets, the Informatica Server issues a commit. If the session completes successfully, the Informatica Server issues commits after 10,000, 20,000, 30,000, and 40,000 source rows.

How to capture performance statistics of individual transformation in the mapping and explain some important statistics that can be captured? informatica : How to capture performance statistics of individual transformation in the mapping and explain some important statistics that can be captured?

Ans: a)Before using performance details to improve session performance you must do the following: Enable monitoring Increase Load Manager shared memory Understand performance counters . To view performance details in the Workflow Monitor: While the session is running, right-click the session in the Workflow Monitor and choose Properties. Click the Performance tab in the Properties dialog box. Click OK. To view the performance details file: Locate the performance details file.

The Informatica Server names the file session_name.perf, and stores it in the same directory as the session log. If there is no session-specific directory for the session log, the Informatica Server saves the file in the default log files directory. Open the file in any text editor. b) Source Qualifier and Normalizer Transformations. BufferInput_efficiency -Percentage reflecting how seldom the reader waited for a free buffer when passing data to the DTM. BufferOutput_efficiency – Percentage reflecting how seldom the DTM waited for a full buffer of data from the reader.

Target BufferInput_efficiency -Percentage reflecting how seldom the DTM waited for a free buffer when passing data to the writer. BufferOutput_efficiency -Percentage reflecting how seldom the Informatica Server waited for a full buffer of data from the writer. For Source Qualifiers and targets, a high value is considered 80-100 percent. Low is considered 0-20 percent. However, any dramatic difference in a given set of BufferInput_efficiency and BufferOutput_efficiency counters indicates inefficiencies that may benefit from tuning. Posted by Emmanuel at 4:31 PM informatica: Ans: Load manager is the primary Informatica server process. It performs the following tasks: a. Manages sessions and batch scheduling. b. Locks the sessions and reads properties. c. Reads parameter files. d. Expands the server and session variables and parameters. e. Verifies permissions and privileges.

f. Validates sources and targets code pages. g. Creates session log files. h. Creates Data Transformation Manager (DTM) process, which executes the session.

Assume you have access to server.

When you run a session, the Informatica Server writes a message in the session log indicating the cache file name and the transformation name. When a session completes, the Informatica Server typically deletes index and data cache files. However, you may find index and data files in the cache directory under the following circumstances: The session performs incremental aggregation. You configure the Lookup transformation to use a persistent cache. The session does not complete successfully.

Table 21-2 shows the naming convention for cache files that the Informatica Server creates: Table 21-2. Cache File Naming Convention Transformation Type Index File Name Data File Name Aggregator PMAGG*.idx PMAGG*.dat Rank PMAGG*.idx PMAGG*.dat Joiner PMJNR*.idx PMJNR*.dat Lookup PMLKP*.idx PMLKP*.dat If a cache file handles more than 2 GB of data, the Informatica Server creates multiple index and data files. When creating these files, the Informatica Server appends a number to the end of the filename, such as PMAGG*.idx1 and PMAGG*.idx2. The number of index and data files are limited only by the amount of disk space available in the cache directory.

How to achieve referential integrity through Informatica?
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Using the Normalizer transformation, you break out repeated data within a record into separate records. For each new record it creates, the Normalizer transformation generates a unique identifier. You can use this key value to join the normalized records. Also possible in source analyzersource analyzer- table1(pk table)-edit-ports-keytype-select primarykey-. table2(fktable) -edit-ports-keytype-select foreign key -select table name &column name from options situated below.

What is Incremental Aggregation and how it should be used?

If the source changes only incrementally and you can capture changes, you can configure the session to process only those changes. This allows the Informatica Server to update your target incrementally, rather than forcing it to process the entire source and recalculate the same calculations each time you run the session. Therefore, only use incremental aggregation if: Your mapping includes an aggregate function.The source changes only incrementally. You can capture incremental changes. You might do this by filtering source data by timestamp. Before implementing incremental aggregation, consider the following issues: Whether it is appropriate for the session What to do before enabling incremental aggregation

When to reinitialize the aggregate caches Scenario :-Informatica Server and Client are in different machines. You run a session from the server manager by specifying the source and target databases. It displays an error. You are confident that everything is correct. Then why it is displaying the error? The connect strings for source and target databases are not configured on the Workstation conatining the server though they may be on the client m/c.

