Informatica Tutorial: Beginner to Expert Level
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Informatica Tutorial: Beginner to Expert Level

Become an expert in world's top Data Integration tool.
Bestselling
4.3 (418 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
2,139 students enrolled
Created by Sid Inf
Last updated 5/2017
English
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Includes:
  • 35.5 hours on-demand video
  • 15 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Understand Data Warehouse Concepts and ETL Concepts
  • Describe Informatica PowerCenter architecture & its different components
  • Use PowerCenter 10x/9.x components to build Mappings, Tasks, Workflows
  • Describe the basic and advanced features functionalities of PowerCenter 10.0/9.6 transformations
  • Understand Workflow Task and job handling
  • Describe Mapping Parameter and Variables
  • Perform debugging, troubleshooting, error handling and recovery
  • Learn different types of cache available and how to calculate cache requirement and implement session cache
  • Execute performance tuning and Optimization
  • Identify and explain the functionalities of the Repository Manager tool
  • Identify how to handle services in the Administration Console
  • Understand techniques of SCD, XML Processing, Partitioning, Constraint based loading and Incremental Aggregation
  • Gain insight on ETL best practices using Informatica
  • Understand all the basic interview questions for all the transformations and real time scenarios
View Curriculum
Requirements
  • Knowledge of basic SQL
  • Basic Unix Commands
Description

Informatica Power Center Data Integration tool is the top in the Gartner’s magic quadrant for the past ten years with high GO LIVE rate compared to any other existing ETL tools in the market.

Informatica Power Center tool supports all the steps of Extraction, Transformation and Load process/life cycle. There are lot of other (third party) products which are offered and created around the Power Center’s ability to connect to different technologies ranging from Legacy systems, Mainframes to CRM, SAP, TIBCO, HP VERTICA to BIG DATA.

Informatica Power Center Developer course, will introduce you to work with the Power Center version 10.x/9.6x to create, execute, as well as administer, monitor and schedule ETL processes and understand how these are used in data mining operations and enterprise data warehouse setup.

Informatica Power Center is an easy to use GUI based tool. It has a simple visual interface which is easy to understand and use. All the components are designed to be used by a simple drag and drop feature for different objects like source, targets, transformations, mapplets, mappings, sessions, worklets and workflows which contribute to the design process flow of the data extraction, transformation and load.

Once the objects are integrated into a package called as workflow, it can be scheduled to run as and when required with rich features to accommodate all the possibilities of a business requirement. 

The architecture of Informatica 10x/9x is created based on the SOA (Service Oriented Architecture) which takes care of the data fetch, execution of the transformation and load the data into the target systems in the desired formats like Relational, Flat File, XML, WSDL, PDF, Word (Structured, Semi-Structured, Un-Structured).

Informatica Power Center 10x/9x has the ability to communicate via ODBC sources/plugins/extensions with all major data sources like Mainframe, Big Data, traditional RDBMS (Oracle, SQL Server, Teradata, Netezza, Informix, DB2 etc), NoSQL (Mongo DB, Cassandra etc) Flat Files, XML, TIBCO, WSDL (Via web services) SAP, Salesforce etc) and move/transform data between them based on the need.

The tool’s ability to fetch/transform and load huge volumes of data in a very effective way, with less resource consumption is better than hand coded programs written for specific data movement using PL/SQL procedures, Java, Perl and other languages.

The course covers all topics starting from Data warehouse concepts, Roles and Responsibilities of an ETL developer, Installation & Configuration of Informatica Power Center 10x/9.X, in detailed explanation of transformations with practical examples, performance tuning tips for each transformation (clearly shown and explained), usually asked interview questions, quizzes for each section and assignments for your hands on and in-depth explanation of the Repository Service, Integration Service and other basic Administration Activities. 

Who is the target audience?
  • Software Developers
  • Analytics Professionals
  • BI/ETL/DW Professionals
  • Mainframe Developers and Architects
  • Individual Contributors in the field of Enterprise Business Intelligence
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Curriculum For This Course
Expand All 285 Lectures Collapse All 285 Lectures 35:22:26
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Some basic information about the course before you start
1 Lecture 10:00

Thank you and welcome to this course. 

In this lecture, I have tried to put in a brief perspective of what you are going to get into and what you will get out of this course. 

Preview 10:00
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Data Warehouse Concepts : Back to Basics
5 Lectures 22:23

The concept of data warehousing is not hard to understand. The notion is to create a permanent storage space for the data needed to support reporting, analysis, and other BI functions. In this lecture we understand what are the main reasons behind creating a data warehouse and the benefits of it. 

