DataWarehousing

1. Accenture Ab Initio Training 1
Introduction to
Ab Initio
Prepared By : Ashok Chanda

2. Accenture Ab Initio Training 2
Ab initio Session 1
 Introduction to DWH
 Explanation of DW Architecture
 Operating System / Hardware Support
 Introduction to ETL Process
 Introduction to Ab Initio
 Explanation of Ab Initio Architecture

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What is Data Warehouse
 A data warehouse is a copy of transaction data
specifically structured for querying and
reporting.
 A data warehouse is a subject-oriented,
integrated, time-variant and non-volatile
collection of data in support of management's
decision making process.
 A data warehouse is a central repository for all
or significant parts of the data that an
enterprise's various business systems collect.

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Data Warehouse-Definitions
 A data warehouse is a database geared towards
the business intelligence requirements of an
organization. The data warehouse integrates
data from the various operational systems and is
typically loaded from these systems at regular
intervals. Data warehouses contain historical
information that enables analysis of business
performance over time. A collection of databases
combined with a flexible data extraction system.

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Data Warehouse
 A data warehouse can be normalized or
denormalized. It can be a relational
database, multidimensional database, flat
file, hierarchical database, object
database, etc. Data warehouse data often
gets changed. And data warehouses often
focus on a specific activity or entity.

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Why Use a Data Warehouse?
 Data Exploration and Discovery
 Integrated and Consistent data
 Quality assured data
 Easily accessible data
 Production and performance awareness
 Access to data in a timely manner

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Simplified Datawarehouse
Architecture

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Data warehouse Architecture
 Data Warehouses can be architected in many different
ways, depending on the specific needs of a
business. The model shown below is the "hub-and-
spokes" Data Warehousing architecture that is popular in
many organizations.
 In short, data is moved from databases used in
operational systems into a data warehouse staging area,
then into a data warehouse and finally into a set of
conformed data marts. Data is copied from one
database to another using a technology called ETL
(Extract, Transform, Load).

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The ETL Process
 Capture
 Scrub or Data cleansing
 Transform
 Load and Index

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ETL Technology
 ETL Technology is an important component of the Data
Warehousing Architecture. It is used to copy data from
Operational Applications to the Data Warehouse Staging
Area, from the DW Staging Area into the Data
Warehouse and finally from the Data Warehouse into a
set of conformed Data Marts that are accessible by
decision makers.
 The ETL software extracts data, transforms values of
inconsistent data, cleanses "bad" data, filters data and
loads data into a target database. The scheduling of
ETL jobs is critical. Should there be a failure in one ETL
job, the remaining ETL jobs must respond appropriately.


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Data Warehouse Staging Area
 The Data Warehouse Staging Area is temporary location
where data from source systems is copied. A staging
area is mainly required in a Data Warehousing
Architecture for timing reasons. In short, all required
data must be available before data can be integrated
into the Data Warehouse.
 Due to varying business cycles, data processing cycles,
hardware and network resource limitations and
geographical factors, it is not feasible to extract all the
data from all Operational databases at exactly the same
time

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Examples- Staging Area
 For example, it might be reasonable to extract sales data on a daily
basis, however, daily extracts might not be suitable for financial
data that requires a month-end reconciliation process. Similarly, it
might be feasible to extract "customer" data from a database in
Singapore at noon eastern standard time, but this would not be
feasible for "customer" data in a Chicago database.
 Data in the Data Warehouse can be either persistent (i.e. remains
around for a long period) or transient (i.e. only remains around
temporarily).
 Not all business require a Data Warehouse Staging Area. For many
businesses it is feasible to use ETL to copy data directly from
operational databases into the Data Warehouse.

