Informatica Training in New York City

Data Warehousing is defined as a process of building and using a data warehouse. Data Warehouse is considered as a core component of Business Intelligence. The warehouse is built by integrating data from multiple sources that mainly support ad hoc queries, decision making, and analytical reporting. Data warehousing is mainly used for analytical purposes and it stores data typically by integrating different copies of transaction data from different sources. 

Topics Covered in this module are:

• Introduction to Data Warehouse
• Data Warehouse Architecture
• Schemas
• OLAP
• Process Managers
• Tuning
• Data Marting
• Key features of Data Warehouse

Informatica is one of the top software companies which offers data integration products. Informatica is a data integration tool that is based on the ETL Architecture.  This Informatica tool is used to extract data from all the systems and then transfer the same to the server and finally load it to the data warehouse. The Informatica do offer products for data masking, data replica, ETL, data virtualization, data quality, etc. 

Topics Covered in this module are:

• Introduction to Informatica
• Data Integration 
• Data Extraction
• Data Transformation
• Data Cleansing
• Data Merging
• Data Loading
• Need for Informatica

Informatica is a data integration tool that is based on the ETL Architecture. The Informatica do offer products for data masking, data replica, ETL, data virtualization, data quality, etc. This Informatica tool is used to extract data from all the systems and then transfer the same to the server and finally load it to the data warehouse. It has a unique property to fetch, connect, and process the data from different types of resources. In this module, you get a clear understanding of the Informatica step by the step installation procedure. 

Topics Covered in this module are:

• What is Informatica 
• Different versions of Informatica
• Informatica Installation procedure

Informatica is a data integration tool that is based on the ETL Architecture. This Informatica tool is used to extract data from all the systems and then transfer the same to the server and finally load it to the data warehouse. Informatica Architecture is defined as a Service-Oriented Architecture. The SOA is basically defined as a group of services that communicate with each other. In a simple way, it can be defined as a process of data transfer during the period of communication. Informatica Administrative Unit is the key fundamental present in the Informatica Domain. In this section, you will get to know in detail about each component and service in the Informatica Architecture.

Topics Covered in this module are:

• Integration Service
• Repository Service
• Administrator Console
• Workflow Manager
• Repository Manager
• Mapping Designer
• Nodes
• Monitoring Statistics
• Creating Mappings
• Monitor tasks and workflows
• PowerCenter Repository

Informatica PowerCenter is the most prominently used ETL tool to load, extract, and transform the data from different sources. Using Informatica PowerCenter you can build enterprise data warehouses. The Informatica crop is also produced from the Informatica PowerCenter. Based on the given requirements the Informatica PowerCenter will extract data from different sources and transform the data and then load it to the target data warehouse. The essential components of Informatica PowerCenter are the repository server, client tools, server, and repository. The Informatica PowerCenter server executes the tasks based on the workflow. 

Topics Covered in this module are:

• Use of Informatica PowerCenter
• Data Governance
• Data Migration
• Master Data Management
• B2B Exchange
• Data Replication
• Data Synchronization
• Service-Oriented Architecture
• PowerCenter Products
• PowerCenter Client tools
• PowerCenter Repository

Informatica ETL is mainly used for the process of data extraction. It is totally based on the data warehouse concepts. In the process of data extraction, the data is extracted from multiple databases. The ETL process includes stages like extract, transform, and load. In this module, you will know in detail about these three phases of ETL. These three phases are executed parallelly and any of the phases will not wait for the start or end of the other phase. It also works as a data cleansing tool by which you can delete or correct records from a database.

Topics Covered in this module are:

• Use of Informatica ETL
• Implementation of ETL tool
• Extract, Transform, and Load data
• Key features of the ETL tool
• Use of ETL tool in real-time businesses
• Parallel Processing
• Characteristics of ETL Tool
• ETL Tool Products and Services

Mapping is defined as a collection of target and source objects that are tied together using a set of transformations. These transformations are built using a set of rules that has the capability to define how the data is loaded into targets and data flow. Mapping is defined as a process of modifying source data before it reaches the destination object. In Informatica, Mapping is considered as a primary object that defines the target object characteristics and data transformation details. 