Have u created parallel sessions How do u create parallel sessions? U can improve performace by creating a concurrent batch to run several sessions in parallel on one informatic server, if u have several independent sessions using separate sources and separate mapping to populate diff targets u can place them in a concurrent batch and run them at the same time , if u have a complex mapping with multiple sources u can separate the mapping into several simpler mappings with separate sources. Similarly if u have session performing a minimal no of transformations on large amounts of data like moving flat files to staging area, u can separate the session into multiple sessions and run them concurrently in a batch cutting the total run time dramatically

What is Data Transformation Manager?

Ans. After the load manager performs validations for the session, it creates the DTM process. The DTM process is the second process associated with the session run. The primary purpose of the DTM process is to create and manage threads that carry out the session tasks. The DTM allocates process memory for the session and divide it into buffers. This is also known as buffer memory. It creates the main thread, which is called the master thread. The master thread creates and manages all other threads. If we partition a session, the DTM creates a set of threads for each partition to allow concurrent processing..

When Informatica server writes messages to the session log it includes thread type and thread ID. Following are the types of threads that DTM creates: • MASTER THREAD – Main thread of the DTM process. Creates and manages all other threads. • MAPPING THREAD – One Thread to Each Session. Fetches Session and Mapping Information. • Pre And Post Session Thread – One Thread Each To Perform Pre And Post Session Operations. • READER THREAD – One Thread for Each Partition for Each Source Pipeline. • WRITER THREAD – One Thread for Each Partition If Target Exist In The Source pipeline Write To The Target. • TRANSFORMATION THREAD – One or More Transformation Thread For Each Partition.

How is the Sequence Generator transformation different from other transformations? informatica : How is the Sequence Generator transformation different from other transformations? Ans: The Sequence Generator is unique among all transformations because we cannot add, edit, or delete its default ports (NEXTVAL and CURRVAL).

Unlike other transformations we cannot override the Sequence Generator transformation properties at the session level. This protecxts the integrity of the sequence values generated.

What are the advantages of Sequence generator? Is it necessary, if so why? informatica : What are the advantages of Sequence generator? Is it necessary, if so why? Ans: We can make a Sequence Generator reusable, and use it in multiple mappings. We might reuse a Sequence Generator when we perform multiple loads to a single target. For example, if we have a large input file that we separate into three sessions running in parallel, we can use a Sequence Generator to generate primary key values. If we use different Sequence Generators, the Informatica Server might accidentally generate duplicate key values. Instead, we can use the same reusable Se

What are the uses of a Sequence Generator transformation?
informatica : What are the uses of a Sequence Generator transformation? Ans: We can perform the following tasks with a Sequence Generator transformation: o Create keys o Replace missing values o Cycle through a sequential range of number

What are connected and unconnected Lookup transformations?
informatica: What are connected and unconnected Lookup transformations? Ans: We can configure a connected Lookup transformation to receive input directly from the mapping pipeline, or we can configure an unconnected Lookup transformation to receive input from the result of an expression in another transformation. An unconnected Lookup transformation exists separate from the pipeline in the mapping. We write an expression using the :LKP reference qualifier to call the lookup within another transformation. A common use for unconnected Lookup transformations is to update slowly changing dimension tables.

What is the difference between connected lookup and unconnected lookup?

informatica : What is the difference between connected lookup and unconnected lookup? Ans: Differences between Connected and Unconnected Lookups:

Connected Lookup Unconnected Lookup Receives input values directly from the pipeline. Receives input values from the result of a :LKP expression in another transformation. We can use a dynamic or static cache We can use a static cache Supports user-defined default values Does not support user-defined default values

What is a Lookup transformation and what are its uses?

informatica : What is a Lookup transformation and what are its uses? Ans: We use a Lookup transformation in our mapping to look up data in a relational table, view or synonym. We can use the Lookup transformation for the following purposes: Get a related value. For example, if our source table includes employee ID, but we want to include the employee name in our target table to make our summary data easier to read. Perform a calculation. Many normalized tables include values used in a calculation, such as gross sales per invoice or sales tax, but not the calculated value (such as net sales). Update slowly changing dimension tables. We can use a Lookup transformation to determine whether records already exist in the target.

