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Data Warehouse Archiving:
A Way to Optimize Data Warehouse Performance and Reduce Costs
W H I T E PA P E R
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This edition published January 2010.
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Table of Contents
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Drivers for Managing Data Growth in Data Warehouses . . . . . . . . . . . . . . . 3
Conventional Solutions and Their Limitations . . . . . . . . . . . . . . . . . . . . . . . 5
Upgrading Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Database Tuning and Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Hand Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Purging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
The Benefits of Data Warehouse Archiving . . . . . . . . . . . . . . . . . . . . . . . . . 6
Key Requirements of a Data Warehouse Archiving Solution . . . . . . . . . . . . 8
Data Growth Assessment Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Metadata Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Simple Metadata Extensibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Robust Archiving Techniques to Enable Optimal Storage Tiers . . . . . . . . . . . . . . . . . . . . . 11
Easy, Multiple Methods to Access Archived Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Universal Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Integration with Other Archiving Platform, Enterprise Content Management,
and Storage Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Informatica Data Archive:
The Complete Data Warehouse Archiving Solution . . . . . . . . . . . . . . . . . . 13
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
1
Executive Summary
Data warehouses are critical systems that link data from multiple source applications, aggregating
the data, and delivering it to analytical decision support systems, which are central to many
organizations’ financial analysis and decision making processes. Given that data warehouses
integrate data from multiple systems and the cumulative nature of the application, which at
the same time requires drilling down to detailed data, data warehouses tend to have huge data
volumes, measurable in terabytes. And the size of data warehouses will continue to grow at a
staggering rate.
The increase in data warehouse volumes stems from a number of factors, including:
• The need to piece together data from more disparate applications for a complete view of the
customer or other business entity
• Increased database complexity as additional information about each transaction is captured
• The need to integrate data more often in real time
• Expanding transaction volumes from organic business growth
• The need to retain data for longer periods to comply with regulations—further increasing data
volumes and management costs
To address these increasingly complex issues, IT organizations need a cost-effective, long-term
solution for managing data growth in data warehouses—along with the performance degradation
and maintenance costs associated with such growth. The answer to this problem is data
warehouse archiving.
This white paper examines how data warehouse archiving can help your IT organization better
manage the growing data volume in your data warehouses and reduce the associated storage
costs by using tiers. After reading this paper, you’ll have a better understanding of:
• The drivers for managing data growth in data warehouses
• How conventional methods of managing data growth fall short
• The benefits of data warehouse archiving
• The key requirements for a data warehouse archiving solution
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Drivers for Managing Data Growth in Data Warehouses
As Figure 1 shows, data volumes aren’t just growing—they’re exploding, Forrester Research
estimates the volume of data housed in large business applications, including data warehouses,
grows by as much as 65 percent each year.1 Most of this growth is due to an accumulation of
inactive data. IDC estimates 85 percent of production data is inactive.
Exabytes
25.0
20.0
15.0
10.0
5.0
0.0
2005
2006
2007
2008 2009
2010 2011
Figure 1. Data repositories for large business systems, including data warehouses are growing by more than 65
percent annually.
1
Forrester Research, Securing Next-Generation Information Architectures, The Promise of Improved Security or the
Risk of New Attack Vectors, October 24, 2008.
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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The growth in your data warehouse volumes can be attributed to several factors:
• Business growth. As your business grows, more transaction volumes are added to your
applications. When your company merges with or acquires another, or expands its operations
globally, the result is more data.
• Demand for real-time data. Today, users don’t want old or stale data. They need current and up-
to-the-minute information, so that they can make better business decisions. As a result, data
warehouses are updated more frequently to integrate the latest information.
• Holistic view driving decision making. More and more information from disparate sources
is integrated into data warehouses to supply a more holistic view of the customer or other
business entities. As a result, data warehouses are becoming massive databases, providing the
complete information necessary to enable faster and more accurate decision making.
• Longer data retention for compliance. Organizations are retaining data for longer and longer
periods due to regulatory compliance. Some regulations are requiring organizations to retain
data for as long as ten years.
Increasing data size brings a number of issues in managing data warehouses:
• Higher infrastructure and maintenance costs. More data also means additional hardware,
software, and maintenance costs. Although storage cost continues to fall, production data
warehouses are usually housed in high-end primary storage, which still commands a significant
portion of the IT budget. With more data to process, you also require more CPUs, which lead
to additional database license costs. More time and effort spent in performing administrative
tasks such as backups and upgrades means not only longer system unavailability but also
time diverted from more critical or strategic IT projects, growing IT staff overtime cost, or even
additional full-time equivalent (FTE) cost.