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Data warehouse
 The purpose of the Data Warehouse in the overall Data
Warehousing Architecture is to integrate corporate
data. It contains the "single version of truth" for the
organization that has been carefully constructed from
data stored in disparate internal and external operational
databases.
 The amount of data in the Data Warehouse is
massive. Data is stored at a very granular level of
detail. For example, every "sale" that has ever occurred
in the organization is recorded and related to dimensions
of interest. This allows data to be sliced and diced,
summed and grouped in unimaginable ways.

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Data Warehouse
 Contrary to popular opinion, the Data Warehouses does
not contain all the data in the organization. It's purpose
is to provide key business metrics that are needed by
the organization for strategic and tactical decision
making.
 Decision makers don't access the Data Warehouse
directly. This is done through various front-end Data
Warehouse Tools that read data from subject specific
Data Marts.
 The Data Warehouse can be either "relational" or
"dimensional". This depends on how the business
intends to use the information.

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Data Warehouse Environment
In addition to a
relational/multidimensional database, a
data warehouse environment often
consists of an ETL solution, an OLAP
engine, client analysis tools, and other
applications that manage the process of
gathering data and delivering it to
business users.

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Data Mart
 A subset of a data warehouse, for use by a
single department or function.
 A repository of data gathered from operational
data and other sources that is designed to serve
a particular community of knowledge workers.
 A subset of the information contained in a data
warehouse.
 Data marts have the same definition as the data
warehouse (see below), but data marts have a
more limited audience and/or data content.

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Data Mart
 ETL (Extract Transform Load) jobs extract data from the Data
Warehouse and populate one or more Data Marts for use by groups
of decision makers in the organizations. The Data Marts can be
Dimensional (Star Schemas) or relational, depending on how the
information is to be used and what "front end" Data Warehousing
Tools will be used to present the information.
 Each Data Mart can contain different combinations of tables,
columns and rows from the Enterprise Data Warehouse. For
example, an business unit or user group that doesn't require a lot of
historical data might only need transactions from the current
calendar year in the database. The Personnel Department might
need to see all details about employees, whereas data such as
"salary" or "home address" might not be appropriate for a Data Mart
that focuses on Sales.

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Star Schema
 The star schema is perhaps the simplest data
warehouse schema.
 It is called a star schema because the entity-
relationship diagram of this schema resembles a
star, with points radiating from a central table.
 The center of the star consists of a large fact
table and the points of the star are the
dimension tables.

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Star Schema – continued
 A star schema is characterized by one or
more very large fact tables that contain
the primary information in the data
warehouse, and a number of much
smaller dimension tables (or lookup
tables), each of which contains
information about the entries for a
particular attribute in the fact table.

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Advantages of Star Schemas
 Provide a direct and intuitive mapping between
the business entities being analyzed by end
users and the schema design.
 Provide highly optimized performance for typical
star queries.
 Are widely supported by a large number of
business intelligence tools, which may anticipate
or even require that the data-warehouse schema
contain dimension tables
 Star schemas are used for both simple data
marts and very large data warehouses.

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Star schema
 Diagrammatic representation of star
schema

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Snowflake Schema
 The snowflake schema is a more complex
data warehouse model than a star
schema, and is a type of star schema.
 It is called a snowflake schema because
the diagram of the schema resembles a
snowflake.
 Snowflake schemas normalize dimensions
to eliminate redundancy.

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Snowflake Schema - Example
 That is, the dimension data has been grouped
into multiple tables instead of one large table.
For example, a product dimension table in a star
schema might be normalized into a products
table, a product_category table, and a
product_manufacturer table in a snowflake
schema. While this saves space, it increases the
number of dimension tables and requires more
foreign key joins. The result is more complex
queries and reduced query performance.

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Diagrammatic representation
for Snowflake Schema

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Fact Table
The centralized table in a star schema is
called as FACT table. A fact table typically
has two types of columns: those that
contain facts and those that are foreign
keys to dimension tables. The primary key
of a fact table is usually a composite key
that is made up of all of its foreign keys.

