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Tuesday, July 2, 2019

Important stats related SQL queries with details of determining Tablespace



This article on Oracle database provides details about how can we monitor tablespace during sql execution. In this article we will also see how to what are the sqls are currently executing. During this process we will understand, how to get SQL ID associated with each sql statement.

Tablespaces in the Oracle database

The tablespace is one or more logical storage units in the Oracle database. Each database table belongs to some tablespace in the oracle database. Normally, Oracle DBA has alerts setup to monitor tablespace, so that SQL or jobs which are based on SQL statements will not fail due to tablespace issue. However, as a developer, we can also monitor tablespace using the SQL statement below -

SELECT B.TABLESPACE_NAME, TBS_SIZE SIZE_MB, A.FREE_SPACE FREE_MB FROM (SELECT TABLESPACE_NAME, ROUND(SUM(BYTES)/1024/1024 ,2) AS FREE_SPACE FROM DBA_FREE_SPACE GROUP BY TABLESPACE_NAME) A, (SELECT TABLESPACE_NAME, SUM(BYTES)/1024/1024 AS TBS_SIZE FROM DBA_DATA_FILES GROUP BY TABLESPACE_NAME UNION SELECT TABLESPACE_NAME, SUM(BYTES)/1024/1024 TBS_SIZE FROM DBA_TEMP_FILES GROUP BY TABLESPACE_NAME ) B WHERE A.TABLESPACE_NAME(+)=B.TABLESPACE_NAME;

To determine used and free tablespace, use the sql below

select tablespace_name, SUM(bytes_used/1024/1024/1024) "Temp_Used", sum(bytes_free/1024/1024/1024) "Temp_Free"

from v$temp_space_header where tablespace_name like '%TEMP%' group by tablespace_name;


Determine currently running Active DB Sessions

As Oracle developer or application developer, we can use the query below to determine the active DB sessions in the Oracle database -
SELECT * FROM GV$SESSION WHERE STATUS = 'ACTIVE' AND TYPE <> 'BACKGROUND' AND USERNAME = 'TEST_SCHEMA_ID';

This query will return important details e.g. USERNAME, STATUS, database MACHINE, SERVICE_NAME, SQL_ID etc.



Determine which SQL statement is running in Database

Before determining SQL statement, we need to determine what are the active session associated with SQL statement which can be determined with help of above section - 'Determine currently running Active DB Sessions'.

By using the result of above query, get the SQL_ID associated with active session. Once we have SQL_ID then execute the SQL statement below to determine the query which is running in the database -

SELECT * FROM GV$SQL WHERE SQL_ID = '61f38skxkw8hc'

e.g. Here '61f38skxkw8hc' is value for SQL_ID


Get Explain Plan
In order to get explain plan execute the statement below -

select * from TABLE(dbms_xplan.display_awr('61f38skxkw8hc'));


Get Start and End time of SQL Query

select sql_id, first_load_time, last_load_time, elapsed_time, cpu_time from   v$sql where  sql_text like 'with /* slow */ rws as (%';

select sql_id, elapsed_time_delta/executions_delta avg_elapsed
from   sys.dba_hist_sqlstat
where  snap_id = :snap;




Getting SQL Event details

SELECT SNAP_ID, SQL_TIME, SESSION_ID, USER_ID, SQL_ID, SQL_OPNAME,
TO_CHAR(A.SQL_EXEC_START, 'MM/DD/YYYY HH24:MI:SS'), SESSION_STATE,
EVENT, TIME_WAITED, PROGRAM, A.MACHINE FROM DBA_HIST_ACTIVE_SESS_HISTORY A
WHERE
A.SQL_TIME BETWEEN TO_DATE('03/10/2018 09:00:00', 'MM/DD/YYYY HH24:MI:SS')
AND TO_DATE('03/11/2018 6:00:00', 'MM/DD/YYYY HH24:MI:SS')
AND SQL_ID = 'XXX'
AND EVENT IS NOT NULL
AND EVENT <> 'CELL SINGLE BLOCK PHYSICAL READ'
ORDER BY SQL_TIME ASC

Monday, May 20, 2019

Overview of Informatica Customer 360i




Would you like to know more about what is Informatica Customer 360i? Are you also interested in knowing what are capabilities of the Informatica Customer 360i application? If so, then refer this article. This article also provides highlights on the underlying architecture of Informatica Customer 360i .