• Reduced system availability. As data volumes grow, it takes more time and effort for your end
users and database administrators to perform essential tasks on production data warehouses.
Data warehouse loads take longer to complete. Database backups are slower and can’t be
done overnight. Upgrading database versions or applying software patches becomes more
complicated and can’t be completed over a weekend. Maintaining application service level
agreements (SLAs), while keeping cost down becomes virtually impossible.
• Data warehouse performance declines as more data accumulates, as Figure 2 illustrates.
Reports take longer to run and overall end-user response time is slower.
Database Size
Performance
Inactive Data
Active Data
Time
Figure 2. Data warehouse performance declines as the volume of data grows over time.
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These challenges are prompting IT organizations to look for more effective solutions to manage the
growing data in their data warehouses.
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Conventional Solutions and Their Limitations
If your IT organization is like most, you’ve used a variety of methods to manage data growth in your
data warehouses. For example:
• You may have purchased additional storage and processing hardware.
• You may have tuned and partitioned the database.
• You may have developed in-house scripts to purge or archive data.
But these conventional approaches often fail to deliver a long-term solution to your data
warehouse management challenges. Let’s explore the limitations of these typical solutions.
Upgrading Hardware
Throwing more hardware at the problem may seem like the simplest answer, but it is not a viable
long-term solution—even with the downward trend of disk and processor costs and the availability
of powerful data warehouse appliances. With larger and larger data volumes, input/output
or network bandwidth becomes the bottleneck eventually. And more hardware just increases
architectural complexity while offering limited scalability improvements. Large, powerful data
warehouse appliances can also become expensive as data continues to grow and more and more
processing power is required.
Database Tuning and Partitioning
Data warehouse administrators commonly turn to tuning and partitioning to manage data
growth within the database and improve application performance. But DBAs quickly discover
that while tuning is effective the first time, successive tunings offer diminishing returns and are
more time intensive.
While partitioning offers some relief to improve database performance, it doesn’t reduce the
required storage capacity and is limited in its potential to lower overall infrastructure cost,
including database license, server, and storage costs.
Hand Coding
In-house code or scripts to purge or archive data in data warehouses are expensive to develop
and maintain because they require deep knowledge of business entities, table schemas,
relationships, and business rules. Because constraints and relationships between data warehouse
objects are not always completely maintained within the data warehouse metadata, in-house
scripts tend to apply business rules for archiving or purging inconsistently across records, tables,
entities, and databases.
Purging Data
Just purging data in data warehouses is not a safe alternative due to compliance reasons.
Although a good percentage of the data in data warehouses is derived from other sources and
can be reproduced from integrating these sources, many data warehouses include additional
operational or transactional data, which is not stored in other data sources. Data warehouses
also tend to evolve to become applications of their own right, which need to be backed up and
archived to ensure availability, quick recovery, and e-discovery for compliance audit purposes.
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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The Benefits of Data Warehouse Archiving
The key to managing exploding data volumes in data warehouses lies in two facts: the value of all
data diminishes over time, and all data is not created equal.
Let’s examine the time issue first. Your business users may need access to detailed revenue
information from the last year for financial reporting purposes. Once the fiscal year ends,
financial information from the previous year or three years ago is not accessed as regularly. As
a result, this “historical” data is largely inactive—used infrequently for aggregate reporting and
compliance purposes.
The second consideration is the fact that all data is not equally important. In data warehouses,
while historical aggregate information is needed for longer periods (e.g., annual revenue
information may be required for reporting performance during the past three to seven years),
transactional data or more granular aggregates (e.g., quarterly revenue information) are rarely
needed beyond a year.
IT organizations need a way to cost-effectively, efficiently, and securely manage different
classifications of production data in data warehouses based on their value to the business
throughout the data lifecycle. According to Gartner, one of the best practices for managing a
scalable data warehouse is that its architecture must account for its storage and access, as well
as its archive and retirement.2 This statement reinforces the need for managing data growth and
the lifecycle of the data in data warehouses by data archiving and retirement.
Data warehouse archiving enables IT organizations to purge or relocate less-valued or lessfrequently accessed data from production data warehouses to second- or third-line storage to
reduce costs, increase system availability, and improve performance—all while satisfying data
retention, access, and security requirements. Figure 3 shows an example of a tiered storage
strategy for data warehouses.