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What happens during the ETL
process?
 During extraction, the desired data is identified and
extracted from many different sources, including
database systems and applications. Depending on the
source system's capabilities (for example, operating
system resources), some transformations may take place
during this extraction process. The size of the extracted
data varies from hundreds of kilobytes up to gigabytes,
depending on the source system and the business
situation. After extracting data, it has to be physically
transported to the target system or an intermediate
system for further processing.

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Examples of Second-
Generation ETL Tools
 Powermart 4.5 – Informatica Corporation
 Pioneer due to market share
 Ardent DataStage – Ardent Software, Inc.
 General-purpose tool oriented to data marts
 Sagent Data Mart Solution 3.0 – Sagent
Technology
 Progressively integrated with Microsoft
 Ab Initio 2.2 – Ab Initio Software
 A kit of tools that can be used to build applications
 Tapestry 2.1 – D2K, Inc
 End-to-end data warehousing solution from a single vendor

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What to look for in ETL tools
 Use optional data cleansing tool to clean-up source data
 Use extraction/transformation/load tool to retrieve,
cleanse, transform, summarize, aggregate, and load data
 Use modern, engine-driven technology for fast, parallel
operation
 Goal: define 100% of the transform rule with point and
click interface
 Support development of logical and physical data models
 Generate and manage central metadata repository
 Open metadata exchange architecture to integrate central
metadata with local metadata.
 Support metadata standards
 Provide end users access to metadata in business terms

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Operating System / Hardware
Support
 This section discusses how a DBMS utilizes
OS/hardware features such as parallel
functionality, SMP/MPP support, and
clustering. These OS/hardware features
greatly extend the scalability and improve
performance. However, managing an
environment with these features is difficult
and expensive.

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Parallel Functionality
 The introduction and maturation of parallel
processing environments are key enablers of
increasing database sizes, as well as providing
acceptable response times for storing, retrieving,
and administrating data. DBMS vendors are
continually bringing products to market that take
advantage of multi-processor hardware
platforms. These products can perform table
scans, backups, loads, and queries in parallel.

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Parallel Features
An overview of typical parallel functionality is given below :
 Queries — Parallel queries can enhance scalability for many query
operations
 Data load — Performance is always a serious issue when loading
large databases. Meeting response time requirements is the
overriding factor for determining the best load method and should
be a key part of a performance benchmark
 Create table as select — This feature makes it possible to create
aggregated tables in parallel
 Index creation — Parallel index creation exploits the benefits of
parallel hardware by distributing the workload generated by a large
index created for a large number of processors .

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Which parallel processor
configuration, SMP or MPP ?
 SMP and clustered SMP environments , have the
flexibility and ability to scale in small increments.
 SMP environments are often useful for the large,
but static data warehouse, where the data
cannot be easily partitioned, due to the
unpredictable nature of how the data is joined
over multiple tables for complex searches and
ad-hoc queries.

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Which parallel processor
configuration, SMP or MPP ?
 MPP works well in environments where growth is potentially
unlimited, access patterns to the database are predictable, and the
data can be easily partitioned across different MPP nodes with
minimal data accesses crossing between them. This often occurs in
large OLTP environments, where transactions are generally small
and predictable, as opposed to decision support and data
warehouse environments, where multiple tables can be joined in
unpredictable ways.
 In fact, data warehousing and decision support are the areas most
vendors of parallel hardware platforms and DBMSs are targeting.
 MPP does not scale well if heavy data warehouse database accesses
must cross MPP nodes, causing I/O bottlenecks over the MPP
interconnect, or if multiple MPP nodes are continually locked for
concurrent record updates.

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A Multi-CPU Computer (SMP)

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A Network of Multi-CPU Nodes

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A Network of Networks

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Parallel Computer Architecture
 Computers come in many “shapes and sizes”:
 Single-CPU, Multi-CPU
 Network of single-CPU computers
 Network of multi-CPU computers
 Multi-CPU machines are often called SMP’s (for
Symmetric Multi Processors).
 Specially-built networks of machines are often called
MPP’s (for Massively Parallel Processors).

