ETL as if it’s one monolithic thing. Actually, ETL jobs vary considerably and involve numerous enabling technologies. In the context of Hadoop, two broad categories of ETL workloads are relevant: those that require substantial relational technologies and those that don’t.
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At one extreme, many ETL jobs join three or more tables, execute complex SQL routines of hundreds of lines, create temporary tables, or involve multi-pass SQL. These relational ETL jobs are often developed and executed with a mature ETL tool, and the tool may push relational processing into a relational database management system (DBMS). This is usually called “ETL push down” or “ELT.” In these cases, the T (i.e., data transformation) occurs in a relational database or similar data platform instead of on the ETL tool hub.
For heavily relational jobs, Hadoop is an unlikely candidate because ANSI standard SQL and other complex relational technologies are not fully supported on Hadoop today. Even so, Hadoop is improving rapidly, and third-party tools are emerging to provide a relational front-end for Hadoop, so it’s probable that Hadoop’s relational capabilities will soon be more compelling for heavily relational and SQL-based processing.
At the other extreme, some ETL jobs simply need basic relational capabilities (as seen in an HBase row store or a Hive table) or no relational capabilities at all (as is typical of the algorithmic approach of most hand-coded MapReduce jobs). For example, some early adaptors of Hadoop have migrated operational data stores to Hadoop, which manage customer masters, archives of transactions, or industry specific data (such as call detail records in telco or supply chain documents in retail and manufacturing).
ETL jobs that make simple aggregations, summations, and calculated values (but at massive scale against millions of records) are well-suited to the Hadoop environment, and these jobs can be developed for a fraction of the cost of a high-end ETL tool – if you have the appropriate in-house programming skills. Let’s not forget that Hadoop originated in Internet firms, where it did simple but massive summations of clicks, page views, and ecommerce transactions. For workloads resembling those, Hadoop continues to be a compelling and cost-effective platform
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Let me talk about Power Center vs Cloud (IICS), as rest of your future architecture is same except these two technologies. Each of these technologies have their own pros and cons
You may have already know most of the capability which PowerCenter offers, one of the main reason to consider moving to PowerCenter is maintenance, you should have a dedicated Administrators to install the product and do the maintenance work on a daily basis., you should use your own infrastructure , database software’s , file storage etc. And you should also plan for upgrades whenever Informatica announces end of life for a certain version.
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Depending on your environment, you may have jobs which process data form Real time loads, Mainframe CDC jobs , Web services , unstructured data processing, flat files and Relational tables, which may slow your environment and some times even after spending months and months with Informatica support you may not get any resolution for the slowness, you need to then plan your scheduling based on the Jobs which use high CPU I/O and during off peaks hours etc.
You also need to procure license based on the edition, irrespective of whether you use a certain feature
You can also enable versioning, to revert to the previous versions in case of any issues during deployment
The major thing to consider when considering cloud is you will get rid of daily maintenance, server patches, upgrades etc. But the real story starts now, there is lot of limitation in terms of development example
• You can’t use any tables in SQL transformation
• You can’t use Lookup transformation if the column name has space
• When connecting ports in web service consumer, if you need to pass a default value to 10 columns, you need to create 10 columns with same default ports and then only connect to 10 ports
• You can’t run your jobs on the cloud, if your web service requests exceed a certain threshold (less than 1 MB, I have to check on the exact number)
• If you change the port name which is used in a variable or transformation, the whole mapping will be invalidated.Learn more from Informatica training
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Transaction Control is an active and related transformation that makes it possible for us to commit or rollback transactions during mapping execution. Commit and rollback practices are of major significance for data availability. Here, we will look after how transaction control transformation takes place in Informatica.
Data integration in Informatica
You can use Data integration in the Informatica Power center. It includes the ability to link and extract data from multiple heterogeneous data sources and process data.
For example, both the SQL Server Database and the Oracle Database can link and the data can integrate it into a third system.
The new available version of Informatica PowerCenter is 9.6.0. For PowerCenter, there are multiple editions available.
Popular clients are using Informatica Power Center as data integration. IBM DataStage, Oracle OWB, Microsoft SSIS, and Ab Initio are common tools available on the market to compete against Informatica.
Transformation of Informatica Transaction Management.
There will be a scenario where committing the data to the target when they handle a high amount of data. Commit and rollback operations are of great significance as they ensure data access. Transformation of transaction management enables one to commit or rollback transactions during the mapping execution.
When a commit executes it too soon, it would be an overhead for the system. When a commit is complete too late, there are risks of data loss in the event of failure.
A transaction is the set of rows that the commit or rollback rows are bound by. Based on a varying number of input rows, you may describe a transaction. Based on a set of rows organized on a common key, such as employee ID or order entry date, you might want to identify transactions. The PowerCenter from Informatica helps you to monitor commit and rollback transactions based on a series of rows going through a transformation of Transaction Control.
Configuring the Transformation to Informatica Transaction Management
In the transaction control transformation, you can configure the following components:
The transformation can rename and a summary adds.
You can build ports for input/output
You should describe the expression of transaction control and the degree of tracing.
Metadata Extensions Tab:
You can add Metadata details.
Expression of Informatica Transaction Power
In the Transaction Control State option on the Assets tab, you can enter the transaction control expression. To evaluate each row against the condition, the transaction control statement uses the IIF function. Using the notation that follows with an expression.