ARCHIVE
ARCHIVE
ERP/SCM
Databases
PROD
DW
DW
Archive
Compressed
File-Based Archive
Flat Files
CRM Application
Production
Applications in 1st
Tier Storage
(e.g., SAN)
Production Data Warehouse on
1st or 2nd Tier Storage
(e.g., SAN, NAS, DW App)
RESTORE
RESTORE
Data Warehouse
Archive on 2nd or
3rd Tier Storage
(e.g., SATA, NAS)
Compressed File-Based
Archive on 2nd or 3rd Tier Storage
(e.g., NAS, CAS, Cloud)
Figure 3. Example of a tiered storage strategy for data warehouses.
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2
Beyer, Mark A., Data Warehouse Architecture Best Practices and Guiding Principles, Gartner Research,
November 6, 2009.
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Data warehouse archiving helps IT organizations to:
• Cost-effectively manage data growth by relocating inactive data to less expensive infrastructure
and enabling storage tiering
• Improve data warehouse performance by removing inactive data to reduce the size of data that
needs to be processed within production data warehouses
• Support regulatory compliance by cost-effectively retaining data for a longer period
Data Warehouse Archiving Solution
What Should Your IT Organization Look For?
• Data growth assessment capabilities. Can the solution assess and target the
largest and fastest growing tables, table spaces, and schemas?
• Metadata discovery. Does the solution provide automatic discovery of metadata
about tables, columns, and relationships?
• Simple metadata extensibility. Does the solution offer simple graphical user
interfaces to allow you to extend and customize the discovered metadata?
• Robust archiving techniques to enable optimal storage tiers. Does the
solution provide multiple archiving formats and destination options? Does
it allow archiving the highest growth tables while maintaining data integrity?
Does it allow restoration of the data to support varying storage and access
requirements?
• Multiple, easy access options to archived data. Can you access the archived
data easily, either from the same application interface or from an applicationindependent interface, using standard protocols?
• Universal connectivity. Can the solution archive data from any source system?
• Integration with other archiving platforms, enterprise content management
(ECM) storage. Does the solution support integration with other archiving
platforms, ECM systems, and archival storage to support central storage
management and discovery of archived data?
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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Key Requirements of a Data Warehouse Archiving Solution
If your IT organization is evaluating a data warehouse archiving solution, the following are key
requirements you should consider:
• Data growth assessment capabilities
• Metadata discovery
• Simple metadata extensibility
• Robust archiving techniques to enable optimal storage tiers
• Easy, multiple methods to access archived data
• Universal connectivity
• Integration with other archiving platform, enterprise content management, and storage solutions
Let’s examine these factors in greater detail.
Data Growth Assessment Capabilities
Your IT organization first needs to evaluate which tables and table spaces are growing most rapidly.
A data warehouse archiving solution should enable you to assess data growth not just once, but on
an ongoing basis to continually adjust archiving strategies and maximize the ROI of your solution.
Once the top-growing fact or detail tables and table spaces are identified, your IT organization can
then define the appropriate archiving strategies.
In-depth data growth analysis allows you to evaluate current and future data growth rates across
tables, table spaces, and schemas within your data warehouses. Figure 4 shows an example of a
data growth analysis that helps your IT organization to understand which tables and table spaces
occupy the most space. This type of analysis also helps your team proactively plan for growth in
data volumes by forecasting the estimated reduction in size from archiving inactive data (see
Figure 5).
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Tables belonging to the “Activity, Backup, and Contract Mgmt modules*” comprise 82% of all data
(920 of 1,121 GB):
Actual
Estimated
Estimated
-3 years
- 2 years
-1 year
Current
+1 year
+2 years
+3 years
Datafile (GB)
-
5,886
10,589
16,062
28,121
40,447
53,877
Data
-
411
739
1,121
1,963
2,823
3,761
2,029.2
Largest modules*
Activity
-
181.6
448.7
809.0
1,148.3
1,555.1
Backup
-
60.0
71.4
82.7
95.2
108.9
124.0
Contract Mgmt
-
6.4
14.8
27.9
346.8
665.6
984.6
Samples
-
23.4
24.7
25.9
36.9
48.0
59.3
Segmentation
-
4.2
54.5
104.9
155.2
-
-
Figure 4. From the data growth analysis, your IT organization has an inventory of the top-growing tables and schemas
in your data warehouse.
Figure 5. Data growth analysis enables your IT organization to understand the impact of data archiving strategies on
data growth in data warehouses.
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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Metadata Discovery
Each data warehouse has its own schema design, with varying relationships and constraints
between dimension and fact tables and among aggregate, fact, and dimension tables. The data
warehouse archiving solution should provide an automatic means of mining the database for
metadata about the entity schema and relationships. The archiving solution needs to be aware of
the relationships between records across tables and schemas to ensure that all related records
are relocated together or links are maintained between them. This way, data integrity is maintained
when data is archived and restored. Without an automated means of discovering this metadata,
you would need to define it manually, requiring significant configuration time before being able to
deploy the solution.