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Introduction to Ab
Initio

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History of Ab Initio
 Ab Initio Software Corporation was founded
in the mid 1990's by Sheryl Handler, the former
CEO at Thinking Machines Corporation, after
TMC filed for bankruptcy. In addition to Handler,
other former TMC people involved in the
founding of Ab Initio included Cliff Lasser,
Angela Lordi, and Craig Stanfill.
 Ab Initio is known for being very secretive in the
way that they run their business, but their
software is widely regarded as top notch.

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History of Ab Initio
 The Ab Initio software is a fourth generation
data analysis, batch processing, data
manipulation graphical user interface (GUI)-
based parallel processing tool that is used
mainly to extract, transform and load data.
 The Ab Initio software is a suite of products that
together provides platform for robust data
processing applications. The Core Ab Initio
Products are: The [Co>Operating System] The
Component Library The Graphical Development
Environment

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What Does “Ab Initio” Mean?
 Ab Initio is Latin for “From the Beginning.”
 From the beginning our software was designed to
support a complete range of business applications, from
simple to the most complex. Crucial capabilities like
parallelism and checkpointing can’t be added after the
fact.
 The Graphical Development Environment and a powerful
set of components allow our customers to get valuable
results from the beginning.

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Ab Initio’s focus
 “Moving Data”
 move small and large volumes of data in an
efficient manner
 deal with the complexity associated with business
data
 High Performance
 scalable solutions
 Better productivity

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Ab Initio’s Software
 Ab Initio software is a general-purpose
data processing platform for mission-
critical applications such as:
 Data warehousing
 Batch processing
 Click-stream analysis
 Data movement
 Data transformation

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Applications of Ab Initio
Software
 Processing just about any form and volume of data.
 Parallel sort/merge processing.
 Data transformation.
 Rehosting of corporate data.
 Parallel execution of existing applications.

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Ab Initio Provides For:
 Distribution - a platform for applications to
execute across a collection of processors within
the confines of a single machine or across
multiple machines.
 Reduced Run Time Complexity - the ability for
applications to run in parallel on any
combination of computers where the Ab Initio
Co>Operating System is installed from a single
point of control.

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Applications of Ab Initio
Software in terms of Data
Warehouse
 Front end of Data Warehouse:
 Transformation of disparate sources
 Aggregation and other preprocessing
 Referential integrity checking
 Database loading
 Back end of Data Warehouse:
 Extraction for external processing
 Aggregation and loading of Data Marts

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Ab Initio or Informatica-
Powerful ETL
 Informatica and Ab Initio both support parallelism. But Informatica
supports only one type of parallelism but the Ab Initio supports
three types of parallelism. In Informatica the developer need to do
some partitions in server manager by using that you can achieve
parallelism concepts. But in Ab Initio the tool it self take care of
parallelism we have three types of parallelisms in Ab Initio 1.
Component 2. Data Parallelism 3. Pipe Line parallelism this is the
difference in parallelism concepts.
2. We don't have scheduler in Ab Initio like Informatica you need to
schedule through script or u need to run manually.
3. Ab Initio supports different types of text files means you can read
same file with different structures that is not possible in Informatica,
and also Ab Initio is more user friendly than Informatica so there is
a lot of differences in Informatica and Ab initio.
 8. AbInitio doesn't need a dedicated administrator, UNIX or NT Admin will suffice, where as other ETL tools do have administrative work.

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Ab Initio or Informatica-
Powerful ETL-continued
 Error Handling - In Ab Initio you can attach error and reject files to
each transformation and capture and analyze the message and data
separately. Informatica has one huge log! Very inefficient when
working on a large process, with numerous points of failure.
 Robust transformation language - Informatica is very basic as far as
transformations go. While I will not go into a function by function
comparison, it seems that Ab Initio was much more robust.
 Instant feedback - On execution, Ab Initio tells you how many
records have been processed/rejected/etc. and detailed
performance metrics for each component. Informatica has a debug
mode, but it is slow and difficult to adapt to.