This long list of benefits is what makes data warehousing an essential management tool for businesses that have reached a certain level of complexity.

Why do we need a Data Warehouse?
05:26

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.

What is a Data Warehouse?
05:48

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions.

What is Business Intelligence?
05:37

Business intelligence (BI) is the use of computing technologies for the identification, discovery and analysis of business data - like sales revenue, products, costs and incomes.

BI technologies provide current, historical and predictive views of internally structured data for products and departments by establishing more effective decision-making and strategic operational insights through functions like online analytical processing (OLAP), reporting, predictive analytics, data/text mining, bench marking and Business Performance Management (BPM). These technologies and functions are often referred to as information management.

What is Business Intelligence? - Extended
03:34

Data Warehouse Concepts play a critical role in all the Data Warehouse and ETL projects. This course is equipped with the content which is required for you to start. 

But, if you want in-depth knowledge on the foundations of the Data Warehouse Concepts, you can enroll to the course as mentioned in the lecture. 

The Complete Master Data Warehouse Concepts Course
01:58

Let's answer few questions about the basic questions of Data Warehouse and Business Intelligence. 

Test your understanding on Data Warehouse & Business Intelligence
4 questions
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Data Warehouse Architectures
6 Lectures 23:19

In this lecture we see how the Centralized architecture is set up, in which there exists only one data warehouse which stores all data necessary for the business analysis

Enterprise Architecture or Centralized Architecture
04:46

In a Federated Architecture the data is logically consolidated but stored in separate physical database, at the same or at different physical sites. The local data marts store only the relevant information for a department. 

The amount of data is reduced in contrast to a central data warehouse. The level of detail is enhanced in this kind of model. 

Federated Architecture
03:05

Multi Tired architecture is a distributed data approach. This process cannot be done in a one step because many sources have to be integrated into a warehouse.

Multi Tired Data Warehouse Architecture
03:13

Different data warehousing systems have different structures. Some may have an ODS (operational data store), while some may have multiple data marts. Some may have a small number of data sources, while some may have dozens of data sources. In view of this, it is far more reasonable to present the different layers of a data warehouse architecture rather than discussing the specifics of any one system.

In general, all data warehouse systems have the following layers:

  • Data Source Layer
  • Data Extraction Layer
  • Staging Area
  • ETL Layer
  • Data Storage Layer
  • Data Logic Layer
  • Data Presentation Layer
  • Metadata Layer
  • System Operations Layer
Components of a Data Warehouse Architecture
03:57

This is where data is stored prior to being scrubbed and transformed into a data warehouse / data mart. Having one common area makes it easier for subsequent data processing / integration. Based on the business architecture and design there can be more than one staging area which can be termed with different naming conventions. 

Purpose of Staging Area in a Data Warehouse Architecture
04:43

This is where data is stored prior to being scrubbed and transformed into a data warehouse / data mart. Having one common area makes it easier for subsequent data processing / integration. Based on the business architecture and design there can be more than one staging area which can be termed with different naming conventions. 

Purpose of Staging Area in a Data Warehouse Architecture - part 2
03:35

Let's review your understanding on the Data Warehouse Architectures

Test your understanding on Data Warehouse Architectures
3 questions
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ODS VS OLTP Vs OLAP
6 Lectures 25:07

An ODS is designed for relatively simple queries on small amounts of data (such as finding the status of a customer order), rather than the complex queries on large amounts of data typical of the data warehouse. 

An ODS is similar to your short term memory in that it stores only very recent information; in comparison, the data warehouse is more like long term memory in that it stores relatively permanent information.

What is ODS?
02:20

To understand the purpose of the ODS and when it is an appropriate solution, its characteristics must first be defined.

Characteristics of an Operational Data Store

Subject Oriented : The ODS contains specific data that is unique to a set of business functions. The data therefore represents a specific subject area.

Integrated : Data in the ODS is sourced from various legacy applications. The source data is taken through a set of ETL operations that includes cleansing and trans-formative processes. These processes are based on rules that        have been created through business requirements for data quality and standardization.

Current (non-historical) : The data in the ODS is up-to-date and is a current status of data from the sourcing applications.

Detail : Data in the ODS is primarily used to support operational business functions. This means that there is a      specific level of granularity based on business requirements that dictate the level of detail that data in the  ODS will have.

Features and Benefits of ODS
02:18

OLAP (Online Analytical Processing) is the technology behind many Business Intelligence (BI) applications. OLAP is a powerful technology for data discovery, including capabilities for limitless report viewing, complex analytical calculations, and predictive “what if” scenario (budget, forecast) planning.

