Informatica Tutorial: Beginner to Expert Level

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.

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.

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.

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.

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. 

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

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. 

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. 

A 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.

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

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. 

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. 

Let's review your understanding on the Data Warehouse Architectures

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.

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.

This lecture covers the topic of the difference between Staging and ODS.

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.

- 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 (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).

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).

Test your understanding ODS, OLAP, OLTP, Data Warehouse

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.

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.

Test your understanding on Data Marts

A 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.

A 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 warehouse, dimensions provide structured labeling information to otherwise un-ordered numeric measures.

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.

A 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.

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

 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.

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.

A 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.

 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.

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. 

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

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)

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

Test your understanding on Dimensional Modeling

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.

A 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.

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

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.

Test your understanding on Data Warehouse Indexes

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.

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.

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.

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.

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

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

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?

Test your understanding on ETL Vs ELT

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.

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

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. 

Test your understanding on different types of ETL Tools

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. 

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

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

Test your understanding on Roles and Responsibilities of an ETL developer