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Both tools are fundamentally
different
Which one to use depends on the work at hand and
existing infrastructure and resources available.
Informatica is an engine based ETL tool, the power this
tool is in it's transformation engine and the code that it
generates after development cannot be seen or
modified. Ab Initio is a code based ETL tool, it generates
ksh or bat etc. code, which can be modified to achieve
the goals, if any that cannot be taken care through the
ETL tool itself.
Ab Initio doesn't need a dedicated administrator, UNIX
or NT Admin will suffice, where as other ETL tools do
have administrative work.

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Ab Initio Product Architecture
Native Operating System (Unix, Windows, OS/390)
The Ab Initio Co>Operating® System
Component
Library
Development Environments
GDE Shell
3rd Party
Components
User-defined
Components
User Applications
Ab Initio
EME

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Ab Initio Architecture-
Explanation
 The Ab Initio Cooperating system unites the network of
computing resources-CPUs,storage disks , programs ,
datasets into a production quality data processing
system with scalable performance and mainframe class
reliability.
 The Cooperating system is layered on the top of the
native operating systems of the collection of servers .It
provides a distributed model for process execution, file
management ,debugging, process monitoring ,
checkpointing .A user may perform all these functions
from a single point of control.

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Co>Operating System Services
 Parallel and distributed application execution
 Control
 Data Transport
 Transactional semantics at the application level.
 Checkpointing.
 Monitoring and debugging.
 Parallel file management.
 Metadata-driven components.

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Ab Initio: What We Do
 Ab Initio software helps you build large-scale data
processing applications and run them in parallel
environments. Ab Initio software consists of two main
programs:
 Co>Operating System:
which your system administrator installs on a host Unix
or Windows NT server, as well as on processing
computers.
 The Graphical Development Environment (GDE):
which you install on your PC (GDE Computer) and
configure to communicate with the host.

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The Ab Initio Co>Operating®
System
 The Co>Operating System Runs across
a variety of Operating Systems and
Hardware Platforms including OS/390 on
Mainframe, Unix, and Windows. Supports
distributed and parallel execution. Can
provide scalability proportional to the
hardware resources provided. Supports
platform independent data transport.

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The Ab Initio Co>Operating®
System-Continued
The Ab Initio Co>Operating System
depends on parallelism to connect (i.e.,
cooperate with) diverse databases. It
extracts,
transforms and loads data to and from
Teradata and other data sources.

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Solaris,
AIX, NT,
Linux,
NCR
Top Layer
Co-Op System
Any OS
Same Co-Op Command
On any OS.
Graphs can be moved from
One OS to another w/o any
Changes.
Co-Operating System Layer
GDE
GDE
GDE
GDE

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The Ab Initio Co>Operating System
Runs on:
 Sun Solaris
 IBM AIX
 Hewlett-Packard HP-
UX
 Siemens Pyramid
Reliant UNIX
 IBM DYNIX/ptx
 Silicon Graphics IRIX
 Red Hat Linux
 Windows NT 4.0
(x86)
 Windows NT 2000
(x86)
 Compaq Tru64 UNIX
 IBM OS/390
 NCR MP-RAS

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Connectivity to Other Software
 Common, high performance database
interfaces:
 IBM DB2, DB2/PE, DB2EEE, UDB, IMS
 Oracle, Informix XPS,Sybase,Teradata,MS SQL
Server 7
 OLE-DB
 ODBC
 Other software packages:
 Connectors to many other third party products
 Trillium, ErWin, Siebel, etc.