OLAP Overview
05:54

OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transnational databases is the entity model (usually 3NF). 

OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).

OLTP Vs OLAP -Part 1
04:33

OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transnational databases is the entity model (usually 3NF). 

OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).

OLTP Vs OLAP - Part 2
06:01

Please refer to the additional resources of this section which contains the Info-graphic on the differences between the ODS, DWH, OLTP, OLAP, DSS and DM (Data Mart).

Differences between ODS,DWH, OLTP, OLAP, DSS and Data Mart
04:01

Test your understanding ODS, OLAP, OLTP, Data Warehouse

Test your understanding ODS, OLAP, OLTP, Data Warehouse
4 questions
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Data Mart
2 Lectures 02:23

The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department.

What is a Data Mart?
01:47

Data Warehouse:

  • Holds multiple subject areas
  • Holds very detailed information
  • Works to integrate all data sources
  • Does not necessarily use a dimensional model but feeds dimensional models.


Data Mart:

  • Often holds only one subject area- for example, Finance, or Sales
  • May hold more summarized data (although many hold full detail)
  • Concentrates on integrating information from a given subject area or set of source systems
  • Is built focused on a dimensional model using a star schema.
Fundamental Difference between DWH and DM
00:36

Test your understanding on Data Marts

Test your understanding on Data Marts
3 questions
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Dimensional Modeling
13 Lectures 01:02:40

Dimensional Model is a database structure that is optimized for online queries and Data Warehousing tools. It is comprised of "fact" and "dimension" tables. A "fact" is a numeric value that a business wishes to count or sum. A "dimension" is essentially an entry point for getting at the facts.

What is Dimensional Modeling?
04:39

dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time. In a data warehousedimensions provide structured labeling information to otherwise un-ordered numeric measures.

What is a Dimension?
02:48

In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is often located at the center of a star schema, surrounded by dimension tables.

There are four types of facts. 

  • Additive - Measures that can be added across all dimensions.
  • Non Additive - Measures that cannot be added across all dimensions.
  • Semi Additive – Measures that can be added across few dimensions and not with others.
  • Fact less fact tables – The fact table does not have aggregate numeric values or information.
What is a Fact?
02:05

surrogate key is any column or set of columns that can be declared as the primary key instead of a "real" or natural key. Sometimes there can be several natural keys that could be declared as the primary key, and these are all called candidate keys. So a surrogate is a candidate key.

What is a Surrogate key?
04:01

A star schema is the simplest form of a dimensional model, in which data is organized into facts and dimensions

Star Schema
05:23

 The snowflake schema is diagrammed with each fact surrounded by its associated dimensions (as in a star schema), and those dimensions are further related to other dimensions, branching out into a snowflake pattern.

Snow flake Schema
03:25

When choosing a database schema for a data warehouse, snowflake and star schema tend to be popular choices. This comparison discusses suitability of star vs. snowflake schema in different scenarios and their characteristics.

SnowFlake Vs Start Schema
05:27

conformed dimension is a dimension that has exactly the same meaning and content when being referred from different fact tables. A conformed dimension can refer to multiple tables in multiple data marts within the same organization.

Conformed Dimension
06:38

 In a Junk dimension, we combine these indicator fields into a single dimension. This way, we'll only need to build a single dimension table, and the number of fields in the fact table, as well as the size of the fact table, can be decreased.

Junk Dimension
03:06

According to Ralph Kimball, in a data warehouse, a degenerate dimension is a dimension key in the fact table that does not have its own dimension table, because all the interesting attributes have been placed in analytic dimensions. The term "degenerate dimension" was originated by Ralph Kimball. 

Degenerate Dimension
03:35

We start with the basic definition of a Dimension, Fact and start with the Slowly Changing Dimensions.

Slowly Changing Dimensions - Intro and Example
05:30

There are many approaches how to deal with SCD. The most popular are: 

  • Type 0 - The passive method
  • Type 1 - Overwriting the old value
  • Type 2 - Creating a new additional record
  • Type 3 - Adding a new column
  • Type 4 - Using historical table
  • Type 6 - Combine approaches of types 1,2,3 (1+2+3=6)
Slowly Changing Dimensions - Type 1, 2 and 3
12:30

Dimension, Fact and SCD Type 1, 2 and 3 are reviewed in this lecture. 

Slowly Changing Dimensions - Summary
03:33

Test your understanding on Dimensional Modeling

Test your understanding on Dimensional Modeling
5 questions
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Data Warehouse Indexes
4 Lectures 11:47

Indexing the data warehouse can reduce the amount of time it takes to see query results. When indexing dimensions, you'll want to index on the dimension key. When indexing the fact table, you'll want to index on the date key or the combined data plus time.