What is Informatica Customer 360i 



Informatica acquired AllSight company which is Artificial Intelligence enabled customer insight company on Feb 28, 2019. AllSight Inc a startup had a product named AllSight Intelligent 360. After-acquired by Informatica it called now as Informatica Customer 360 Insight (Customer 360i). It is powered by CLAIRE engine (Cloud-scale AI-powered Real-time Engine). CLAIRE uses artificial intelligence (AI) and machine-learning techniques powered by enterprise-wide data and metadata. It helps to significantly boost the productivity of all managers and users of data across the organization.

Capabilities of Informatica Customer 360i




1. It connects data of any type
2. It has capabilities to manage billions of records across all data sources
3. The customer data linkages can be easily resolved
4. With the help of Customer 360i, we can create relationships using advanced machine learning algorithms
5. Using Natural Language Processing we can provide additional customer attributes from unstructured data.
6. The relationships, households and complex B2B hierarchies using a graph data store can be easily visualized with product
7. It has capabilities to present multiple perspectives of the customer based on unique users and use case context




The architecture of Informatica Customer 360i

1. Customer 360 Insights is built on a big data technology stack.
2. The technologies used are Spark, Apache Hadoop, In-memory data stores, Graph, Columnar.
3. Data scientists can use R and Python languages with Informatica Customer 360i for flexibility.
4. It uses the microservices architecture to achieve scalability for deployment and redeployment of functionality
5. It also uses the SaaS deployment model which helps to simplify as well as accelerates implementation.

Use of Informatica Customer 360i

1. Informatica Customer 360i can be used for customer engagement
2. It works on structured and unstructured data sources
3. It will help enterprises to create the relationship between master, transaction, interaction, and reference data
4. These relationships will help to discover rich, personalized behavioral insights.


5. These insights can be used across the enterprise to connect customer interactions in real time and ensure the delivery of the next best action.
6. This new solution automates and simplifies profile and relationship unification
7. It also scales AI across transactions and interactions in the business data.




Customer Intelegence evolution

Application Centric
 1. Fix data quality
 2. De-duplication in the business data

Master Data Driven
 1. Resolve duplicate records from multiple store
 2. Manages master data
 3. Fix data quality in the enterprise system data


Customer Intelligence Empowered
 1. Match customer entities
 2. Enrich data with derived intelligence
 3. Provide multiple unique customer views

Sunday, May 19, 2019

Details about Informatica MDM metadata or infrastructure tables




You might have come across the term metadata tables in infrastructure tables during your Informatica MDM project implementation. What are these infrastructure tables? What is the significance of these tables? How can we access it and use it? Are you facing these questions and would like to know more about these? If so, then you reached the right place. In this article, we will explore the infrastructure tables get generated during the Base Object, Stage and Landing tables configuration. So let's start.

Introduction:

The MDM infrastructure tables are the core part of Informatica MDM. These tables are created, whenever we configure the basic tables such as Base Object (BO), Stage and Landing tables along with their properties such as Raw Retention, Delta detection on the Stage table or match and merge setting on the Base Object table.




What are the MDM infrastructure tables?

Assume that we create Landing table, Stage table, and Base Object table as C_L_PARTY, C_S_SALES_PARTY, and C_B_PARTY respectively. Also assume that we configure raw retention, delta detection, tokenization, match and merge rule as well. After doing all these configurations at table level the supporting tables are created.

  1. Tables at Landing table level: There is no infrastructure table created at the landing table level
  2. Tables at Staging table level: The tables created at the Staging table level are 
  • C_S_SALES_PARTY_RAW
  • C_S_SALES_PARTY_PRL
  • C_S_SALES_PARTY_OPL
  • C_S_SALES_PARTY_REJ
    Each of these tables has its own importance and are used during MDM batch job execution.
           3. Tables at Base Object table level: There are 14 supporting infrastructure tables are created.
  • C_B_PARTY_MTCH
  • C_B_PARTY_HIST
  • C_B_PARTY_XREF
  • C_B_PARTY_HXRF
  • C_B_PARTY_DRTY
  • C_B_PARTY_CTL
  • C_B_PARTY_HMRG
  • C_B_PARTY_HCTL
  • C_B_PARTY_EMI
  • C_B_PARTY_EMO
  • C_B_PARTY_VXR
  • C_B_PARTY_HVXR
  • C_B_PARTY_VCT
  • C_B_PARTY_STRP  

What is the need of the MDM infrastructure tables?