What is a lookup table? (KPIT Infotech, Pune)
informatica: What is a lookup table? (KPIT Infotech, Pune) Ans: The lookup table can be a single table, or we can join multiple tables in the same database using a lookup query override. The Informatica Server queries the lookup table or an in-memory cache of the table for all incoming rows into the Lookup transformation. If your mapping includes heterogeneous joins, we can use any of the mapping sources or mapping targets as the lookup table.

Where do you define update strategy?

informatica : Where do you define update strategy? Ans: We can set the Update strategy at two different levels: • Within a session. When you configure a session, you can instruct the Informatica Server to either treat all records in the same way (for example, treat all records as inserts), or use instructions coded into the session mapping to flag records for different database operations. • Within a mapping. Within a mapping, you use the Update Strategy transformation to flag records for insert, delete, update, or reject.

What is Update Strategy?

informatica : What is Update Strategy? When we design our data warehouse, we need to decide what type of information to store in targets. As part of our target table design, we need to determine whether to maintain all the historic data or just the most recent changes. The model we choose constitutes our update strategy, how to handle changes to existing records. Update strategy flags a record for update, insert, delete, or reject. We use this transformation when we want to exert fine control over updates to a target, based on some condition we apply. For example, we might use the Update Strategy transformation to flag all customer records for update when the mailing address has changed, or flag all employee records for reject for people no longer working for the company.

What are the different types of Transformations? (Mascot)

Informatica: What are the different types of Transformations? (Mascot) Ans: a) Aggregator transformation: The Aggregator transformation allows you to perform aggregate calculations, such as averages and sums. The Aggregator transformation is unlike the Expression transformation, in that you can use the Aggregator transformation to perform calculations on groups. The Expression transformation permits you to perform calculations on a row-by-row basis only. (Mascot) b) Expression transformation: You can use the Expression transformations to calculate values in a single row before you write to the target. For example, you might need to adjust employee salaries, concatenate first and last names, or convert strings to numbers. You can use the Expression transformation to perform any non-aggregate calculations.

You can also use the Expression transformation to test conditional statements before you output the results to target tables or other transformations. c) Filter transformation: The Filter transformation provides the means for filtering rows in a mapping. You pass all the rows from a source transformation through the Filter transformation, and then enter a filter condition for the transformation.

All ports in a Filter transformation are input/output, and only rows that meet the condition pass through the Filter transformation. d) Joiner transformation: While a Source Qualifier transformation can join data originating from a common source database, the Joiner transformation joins two related heterogeneous sources residing in different locations or file systems. e) Lookup transformation: Use a Lookup transformation in your mapping to look up data in a relational table, view, or synonym. Import a lookup definition from any relational database to which both the Informatica Client and Server can connect. You can use multiple Lookup

transformations in a mapping. The Informatica Server queries the lookup table based on the lookup ports in the transformation. It compares Lookup transformation port values to lookup table column values based on the lookup condition. Use the result of the lookup to pass to other transformations and the target.

What is a transformation?
informatica: What is a transformation? A transformation is a repository object that generates, modifies, or passes data. You configure logic in a transformation that the Informatica Server uses to transform data. The Designer provides a set of transformations that perform specific functions. For example, an Aggregator transformation performs calculations on groups of data. Each transformation has rules for configuring and connecting in a mapping. For more information about working with a specific transformation, refer to the chapter in this book that discusses that particular transformation. You can create transformations to use once in a mapping, or you can create reusable transformations to use in multiple mappings.

What are the tools provided by Designer?
informatica: What are the tools provided by Designer? Ans: The Designer provides the following tools: • Source Analyzer. Use to import or create source definitions for flat file, XML, Cobol, ERP, and relational sources. • Warehouse Designer. Use to import or create target definitions. • Transformation Developer. Use to create reusable transformations. • Mapplet Designer. Use to create mapplets. • Mapping Designer. Use to create mappings.

What are the different types of Commit intervals?
Informatica: What are the different types of Commit intervals? Ans: The different commit intervals are: • Target-based commit. The Informatica Server commits data based on the number of target rows and the key constraints on the target table. The commit point also depends on the buffer block size and the commit interval. • Source-based commit. The Informatica Server commits data based on the number of source rows. The commit point is the commit interval you configure in the session properties.