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Ab Initio Cooperating System
Ab Initio Software Corporation, headquartered in Lexington, MA, develops
software solutions that process vast amounts of data (well into the terabyte
range) in a timely fashion by employing many (often hundreds) of server
processors in parallel. Major corporations worldwide use Ab Initio software
in mission critical, enterprise-wide, data processing systems. Together,
Teradata and Ab Initio
deliver:
• End-to-end solutions for integrating and processing data throughout
the enterprise
• Software that is flexible, efficient, and robust, with unlimited scalability
• Professional and highly responsive support
The Co>Operating System executes your application by creating and managing
the processes and data flows that the components and arrows represent.

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Graphical Development
Environment GDE

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The GDE
The Graphical Development Environment (GDE) provides
a graphical user interface into the services of the
Co>Operating System. The Graphical Development
Environment Enables you to create applications by
dragging and dropping Components. Allows you to point
and click operations on executable flow charts. The
Co>Operating System can execute these flowcharts
directly. Graphical monitoring of running applications
allows you to quantify data volumes and execution
times, helping spot opportunities for improving
performance.

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The Graph Model

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The Component Library:
 The Component Library: Reusable software
Modules for Sorting, Data Transformation,
database Loading Etc. The components adapt at
runtime to the record formats and business rules
controlling their behavior.
 Ab Initio products have helped reduce a
project’s development and research time
significantly.

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Components
 Components may run on any computer running
the Co>Operating System.
 Different components do different jobs.
 The particular work a component accomplishes
depends upon its parameter settings.
 Some parameters are data transformations, that
is business rules to be applied to an input (s) to
produce a required output.

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3rd Party Components

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EME
 The Enterprise Meta>Environment (EME) is a high-
performance object-oriented storage system that
inventories and manages various kinds of information
associated with Ab Initio applications. It provides storage
for all aspects of your data processing system, from
design information to operations data.
 The EME also provides rich store for the applications
themselves, including data formats and business rules. It
acts as hub for data and definitions . Integrated
metadata management provides the global and
consolidated view of the structure and meaning of
applications and data- information that is usually
scattered throughout you business .

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Benefits of EME
The Enterprise Meta>Environment provides a rich store
for applications and all of their associated information
including :
 Technical Metadata-Applications related business rules
,record formats and execution statistics
 Business Metadata-User defined documentations of job
functions ,roles and responsibilities.
Metadata is data about data and is critical to understanding
and driving your business process and computational
resources .Storing and using metadata is as important to
your business as storing and using data.

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EME-Ab Initio Relevance
 By integrating technical and business
metadata ,you can grasp the entirety of
your data processing – from operational to
analytical systems.
 The EME is completely integrated
environment. The following figure shows
how it fits in to the high level architecture
of Ab Initio software.

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Stepwise explanation of Ab
Initio Architecture
 You construct your application from the building blocks
called components, manipulating them through the
Graphical Development Environment (GDE).
 You check in your applications to the EME.
 The EME and GDE uses the underlining functionality of
the Co>Operating System to perform many of their
tasks. The Cooperating System units the distributed
resources into a single “virtual computer” to run
applications in parallel.
 Ab Initio software runs on Unix ,Windows NT,MVS
operating systems.

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Stepwise explanation of Ab
Initio Architecture - continued
 Ab Initio connector applications extract
metadata from third part metadata sources into
the EME or extract it from the EME into a third
party destination.
 You view the results of project and application
dependency analysis through a Web user
interface .You also view and edit your business
metadata through a web user interface.

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EME :Various users
constituency served
The EME addresses the metadata needs of
three different constituencies:
 Business Users
 Developers
 System Administrators

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EME :Various users
constituency served
 Business users are interested in exploiting data
for analysis, in particular with regard to
databases ,tables and columns.
 Developers tend to be oriented towards
applications ,needing to analyze the impact of
potential program changes.
 System Administrator and production personnel
want job status information and run statistics.

75. Accenture Ab Initio Training 75
EME Interfaces
We can create and manage EME through
3 interfaces:
 GDE
 Web User Interface
 Air Utility

76. Accenture Ab Initio Training 76
Thank You
End of Session 1