The Informatica MDM implementation involves various process such as Stage, Load, Tokenization, Match, Merge, etc. During each process, the data is transferred from the source table to the target table. During this transfer data is manipulated with the help of supporting table. e.g. During the stage job, the data is transferred from the landing tables to Staging tables. During this transfer, the landing data is maintained in _PRL, _RAW tables. The _PRL table data is used to determine delta of the source record which is subprocess during stage job. 

Similar cases are involved during load job as well tokenization job. These infrastructure tables play a vital role in Informatica MDM implementation.




Relationship between Landing table and the Base Object table

  • The load job loads data from the Stage table to a Base Object
  • There is still the dependency on landing table data to handle the rejection. 
  • The batch job will try to pull the source table record for inserting into the reject table.
  • If the landing table is missing the corresponding records, then the reject table will have an entry to state that the source table entry not found. 
  • If the landing table is huge and performance issues occur in the load job during the rejection handling, then assess the environment to add a custom index on the landing table.

Is it ok to modify the existing structure of the MDM infrastructure tables?

Informatica strongly recommends that do not modify the structure of these tables as these designed for internal processing purpose only. If you modify these tables, metadata validation may complain error.

The video below provides detailed information about the MDM infrastructure tables -


Friday, February 22, 2019

Informatica Master Data Management (MDM) Architecture Overview



Are you looking for information about Informatica Master Data Management Architecture? Would you be also interested in knowing what components involved? If so, then you reached the right place, in this article we will explore Informatica MDM Architecture in detail. We will also understand what upstream and downstream systems are involved.

MDM Architecture Overview



As we know, Master Data Management i.e. MDM is a solution for mastering business information. MDM involves several processes with the help of which we can achieve uniformity, accuracy, and consistency in the business data. Such business-critical data can be used for better process management and for achieving the organization's goals. With the help of the MDM solution, we can carry out the data governance practices very effectively.

If we look at the big picture of MDM architecture, we can see, there are basically three layers. The first layer is the source systems, the second layer is MDM implementation and the third layer is consumption.



The source system layer includes operational systems which maintain 3rd party data. This layer may include multiple sources with different platforms such as Siebel, Oracle, SAP, Acxiom or D and B. The data from source systems will not be pushed to MDM layer directly. In order to push data from source system to MDM layer, we normally use ETL layer (Here ETL stands for Extract, Transport, and Load). This data push may happen in the batch mode or real-time mode or near real-time mode.

Once data is entered to MDM landing tables, first data cleansing will happen. Data standardization rules will be applied to enrich the business data. Cleansed and standardized data will be loaded into staging tables. To achieve data integrity constraints are enforced to the Base Object table while loading data from staging table to base object table. This is not the end of the process. Actual processing work will start after this. Even though the cleansed and standardized records are loaded in the MDM system, there will be duplicate and fuzzy records in the system. The next processes i.e. match process will identify such records based on the business criteria and rules developed during the data quality analysis phase. These duplicate records will be consolidated to make a golden copy of records. The golden copies of records may hold relations among them e.g. Manager and Employee relationships or Organization and Branch relationships etc. These relationships can also be maintained in the MDM system in the form of hierarchies.
The data stewardship will help to keep a golden copy of records in its consistency state and enforce controls on create and update processes through the user interface which comes with Informatica MDM product. e.g. Informatica Data Director or Customer 360 application.

All these MDM features such data modeling, data quality, identifying duplicates, consolidating records, maintaining hierarchies and workflows will not be compromised over the security, hence MDM also comes with role based in build security. However, if it is required, we can integrate the organization's existing security features such as LDAP security for authentication. However, authorization of MDM components needs to be happening in the MDM hub as per role-based mechanism. One of the great thing about Informatica MDM is it keeps MDM configurations in synch with the help of metadata.

Okay, we created golden of records in the MDM hub. What we do with this data? Thanks for asking that question, actually, after the successful implementation of MDM solution, the golden copies of records will be available to the consumer to consume. There could be a third party application which can consume data directly from MDM. However, in most of the cases, the data will be pushed from MDM to these consuming system through ETL layer as like data loading from source systems to MDM. It could be the batch mode, real-time or near real-time. There are few other types of systems such as analytical or reporting systems which consumes data for different purposes. The analytical consuming systems such as Data warehouse, Data marts or Portal dashboard will use these golden records to analyze the data and comes better organization growth plans. On the other hand, reporting consuming systems such as business intelligence or corporate performance management will help to produce the report to achieve effectiveness in business processes and to achieve business goals.