What is an Index?
02:12

bitmap index is a special kind of database index that uses bitmaps.Bitmap indexes have traditionally been considered to work well for low-cardinality columns, which have a modest number of distinct values, either absolutely, or relative to the number of records that contain the data.

Bit-Map Index
04:17

B-tree is a self-balancing tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.

BTree Index
01:56

One of the common questions which come up in the interviews is which one is the better one to use, Is it Bitmap or B Tree?

In this lecture, we try to evaluate the differences and the best one to use.

BTree Vs BitMap
03:22

Test your understanding on Data Warehouse Indexes

Test your understanding on Data Warehouse Indexes
4 questions
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ETL Vs ELT
7 Lectures 45:37

Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from a variety of sources.

What is Data Integration?
06:49

ETL (Extract, Transform and Load) is a process in data warehousing responsible for pulling data out of the source systems and placing it into a data warehouse.

ETL - Explained
06:41

ELT is a variation of the Extract, Transform, Load (ETL), a data integration process in which transformation takes place on an intermediate server before it is loaded into the target. In contrast, ELT allows raw data to be loaded directly into the target and transformed there.

ELT - Explained
05:57

In ETL (extract, transform, load) operations, data are extracted from different sources, transformed separately, and loaded to a Data Warehouse database.

 In ELT, the extracts are fed into the single staging database that also handles the transformations.

ETL Vs ELT
02:23

Though, its not limited to the below, here are some of the commonly used terms in any ETL project.

•Source Systems
•Mapping
•Metadata
•Staging Area
•Data Cleansing/Scrubbing
•MDM -  Golden Source
•Transformation
•Target Systems
•Reporting/BI
•Scheduling

Commonly used terms in an ETL project - Part 1
07:52

Though, its not limited to the below, here are some of the commonly used terms in any ETL project.


•Source Systems
•Mapping
•Metadata
•Staging Area
•Data Cleansing/Scrubbing
•MDM -  Golden Source
•Transformation
•Target Systems
•Reporting/BI
•Scheduling

Commonly used terms in an ETL project - Part 2
06:34

This lecture is in response to the question below:

Could you please elaborate MDM - Golden Source. What does MDM store? Does MDM store dimension data and the data warehouse store fact data where MDM is implemented?

MDM - Golden Source explained
09:21

Test your understanding on ETL Vs ELT

Test your understanding on ETL Vs ELT
3 questions
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Different Tools/Technologies used in DWH/BI/ETL
3 Lectures 14:46

In this lecture we talk about the different Enterprises Databases which can be used as a Data Warehouse. 

Please note, NoSQL databases are not discussed in this lecture.

Different Types of Enterprise Databases
04:46

In this lecture we talk about the different popular ETL tools available in the market. 

Different ETL tools available in the Market
06:48

Based on the Gartner's magic quadrant we see which ETL tool is the leader in the ETL technologies and what is the best choice for you to learn. 

Which ETL tool should you learn?
03:12

Test your understanding on different types of ETL Tools

Test your understanding on different types of ETL Tools
1 question
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Roles and Responsibilities of ETL Developer
3 Lectures 21:09

The daily activities and the roles and responsibilities of an ETL developer are mentioned. These are covered considering the involvement of the ETL developer at various phases of the Data Warehouse implementation life cycle. 

Roles and Responsibilities of an ETL Developer - Part 1
07:27

This is in continuation of the previous lecture (Part 2) and we talk about the different responsibilities of an ETL developer. 

Roles and Responsibilities of an ETL Developer - Part 2
06:11

This is in continuation of the previous lecture (Part 3) and we talk about the different responsibilities of an ETL developer. 

Roles and Responsibilities of an ETL Developer - Part 3
07:31

Test your understanding on Roles and Responsibilities of an ETL developer

Test your understanding on Roles and Responsibilities of an ETL developer
1 question
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About the Instructor
Sid Inf
4.3 Average rating
1,029 Reviews
6,046 Students
5 Courses
Data/ETL Architect

Business Intelligence Consultant and Trainer with 14+ years of extensive work experience on various client engagements. Delivered many large data warehousing projects and trained numerous professionals on business intelligence technologies. Extensively worked on all facets of data warehousing including requirement gathering, gap analysis, database design, data integration, data modeling, enterprise reporting, data analytics, data quality, data visualization, OLAP.

Has worked on broad range of business verticals and hold exceptional expertise on  various ETL tools like Informatica Powercenter, SSIS, ODI and IDQ, Data Virtualization, DVO, MDM.