Simple Metadata Extensibility
Not all metadata can be discovered by mining the database. A data warehouse archiving solution
should provide a simple graphical interface to allow users to extend and customize the discovered
metadata. Groupings of tables into business entities and definitions of business rules specifying
the eligibility criteria for records to be archived are metadata that may not be discovered or
inferred accurately. Therefore, some user-provided guidance may be required.
With a simple graphical user interface as shown in Figure 6, you can easily view, edit, and extend
your data warehouse entity model metadata and business rules. By mining the database and
using a wizard-based interface, you can quickly discover metadata in the data warehouse and add
new attributes to augment structural metadata with rich context.
Figure 6. A simple graphical user interface lets you easily view, edit, and extend discovered metadata from your
data warehouse.
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Robust Archiving Techniques to Enable Optimal Storage Tiers
The major drivers for data warehouse archiving are usually to reduce infrastructure cost by creating
storage tiers, reduce maintenance cost, and maintain peak data warehouse performance. Simply
relocating inactive data from the production data warehouses to lower-cost servers and storage
achieves those goals, but your business requirements are likely to be more complex. You need to
consider your organization’s budget constraints and performance and access requirements when
selecting a data warehouse archiving solution.
Your IT organization will probably access archived data less frequently than active data. But you
may still have to periodically retrieve the combined archived and current data directly from the
original application interface. In this case, the data should be archived to a format that facilitates
relatively high query performance—such as another data warehouse instance, located on a lowercost infrastructure.
On the other hand, if inactive data is old and ready to be retired, you may have to access it only
rarely. In this case, access from a reporting tool, rather than from an application interface, may
be adequate. Slower query performance can be tolerated, and the data may be archived to a
more optimal, compressed format, such as a compressed file. Archiving to a compressed file
format can result in very high storage capacity saving. Depending on the data size and the level of
redundancy in data values, you may be able to achieve a compression ratio ranging from 20:1 to
60:1 compared to the original data size.
Based on the age of the data and response time as well as frequency of access, the compressed
archive file can be stored on a file system located in lower-cost storage or even storage in the
cloud, for economies of scale. As data ages and access requirements change over time, your IT
organization needs a way to convert and relocate the data from one archiving format and location
to another, enabling multiple cost-effective storage tiers.
A data warehouse archiving solution also needs to enable archiving transactional and detailed
data only, which are the fastest growing. This needs to be done while maintaining data integrity
and links to dimensional and aggregate tables that may still be stored in the production system.
Eventually, some older dimension records may also be archived as well. The data warehouse
archiving solution should know what types of tables need to be archived to support an optimal
archiving strategy. At the same time, the user should be able to define an archiving job easily
without extensive configuration or programming. Figure 7 illustrates a data warehouse archiving
strategy where detailed data are slowly relocated to another database and subsequently to a
more optimal compressed file format, which results in extreme reduction in storage capacity.
Figure 8 shows a wizard-based interface to allow users to easily define and monitor archiving jobs.
Production
Data Warehouse
(less than 2 years old)
DIM1
DIM2
DIM3
DETAIL1
DETAIL2
Archive
Data Warehouse
(2 – 7 years old)
Optimized
File Archive (40:1 compression)
(over 7 years old)
OLD_DIM2
OLD_DIM3
DETAIL 3
DETAIL 4
DETAIL 5
AGG1
DETAIL 6
AGG2
DETAIL 7
AGG2
Figure 7. A data warehouse archiving solution should offer multiple archiving formats (database or compressed file)
that enable optimal storage tiering and the flexibility to archive different types of records while maintaining data integrity
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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A data warehouse archiving solution that offers multiple archiving formats and accessibility
options allows IT organizations to determine the appropriate trade-offs among archive size,
performance, application accessibility, and cost.
Figure 8. Archive complete business entities using Informatica Data Archive.
Your IT organization must also be able to restore archive data to its original location. Otherwise,
there is no way to correct mistakes during archiving or to accommodate changes to access
requirements. If archived data later needs to become active again and for some reason modified
and annotated, then it also needs to be restored. For example, a customer order that is closed
and reopened may need to be restored because it has become active again. The data warehouse
archiving solution must be able to restore archived data at different levels of granularity, such as
selected detail records, business entities, or an entire archive.
Easy, Multiple Methods to Access Archived Data
Regardless of the archive format, archived data needs to be easily accessible either from the
original application interface or through standard interfaces for reporting. Standard SQL/ODBC/
JDBC interfaces should be available for reporting using any reporting or business intelligence tool.