MDM Architecture - Deep Dive



We got a basic idea where Informatica MDM fits in the enterprise application. Now is the time to deep dive into the MDM system architecture. Informatica MDM three major components and those are Hub store, MDM hub and Services Integration Framework.

The Hub Store is a database component where business data is stored and consolidated. Hub store is based on underlyng database which can be Microsoft SQL Server, Oracle or DB2. It contains information about all of the databases that are part of your Informatica MDM Hub implementation. It has two parts, one is Master Database and second is Operational Reference Store, also known as ORS. We will use ORS term quite often during  our lectures as well as during real time MDM implementation.

What is this Master Database component?  Master Database is a database schema which maintains most critical configuration details of Informatica MDM hub. It includes user accounts created using MDM hub users section. Security configuration such as username and encrypted passwords for database schema users and application users. Master data maintains registry for Operational reference Store. e.g. If you register 3 ORS then those 3 entries will be present in Master Database. The default name of the Master Database is CMX_SYSTEM. Normal practice we use term CMX_SYSTEM quite often as like ORS.


We know Registering Database, Creating Users, providing tool access, overview of message queue and security provider. Where these configurations are maintained? Yes, you are right, this information is also persisted in the master database. The Master Database stores the connection settings and properties for each Operational Reference Store which are registered through MDM hub. In other words, we can access and manage multiple Operational Reference Stores from one Master Database.

Important thing to remember is for a given Informatica MDM Hub environment we can have only one Master Database. i.e. only one CMX_SYSTEM for one MDM environment.

Okay, if master database maintains configuration details then where business data is stored? I am glad you asked that question. The answer is, business data is stored in Operational reference store. Lets understand what else Operational Reference Store contains. Along with business data, ORS also maintains the rules for processing the master data. If you remember, about match columns, matchrule sets, all such rules are stored in the ORS. It also stores additional information such as BVT, Tokens, data leaniage along with history. It has Repository tables which start with C_REPOS and Repository archive table which starts with C_REPAR which holds all this information. Do you want to know what is default name of ORS? It is CMX_ORS, but you can name whatever you like because at the end it is database schema name. Unlike Master Database there is no restriction on number of ORS in a given MDM hub environment. But if we configure more ORS in the MDM hub it will adversely impact your MDM env, so use it wisely.

Important thing to remember about ORS is, we cannot associated a single Operational Reference Store with multiple Master Databases. The Master Database also stores site-level information, such as the number of incorrect log-in attempts allowed before a user account is locked out.

Next important component in the MDM architecture is application server. Informatica MDM supports three application servers and those are JBOSS, Weblogic and Webshphere. Irrespective of what kind of application server you are using, the components which get installed on these servers will remain same. We normally install Process Server and Hub Server on the application server. Lets understand little more about Process server. It is java code (to be specific a Java servlet) that cleanses the data and also processes batch jobs. Prior to MDM 9.7, we used to call it as cleanse server instead of process server. Why? because only cleansing used happen on cleanse server. But now, both data cleansing and batch job processing happens on Process server and hence the name. We can configure mutliple process server for better performance. Apart from that, we can configure configure process server in 3 different modes and those are Batch mode, online mode and the Batch and Online mode. We can choose the mode as per our business need. On other hand, Hub Server is used for core and common services which includes security, access and session management.



Monday, January 28, 2019

Important Informatica MDM Interview Questions and Answers - Part IV



Are you looking for the Informatica MDM interview questions and answers? Are you also looking for an explanation for various concepts in MDM? If yes, then refer to this article where we have explained various MDM concepts in the form of interview questions and answers. This article will be helpful for the Informatica MDM interview. In this article, we will focus on questions and answers about Cleanse function in the Informatica MDM.

Q1: What is the use of cleanse function in MDM?

Answer: Informatica MDM hub is used for data enrichment and consolidation. In order to perform data enrichment, it has to go through cleansing and standardization. Cleansing is a process through source data is cleansed for nuisance characters or words, invalid data or repeated words. Cleansing also helps to achieve standardization e.g. converting Limited, Ltd.,  Lmtd,  Lt to standard work LTD etc.
In Informatica MDM hub, cleansing is achieved during stage process while moving data from the landing table to the staging table. We need to install and configure cleanse engine before running stage job.



Q2: What are the cleanse functions you have used your projects so are?