What is Event-Based Scheduling?
Informatica: What is Event-Based Scheduling?

Ans: When you use event-based scheduling, the Informatica Server starts a session when it locates the specified indicator file. To use event-based scheduling, you need a shell command, script, or batch file to create an indicator file when all sources are available. The file must be created or sent to a directory local to the Informatica Server. The file can be of any format recognized by the Informatica Server operating system. The Informatica Server deletes the indicator file once the session starts. Use the following syntax to ping the Informatica Server on a UNIX system: pmcmd ping [{user_name | %user_env_var} {password | %password_env_var}] [hostname:]portno Use the following syntax to start a session or batch on a UNIX system: pmcmd start {user_name | %user_env_var} {password | %password_env_var} [hostname:]portno [folder_name:]{session_name | batch_name} [:pf=param_file] session_flag wait_flag Use the following syntax to stop a session or batch on a UNIX system: pmcmd stop {user_name | %user_env_var} {password | %password_env_var} [hostname:]portno[folder_name:]{session_name | batch_name} session_flag Use the following syntax to stop the Informatica Server on a UNIX system: pmcmd stopserver {user_name | %user_env_var} {password | %password_env_var} [hostname:]portno

What are the different types of locks?
Informatica: What are the different types of locks? There are five kinds of locks on repository objects: • Read lock. Created when you open a repository object in a folder for which you do not have write permission. Also created when you open an object with an existing write lock. • Write lock. Created when you create or edit a repository object in a folder for which you have write permission. • Execute lock. Created when you start a session or batch, or when the Informatica Server starts a scheduled session or batch. • Fetch lock. Created when the repository reads information about repository objects from the database. • Save lock. Created when you save information to the repository.

What is Dynamic Data Store?
Informatica: What is Dynamic Data Store? The need to share data is just as pressing as the need to share metadata. Often, several data marts in the same organization need the same information. For example, several data marts may need to read the same product data from operational sources, perform the same profitability calculations, and format this information to make it easy to review. If each data mart reads, transforms, and writes this product data separately, the throughput for the entire organization is lower than it could be. A more efficient approach would be to read, transform, and write the data to one central data store shared by all data marts. Transformation is a processing-intensive task, so performing the profitability calculations once saves time. Therefore, this kind of dynamic data store (DDS) improves throughput at the level of the entire organization, including all data marts. To improve performance further, you might want to capture

incremental changes to sources. For example, rather than reading all the product data each time you update the DDS, you can improve performance by capturing only the inserts, deletes, and updates that have occurred in the PRODUCTS table since the last time you updated the DDS. The DDS has one additional advantage beyond performance: when you move data into the DDS, you can format it in a standard fashion. For example, you can prune sensitive employee data that should not be stored in any data mart. Or you can display date and time values in a standard format. You can perform these and other data cleansing tasks when you move data into the DDS instead of performing them repeatedly in separate data marts.

What are Target definitions?
Informatica: What are Target definitions? Detailed descriptions for database objects, flat files, Cobol files, or XML files to receive transformed data. During a session, the Informatica Server writes the resulting data to session targets. Use the Warehouse Designer tool in the Designer to import or create target definitions.

. What are Source definitions?
informatica: . What are Source definitions? Detailed descriptions of database objects (tables, views, synonyms), flat files, XML files, or Cobol files that provide source data. For example, a source definition might be the complete structure of the EMPLOYEES table, including the table name, column names and datatypes, and any constraints applied to these columns, such as NOT NULL or PRIMARY KEY. Use the Source Analyzer tool in the Designer to import and create source definitions.

What are fact tables and dimension tables?
As mentioned, data in a warehouse comes from the transactions. Fact table in a data warehouse consists of facts and/or measures. The nature of data in a fact table is usually numerical. On the other hand, dimension table in a data warehouse contains fields used to describe the data in fact tables. A dimension table can provide additional and descriptive information (dimension) of the field of a fact table. e.g. If I want to know the number of resources used for a task, my fact table will store the actual measure (of resources) while my Dimension table will store the task and resource details. Hence, the relation between a fact and dimension table is one to many.