Topics Covered in this module are:

• What is Mapping
• Need for Mapping
• Key components of Mapping
• Stage Mapping
• Mapping variables and parameters

Informatica Cloud is a data integration platform and solution that mainly works as Software as a Service (SaaS). Informatica Cloud is mainly used to connect databases, cloud-based applications, files, networking sites, etc. Informatica Cloud Data Integration is also known as Cloud-based PowerCenter which mainly delivers secure data to make more valuable business decisions. In this module, you will learn more in detail about the Informatica Cloud.

Topics Covered in this module are:

• What is Informatica Cloud
• Informatica Cloud Architecture
• What are Connectors
• Key components of Informatica Cloud
• Data Replication
• Data Synchronization
• Monitoring Services

Informatica MDM is abbreviated as Master Data Management. It is mainly used to manage the organization data as a single coherent system. Informatica MDM mainly ensures the reliability of data because the data collected from different sources and represented in different formats. It is also responsible for AI Training, digital transformation, and analytics decision making. The Informatica MDM processes are a mixture of different underlying processes. MDM creates a clear workflow between different destination sources and all the data sources. 

Topics Covered in this module are:

• What is Master Data Management
• Data Normalization
• Data Enrichment
• Data Consolidation
• Business Rule Administration
• Data Distribution
• Key Features of Informatica MDM
• Benefits of MDM
• Use Cases
• Recent trends in Informatica MDM
• MDM Best Practices

Informatica BDM is abbreviated as Big Data Management. It is a GUI based integrated development tool. This tool is widely used across many organizations to build data governance, data integration, and data quality processes for large platforms. Informatica BDM consists of a built-in smart executor that mainly supports different processing engines like Blaze, Apache Hive, Apache Spark, etc. BDM is mainly used to process data ingestion into a Hadoop Cluster and also the extraction of data from Hadoop Cluster.

Topics Covered in this module are:

• What is Big Data Management
• How to build data integration using BDM
• Authentication Process
• Authorization Process
• Blaze-Yarn Application
• Introduction Apache Spark
• Informatica Node Translation

Informatica Transformations plays a key role in Informatica Software. These Informatica Transformations are considered as repository objects that are mainly used to read, create, pass, and modify data to the defined target systems such as files, tables, and others. The main purpose of transformation in Informatica software is to modify the source data according to the given requirements of the destination system. It is responsible to ensure the quality of data loaded into the destination system. Moreover, these transformations are also used to represent the set of rules and the data flow process. 

Topic Covered in this module are:

• What is a Transformation
• Transformation Classification
• Connected Transformation
• Unconnected Transformation
• Active Transformations
• Passive Transformations

The Source Qualifier comes under the Connected and Active category. The main function of the Source Qualifier Transformation is to convert the various source data types into native data types. This transformation is also used to represent rows in which the integration service performs reading when the session runs. In the mapping process, you need to join the Qualifier Transformation with a flat-file or relational definition. In this module, you will get to know in detail about the Source Qualifier properties and other operations. 

Topics Covered in this module are:

• What is the use of Source Qualifier Transformation
• What are joins
• Filter Rows
• How to create Source Qualifier Transformation

The Router Transformation also comes under the Connected and Active Category. This transformation is mainly used to filter the source data. The data integration technique is used as a filter condition in the Router Transformation to evaluate every row of the incoming data. It always verifies the condition of every user-defined group before processing the default group. In this module, you will get to know in detail about the creation of Router Transformation and other properties. 

Topics Covered in this module are:

• What is a Router Transformation
• Comparison between Filter Transformation and Router Transformation
• Creation of Router Transformation

Rank Transformation also comes under the category of Connected and Active Transformations. This Transformation is mainly used to filter the data based on the ranks and groups. The Rank Transformation mainly consists of the output port to assign a rank to the rows. Rank Transformation is also used to choose the top or bottom range of data in Informatica. The Informatica Rank Transformation is used to rank the numeric port values. In this module, you will learn in detail about the process of creating Rank Transformation using the following ports. 