Answer: This is one of the common questions get asked during the Informatica MDM interview. 
  1. In order to achieve data cleansing we normally use inbuild cleanse functions such as Concatenation,  Trim, Uppercase, regular expression.
  2. For complex operation where IF-ELSE conditions need to be handled then we use graph function. We also use Cleanse List function to achieve cleansing and standardization.
  3. There are several scenarios where inbuild cleanse function do not satisfy the business requirement in such case we build the custom Java Cleanse function. e.g. determining the length of String, Determine the index of the character in the given String.
The video below explains how to develop custom Java cleanse function.



Q3: How to read the database using cleanse function?

Answer: Read database function is used to perform the lookup and get values from the database table. While using read database cleanse function we need to connect to the database by passing table name and column name on which we need to perform the lookup. 

Normally, Read database cleanse function is used if we need to populate values in staging table by reading database table.




Q4: What is the Graph Cleanse function and how to create it?

Answer: MDM hub comes with various types of inbuilt cleanse function such as Data Conversion, General Processing, Geographic, Logic Functions, Math Functions, Misc Functions, Noise Functions, and String Functions. However, there are some business scenarios where these inbuilt functions do not meet the requirements. 

          In such cases, we can combine inbuilt cleanse function and create our own cleanse function. In order to create such a function, we need to use the Graph Cleanse Function. Using Graph Cleanse function we can achieve IF-ELSE or CASE statement scenarios. 


Q5: Have you created a custom Java Cleanse function? If yes, what was use case?

Answer: Informatica MDM hub comes with inbuilt cleanse function. We can build custom complex function by combining these inbuilt functions to cleanse and standardize the business data. There are some business cases where inbuilt cleanse function or custom complex cleanse function does not satisfy business needs.  In such cases, we need to create custom Java Cleanse functions. Informatica MDM provides Java framework to create custom Java cleanse function.

Business use case: Determining the geocode of the given address.
Assume that your business would like to determine the geocode of the given physical address. We have two options here:
a) Buy address doctor license from Informatica and populate co-ordinates for address
b) Build custom logic using Google Geocoding API (Free) with no extra license money

If we choose option b) where we no need to pay for determining Geocoding of address. In order to implement such custom logic, we need to write custom Java cleanse function.

The video below provides a detailed explanation about how to build custom Java Cleanse function.






Thursday, December 27, 2018

Important Informatica MDM Interview Questions and Answers - Part III




Are you preparing for Informatica MDM interview? And are you looking for the interview questions and answers about Informatica MDM? If yes, then refer to this article. In this article, we are going discuss various questions and their answers which are normally asked in Informatica MDM interview. You can also refer the previous article - Important Informatica MDM Interview Questions and Answers - Part II

Q 1: Suppose you are running stage job with delta detection enabled. While running stage job delta detection is successful but a stage job failed to insert the records in Stage table. How do you handle this issue?

Answer
This is scenario based question which can be asked by the interviewer to check knowledge of the candidate.
In the case of full data load if stage job failed to process records then we can handle this situation in two ways-
1. Truncate PRL and reload:

  • When we run stage job, the records from landing table get compared with _PRL table and delta is determined. 
  • If we re-run stage job after its failure then no delta will be determined as the _PRL table will be same as the landing table. 
  • To fix this we can truncate PRL table and re-run stage job. 
  • There will be more time required to run stage job as it is going to process whole data set. 
  • Only delta records will be updated or inserted as part of the load job.
2. Populate PRL table using _RAW table:

  • If we have enabled RAW retention then this approach will be an efficient approach.
  • First, we need to determine JOB_ROWID for the previous run using C_REPOS_JOB_CONTROL table.
  • Using JOB_ROWID we can pull all records from the _RAW table and insert into the _PRL table.
  • We need to re-run -stage job to process delta records.

The video below provides more insights about the stage and load jobs in Informatica MDM


Q 2: When PRL, OPL, RAW and REJ tables are created?

Answer:
When we configure the landing and staging tables the next is to create the mapping. Once mappings are created then Raw retention and delta detections properties get enabled. The mentioned below are the instances during which PRL, OPL, RAW and REJ tables are created.
a. _REJ table get created when we create the staging table
b. When we configure the staging table for Raw Retention, the _RAW table associated with the staging table is created.  
c. The _PRL and _OPL tables are created when we configure delta detection for the staging tables.




Q 3: What are the causes of record rejection?

Answer:
The _REJ table is associated with the staging table. e.g. If the staging table name is C_STG_CRM_PARTY then associated reject table name will be C_STG_CRM_PARTY_REJ.