When should you create the dynamic data store? Do you need a DDS at all? informatica: When should you create the dynamic data store? Do you need a DDS at all? To decide whether you should create a dynamic data store (DDS), consider the following issues: • How much data do you need to store in the DDS? The one principal advantage of data marts is the selectivity of information included in it. Instead of a copy of everything potentially relevant from the OLTP database and flat files, data marts contain only the information needed to answer specific questions for a specific audience (for example, sales performance data used by the sales division). A dynamic data store is a hybrid of the galactic warehouse and the individual data mart,

since it includes all the data needed for all the data marts it supplies. If the dynamic data store contains nearly as much information as the OLTP source, you might not need the intermediate step of the dynamic data store. However, if the dynamic data store includes substantially less than all the data in the source databases and flat files, you should consider creating a DDS staging area. • • What kind of standards do you need to enforce in your data marts? Creating a DDS is an important technique in enforcing standards. If data marts depend on the DDS for information, you can provide that data in the range and format you want everyone to use.

For example, if you want all data marts to include the same information on customers, you can put all the data needed for this standard customer profile in the DDS. Any data mart that reads customer data from the DDS should include all the information in this profile. • • How often do you update the contents of the DDS? If you plan to frequently update data in data marts, you need to update the contents of the DDS at least as often as you update the individual data marts that the DDS feeds. You may find it easier to read data directly from source databases and flat file systems if it becomes burdensome to update the DDS fast enough to keep up with the needs of individual data marts. Or, if particular data marts need updates significantly faster than others, you can bypass the DDS for these fast update data marts. •

• Is the data in the DDS simply a copy of data from source systems, or do you plan to reformat this information before storing it in the DDS? One advantage of the dynamic data store is that, if you plan on reformatting information in the same fashion for several data marts, you only need to format it once for the dynamic data store. Part of this question is whether you keep the data normalized when you copy it to the DDS. • • How often do you need to join data from different systems? On occasion, you may need to join records queried from different databases or read from different flat file systems. The more frequently you need to perform this type of heterogeneous join, the more advantageous it would be to perform all such joins within the DDS, then make the results available to all data marts that use the DDS as a source.

What is the difference between PowerCenter and PowerMart?
With PowerCenter, you receive all product functionality, including the ability to register multiple servers, share metadata across repositories, and partition data. A PowerCenter license lets you create a single repository that you can configure as a global repository, the core component of a data warehouse. PowerMart includes all features except distributed metadata, multiple registered servers, and data partitioning. Also, the various options available with PowerCenter (such as PowerCenter Integration Server for BW, PowerConnect for IBM DB2, PowerConnect for IBM MQSeries, PowerConnect for SAP R/3, PowerConnect for Siebel, and PowerConnect for PeopleSoft) are not available with PowerMart.

What are Shortcuts?
Informatica: What are Shortcuts? We can create shortcuts to objects in shared folders. Shortcuts provide the easiest way to reuse objects. We use a shortcut as if it were the actual object, and when we make a change to the original object, all shortcuts inherit the change. Shortcuts to folders in the same repository are known as local shortcuts. Shortcuts to the global

repository are called global shortcuts. We use the Designer to create shortcuts.

What are Sessions and Batches?
informatica: What are Sessions and Batches? Sessions and batches store information about how and when the Informatica Server moves data through mappings. You create a session for each mapping you want to run. You can group several sessions together in a batch. Use the Server Manager to create sessions and batches.

What are Reusable transformations?
Informatica: What are Reusable transformations? You can design a transformation to be reused in multiple mappings within a folder, a repository, or a domain. Rather than recreate the same transformation each time, you can make the transformation reusable, then add instances of the transformation to individual mappings. Use the Transformation Developer tool in the Designer to create reusable transformations

What is a metadata?
Designing a data mart involves writing and storing a complex set of instructions. You need to know where to get data (sources), how to change it, and where to write the information (targets). PowerMart and PowerCenter call this set of instructions metadata. Each piece of metadata (for example, the description of a source table in an operational database) can contain comments about it. In summary, Metadata can include information such as mappings describing how to transform source data, sessions indicating when you want the Informatica Server to perform the transformations, and connect strings for sources and targets.