Topics Covered in this module are:

• What is Rank Transformation
• Rank Port
• Variable Port
• Input and Output Port
• Configuring Rank Transformation
• Essential properties of Rank Transformation
• Rank Index
• How to define groups

Aggregator Transformation of Informatica comes under the category of Active Transformation. This transformation is mainly used to perform arithmetic calculations on the data like counts, sum, average, etc. In the Aggregate Cache, the row data and the group of data are stored by integration service. Aggregator Transformation is the best choice when compared with SQL because you can make use of conditional clauses to filter rows. In this section, you will get through different Aggregate expressions and the process of creating Aggregator transformation.

Topics Covered in this module are:

• What is Aggregator Transformation
• What is Aggregate Expression
• What is Aggregate Cache
• Aggregate Functions
• Creation of Aggregator Transformation
• Configuring Aggregator Transformation
• What is Incremental Aggregator

Joiner Transformation also comes under the category of Connected and Active Transformation. It mainly provides the facility of creating joins in Informatica. With the help of Joiner Transformation, the created joins are closely related to joins in the databases. The main aim of a joiner transformation is to join two different heterogeneous sources. This transformation joins sources based on the given condition. There are two key sources present in the joiner transformation that are used for joins. You will get to know in detail how to configure joiner transformation.

Topics Covered in this module are:

• What is Joiner Transformation
• Different types of Joins
• Joiner Transformation Configuration
• What is Blocking Transformation
• How to improve the performance of Joiner Transformation
• Limitations of Joiner Transformation

The Transaction Control Transformation is connected and active transformation. This transformation is mainly used to rollback and commit transactions based on a set of rows pass through Transaction Control Transformation. In this module, you come across various commands, different transactions, and the creation of Transaction Control Transformation. 

Topics Covered in this module are:

• What is Transformation Control Transformation
• What is Transaction control expression
• How to configure Transaction Control Transformation
• Real-time examples of Transaction Control Transformation

The Normalizer Transformation is an Active Transformation. The main purpose of Normalizer is to convert a single row into multiple rows. This transformation has the capability to parse multiple occurring columns from relational tables, COBOL sources, and others. You will also learn in detail about the Normalizer Types and their working process. You can even configure a Normalizer Transformation in order to generate a separate result for each flat. 

Topics Covered in this module are:

• What is Normalizer Transformation
• Different Normalizer Transformation Types
• VSAM Normalizer Transformation
• Pipeline Normalizer Transformation

Sequence Generator Transformation also comes under the category of Connected and Passive Transformation. This transformation is mainly used to create a unique key value to replace the missing primary keys. Sequence Generator Transformation also generates a numeric sequence value without affecting the number of input rows. In this module, you will get to know in detail about the output ports used in the sequence generator transformation and the creation of the transformation.

Topics Covered in this module are:

• What is the Sequence Generator Transformation
• CURRVAL Port
• NEXTVAL Port
• Essential properties of Sequence Generator Transformation

The main aim of the Lookup transformation is to lookup after the source qualifier, target, and source to get all the relevant data. It is defined as a kind of join operation in which the source data and the lookup table are considered as join tables. You can retrieve the data using Lookup transformation based on the given condition. In this transformation, the result is returned to the target or other transformations. You will get to know in detail about configuring Lookup transformation and performing various tasks using Lookup.

Topics Covered in this module are:

• What is Lookup Transformation
• How to Configure Lookup Transformation
• Essential Properties of Lookup Transformation

Informatica Data Quality is a well-structured suite of components and applications that are mainly used to integrate with PowerCenter to deliver strong strength data quality capability in different scenarios. Informatica Data Quality was a front runner in the IDQ tools market. In this module, you will get to know in detail about data quality tools, different types of variants available in IDQ, core components of IDQ, and the Integration of IDQ with MDM.

Topics Covered in this module are:

• What is Informatica Data Quality
• Data Quality Server
• Data Quality Workbench
• Different variants of Lookup transformation

The main aim of Performance Tuning in Informatica is to eliminate performance bottlenecks to optimize the session performance and also to get accurate ETL Load Time. During the session, you will gain more knowledge about performance tuning in Informatica. You will also get to know in detail about the different tasks used to improve the session performance.

Topics Covered in this module are:

• What is Performance Tuning
• How to improve session performance
• Informatica PowerCenter Services