Reason for Reject table creation:
1. The reject table is created to store rejected records during the stage job and the load job.
2. To increases performance by rejecting a record when it first encounters a reason to reject the record

Note: If there is more than one reason to reject a record, the reject table describes the first reason that  encounters.

There are several causes for the record to reject during MDM processes. The main reasons or causes for record rejections are as follows:

  • The value of PKEY_SRC_OBJECT column is null 
  • The duplicate value in PKEY_SRC_OBJECT column. One one record is processed successfully (One with highest SRC_ROWID). The other duplicate record/records are rejected
  • The value in the LAST_UPDATE_DATE column contains a future date or null date.
  • The value in the LAST_UPDATE_DATE column is less than 1900.
  • The unique column contains duplicate values.
  • The column HUB_STATE_IND contains values other than 1, -1, 0
  • The column contains invalid referential integrity value.



Q 4: When PRL, REJ, STG and RAW table get cleared/truncated?

Answer:
This is another interesting question interviewer may ask to check how extensive candidate has worked with Informatica MDM tool.

Not all the system tables in the Informatica MDM are truncated. Some of the system tables are truncated during specific processes.
a. The _PRL table gets truncated during each stage job run
b. The _REJ table never gets truncated during stage or load job. However, we can manually truncate it or we can use Clean  SIF API on Base Object table to clean or truncate REJ table.
c. The _STG table is truncated during each stage job
d. The _RAW table never gets truncated during stage or load job. However, if the retention period is complete then the unique records are kept in the _RAW table from stage job prior to the retention period. The remaining records are deleted from the _RAW table. The _RAW table also get truncated when we call Clean SIF API on Base Object Table.

Read More: Learn more about how to handle rejected records.

Q 5: Have you used any data quality tool along with Informatica MDM such as Informatica Data Quality?

Answer:
In some projects, Data Quality tools are used. It is not mandatory to have knowledge or work experience in Data Quality tool. However, having knowledge about Data Quality tool will make your career profile strong.

So if you have Data Quality experience then mention about it. e.g. You can mention that you used Data Quality to perform data analysis and come with data standardization rules for Party and Address data.




You can refer the video below learn more about Informatica Data Quality





Sunday, December 23, 2018

Important Informatica MDM Interview Questions and Answers - Part II

In this article, we will focus on interview questions related to Informatica MDM stage table and delta detection process. Are you interested to also know interview questions and answers about Hard Delete detection process? If yes, then refer to this article where we provide detailed questions and answers about Informatica MDM. Here is the link for Important Informatica MDM Interview Questions and Answers - Part I, in case you have not gone through it already.


Q 1: Where do you configure Audit Trail?

Answer:

The audit trail is used to maintain the history of source data. The history of the source data can be maintained for the specific number of runs or the specific number of job runs. The audit trail is configured at the Stage table level. Audit trail option gets enabled when we create the mapping between landing and staging table. Once Audit trail is configured _RAW table associated with the Stage is get created.

Read more: Click here to read more about Audit Trail and Delta Detection



Q 2: What is Hard Delete Detection?

Answer:
The hard delete detection (HDD) is used to determine records physically deleted from the source. There are two types of Hard Delete Detection in Informatica MDM -
a) Direct Delete
b) Consensus Delete

The details about how to configure Hard Delete Detection in Informatica MDM is explained in the video below -



Q 3: What is delta detection? How to enable delta detection?

Answer:
The delta detection is used determine new inserts and update in existing source record for full data load process. The delta detection happens for the specific column which we can configure at the Stage table level. The delta detection option gets enabled when we create the mapping between landing and staging table. In order to achieve delta detection data from the landing is compared with the _PRL table which is created at the time of delta detection configuration.

In the figure below, we can see data changes on day 1

The second figure below provides states of records in each landing, staging and PRL table due to delta detection process -





Q 4: What is the full data load and incremental data load?

Answer:
A) Full data load: In this case, the source sends full data files every day to load data in MDM. The new inserts and update to existing records will be determined in MDM as part of the delta detection process.

B) Incremental data load: In this case, the incremental file from source is loaded in MDM landing tables every day. The new inserts and update to existing records will be determined outside the MDM process. The MDM delta detection is not required.

Q 5: How to use delta detection with incremental data load?

Answer:
This is tricky question interviewer might ask in order to check whether the candidate really has real-time experience.

The answer to this question is - The delta detection only works with full data load and not with incremental data load.

You can learn more about Informatica MDM here.





                                              






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