The IIF (condition, value1, value2)
IIF(dept id=10, TC-COMMIT BEFORE, TC ROLLBACK BEFORE)
Transaction control transfers it to provide flexibility. In this transformation, five built-in variables are available to manage the operation.
**TC CONTINUE TRANSACTION **
For this line, the Integration Service does not make any transaction adjustments. This is the expression’s default meaning.
**TC COMMIT BEFORE **
The Integration Service commits the transaction, starts a new transaction, and writes the current row to the target. The existing line locates it in the latest deal.
The Integration Service writes the current row to the target, commits the transaction, and starts a new transaction. The current row includes it in the transaction committed.
**TC ROLLBACK BEFORE **
Rollback the current transaction from the Integration Service, launch a new transaction, and write the current row to the target. In the latest deal, the existing line is placed.
TC ROLLBACK AFTER
The Integration Service writes to the target the current line, rolls the transaction back, and starts a new transaction. In the rolled-back transaction, this is the existing line.
Guidelines and Evaluation for Mapping
When you build a map with a transaction control transformation, use the following rules and guidelines.
If the mapping contains an XML target, and you want to add or construct a new commit text, data must receive it from the same transaction control point by the input classes.
For such targets, transaction control transformations linked to any target other than relational, XML, or dynamic MQSeries targets are unsuccessful.
You must relate each target instance to a transformation of Transaction Power.
You can relate several goals to a single transformation of Transaction Power.
Just one active Transaction Control transition may link it to a goal.
In a pipeline branch that begins with a Sequence Generator transformation, you can not put a Transaction Control transformation.
A rolled-back transaction could result in unsynchronized target data if you use a dynamic Lookup transformation and a Transaction Control transformation within the same mapping.
Transformation of a Transaction Control can be successful for one goal and inefficient for another. If each target is related to an efficient transformation of Transaction Control, the mapping is true.
A successful transformation of Transaction Control can be related to either all the targets or none of the targets in the mapping.
To identify requirements to commit and rollback transactions from transactional targets, use the Transaction Control transition. Relational, XML, and dynamic MQSeries targets provide transactional targets.
Informatica Transaction control
The transaction control transition expression includes values that reflect acts that are done by the Integration Service based on a condition’s return value. On a row-by-row basis, the Integration Provider analyses the case. The return value defines when the Integration Provider commits, rolls back, or does not adjust a row for any transaction. When a commit or rollback is provided by the Integration Provider depending on the expression’s return value, a new transaction begins.
You configure it for the user-defined commit when you configure a session. The Integration Service commits and rolls back transactions during a user-defined commit session based on a row or group of rows going through a transition of Transaction Power. For each row that enters the transition, the Integration Service evaluates the transaction control expression. The transaction control expression’s return value determines the commit or rollback point.
Originally, the mappings create it using PowerCenter Designer. If you use PowerCenter Express (PCX), it is not possible to import all mappings because PCX only contains the Informatica Developer tool and not the PowerCenter Designer tool.
The artifacts are based on the code page ‘UTF-8.’ XML can need to edit it if you have modified the file tab.
You will describe the transaction at the following levels in the Informatica power center.
To identify the transactions, use a transaction control transformation.
In the Session Properties tab, you can define the “Commit type” option. The multiple ‘Commit Type’ options are Goal, Source and User Defined. The “Commit Type” will only be “User Defined” if you have used the transaction control transformation in the mapping.
The integration service calculates the expression for each row in the transaction control transition while you are running a session. When the expression evaluates it as commit, it commits all rows in the contract to the destination (s). Also, when the integration service calculates the expression as a rollback, all the rows in the transaction roll it back from the target (s).
If you have a flat file as the target, so each time the transaction is committed, the integration service generates an output file. You can label the target flat files dynamically. Look at the example of dynamically generating flat files: Dynamic creation of flat files.
There will be a condition when committing the data to the target when handling a high amount of data. When a commit executes it too soon, it would be an overhead for the system. When a commit completes it too late, there are risks of data loss in the event of failure. Transaction control transfer provides it to provide flexibility. Hence, learn further in this regard through online informatica training
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Data cleansing and standardization is an important aspect of any Master Data Management (MDM) project. Informatica MDM Multi-Domain Edition (MDE) provides reasonable number of cleanse functions out-of-the-box. However, there are requirements when the OOTB cleanse functions are not enough and there is a need for comprehensive functions to achieve data cleansing and standardization, for e.g. address validation, sequence generation. Informatica Data Quality (IDQ) provides an extensive array of cleansing and standardization options. IDQ can easily be used along with Informatica MDM.
This blog post describes the various options to integrate Informatica MDM and IDQ, explains the advantages and disadvantages of each approach to aid in deciding the optimal approach based on the requirements.
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Informatica MDM-IDQ Integration Options
There are three options through which IDQ can be integrated with Informatica MDM.
Option 1: Informatica Platform Staging
Starting with Informatica MDM’s Multi-Domain Edition (MDE) version 10.x, Informatica has introduced a new feature called “Informatica Platform Staging” within MDM to integrate with IDQ (Developer Tool). This feature enables to directly stage/cleanse data using IDQ mappings to MDM’s Stage tables bypassing Landing tables.
Stage tables are immediately available to use in the Developer tool after synchronization eliminating the need to manually create physical data objects.
Changes to the synchronized structures are reflected into the Developer tool automatically.
Enables loading data into Informatica MDM’s staging tables bypassing the landing tables.
I hope you reach a conclusion about Data Warehousing in Informatica. You can learn more about Informatica from online Informatica online course
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