The option of accessing the data from an e-discovery interface should be available if the data is to
be retired and accessed only for compliance audit purposes.
Universal Connectivity
If your organization is like many other enterprises, you have data warehouses and applications
on multiple database systems on varying operating systems. To support your enterprise needs,
your archiving solution should allow you to manage archive processes across data warehouses
and applications on diverse databases, including relational (e.g., Oracle, DB2, Sybase, SQL
Server, Teradata, Informix), mainframe (e.g., IDMS, VSAM, IMS), files, and packaged CRM and ERP
applications on open systems (e.g., Windows, Linux, UNIX) or mainframes (e.g., z/OS, AS/400).
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Integration with Other Archiving Platform, Enterprise Content
Management, and Storage Solutions
Your company may already have an archiving solution for emails and files. Your IT organization
may also have standardized on an enterprise content management ECM solution to manage your
unstructured data. To support compliance to regulatory requirements and ensure immutability and
single-instance storage of retained data, you may be using archiving platforms, such as content
addressable storage, which requires proprietary connectivity
To enable your organization to respond quickly and accurately to audit requests as well as to
cost-effectively retain data for longer periods, your archiving solution should allow you to manage
and discover archived data of all types, both structured and unstructured, centrally. You can
do so if your data warehouse archiving solution integrates with your existing archiving, content
management, and storage solutions to facilitate centralized management and e-discovery of all
types of archived data.
Informatica Data Archive: The Complete Data Warehouse
Archiving Solution
Informatica Data Archive™ helps your IT organization to cost-effectively manage the explosion of
data volumes in data warehouses. It allows IT to easily and safely archive inactive data and then
readily access it when needed. Informatica Data Archive delivers the full range of capabilities that
your IT organization needs to effectively manage data growth in data warehouses, including:
• Robust data growth assessment capabilities
• Complete metadata discovery
• Simple metadata extensibility
• Robust archiving techniques that ensure data integrity after archiving and supporting multiple
archive formats to enable optimal storage tiers
• Multiple, easy methods to access archived data
• Universal connectivity
• Integration with other archiving platforms, ECM, and storage solutions, such as Symantec,
Commvault, and EMC
Informatica Data Archive leverages the power of the Informatica Platform, the industry’s leading
data integration platform, to handle the huge data volumes typical of very large global enterprises.
The software provides superior scalability and performance, delivering data to the most costeffective storage option based on its value. It also offers unparalleled interoperability.
The software is based on an open, easily extensible architecture, enabling simple integration
with third-party solutions.
Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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Conclusion
Your IT organizations can no longer ignore the escalating costs associated with managing your
growing data volumes in your data warehouses. Traditional methods of managing data growth
address only the symptoms—not the root cause of the problem. The key to capping your IT
organization’s data management costs and risks is to relocate dormant data to lower-cost
infrastructure. This is what data warehouse archiving solutions can do for you.
Informatica Data Archive delivers the full range of capabilities that your IT organization needs
to effectively manage data growth in data warehouses. When your IT organization implements
Informatica’s complete, scalable, and flexible archiving solution, you’ll lower the total cost of
ownership of your data warehouses and other applications by:
• Reducing storage, server, software, and maintenance costs
• Improving data warehouse performance
• Increasing data warehouse availability
• Supporting compliance with internal, industry, and governmental mandates and regulations
Together, Informatica and your IT organization can align the business value of data with the most
appropriate and cost-effective IT infrastructure to manage it.
Learn More
Learn more about Informatica Data Archive and the entire Informatica Platform. Please visit us at
www.informatica.com or call 1.800.653.3871.
About Informatica
Informatica Corporation (NASDAQ: INFA) is the world’s number one independent leader in data
integration software. The Informatica Platform provides organizations with a comprehensive, unified,
open, and economical approach to lower IT costs and gain competitive advantage from their
information assets. More than 3,700 enterprises worldwide rely on Informatica to access, integrate,
and trust their information assets held in the traditional enterprise and in the Internet cloud.
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Data Warehouse Archiving: A Way to Optimize Data Warehouse Performance and Reduce Costs
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Worldwide Headquarters, 100 Cardinal Way, Redwood City, CA 94063, USA
phone: 650.385.5000 fax: 650.385.5500 toll-free in the US: 1.800.653.3871 www.informatica.com
© 2010 Informatica Corporation. All rights reserved. Printed in the U.S.A. Informatica, the Informatica logo, and The Data Integration Company are trademarks or registered trademarks of Informatica Corporation in the United States and in
jurisdictions throughout the world. All other company and product names may be trade names or trademarks of their respective owners. First Published: 2010
7082 (01/06/2010)