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An Oracle White Paper

August 2012

Best Practices for Real-time Data Warehousing

Executive Overview

Today’s integration project teams face the daunting challenge that, while data volumes are

exponentially growing, the need for timely and accurate business intelligence is also

constantly increasing. Batches for data warehouse loads used to be scheduled daily to weekly;

today’s businesses demand information that is as fresh as possible. The value of this real-

time business data decreases as it gets older, latency of data integration is essential for the

business value of the data warehouse. At the same time the concept of “business hours” is

vanishing for a global enterprise, as data warehouses are in use 24 hours a day, 365 days a

year. This means that the traditional nightly batch windows are becoming harder to

accommodate, and interrupting or slowing down sources is not acceptable at any time during

the day. Finally, integration projects have to be completed in shorter release timeframes,

while fully meeting functional, performance, and quality specifications on time and within

budget. These processes must be maintainable over time, and the completed work should be

reusable for further, more cohesive, integration initiatives.

Conventional “Extract, Transform, Load” (ETL) tools closely intermix data transformation

rules with integration process procedures, requiring the development of both data

transformations and data flow. Oracle Data Integrator (ODI) takes a different approach to

integration by clearly separating the declarative rules (the “what”) from the actual

implementation (the “how”). With ODI, declarative rules describing mappings and

transformations are defined graphically, through a drag-and-drop interface, and stored

independently from the implementation. ODI automatically generates the data flow, which

can be fine-tuned if required. This innovative approach for declarative design has also been

applied to ODI's framework for Changed Data Capture (CDC). ODI’s CDC moves only

changed data to the target systems and can be integrated with Oracle GoldenGate, thereby

enabling the kind of real time integration that businesses require.

This technical brief describes several techniques available in ODI to adjust data latency from

scheduled batches to continuous real-time integration.

Best Practices for Real-time Data Warehousing

Introduction

The conventional approach to data integration involves extracting all data from the source

system and then integrating the entire set—possibly using an incremental strategy—in the

target system. This approach, which is suitable in most cases, can be inefficient when the

integration process requires real-time data integration. In such situations, the amount of data

involved makes data integration impossible in the given timeframes.

Basic solutions, such as filtering records according to a timestamp column or “changed” flag,

are possible, but they might require modifications in the applications. In addition, they

usually do not sufficiently ensure that all changes are taken into account.

ODI’s Changed Data Capture identifies and captures data as it is being inserted, updated, or

deleted from datastores, and it makes the changed data available for integration processes.

Real-Time Data Integration Use Cases

Integration teams require real-time data integration with low or no data latency for a number

of use cases. While this whitepaper focuses on data warehousing, it is useful to differentiate

the following areas:

- Real-time data warehousing

Aggregation of analytical data in a data warehouse using continuous or near real-

time loads.

- Operational reporting and dashboards

Selection of operational data into a reporting database for BI tools and dashboards.

- Query Offloading

Replication of high-cost or legacy OLTP servers to secondary systems to ease query

load.

- High Availability / Disaster Recovery

Duplication of database systems in active-active or active-passive scenarios to

improve availability during outages.

- Zero Downtime Migrations

Ability to synchronize data between old and new systems with potentially different

technologies to allow for switch-over and switch-back without downtime.

- Data Federation / Data Services

Provide virtual, canonical views of data distributed over several systems through

federated queries over heterogeneous sources.

Best Practices for Real-time Data Warehousing

Oracle has various solutions for different real-time data integration use cases. Query

offloading, high availability/disaster recovery, and zero-downtime migrations can be handled

through the Oracle GoldenGate product that provides heterogeneous, non-intrusive and

highly performant changed data capture, routing, and delivery. In order to provide no to low

latency loads, ODI has various alternatives for real-time data warehousing through the use of

CDC mechanism, including the integration with Oracle GoldenGate. This integration also

provides seamless operational reporting. Data federation and data service use cases are

covered by Oracle Data Service Integrator (ODSI).

Architectures for Loading Data Warehouses

Various architectures for collecting transactional data from operational sources have been used to populate data warehouses. These techniques vary mostly on the latency of data integration, from daily batches to continuous real-time integration. The capture of data from sources is either performed through incremental queries that filter based on a timestamp or flag, or through a CDC mechanism that detects any changes as it is happening. Architectures are further distinguished between pull and push operation, where a pull operation polls in fixed intervals for new data, while in a push operation data is loaded into the target once a change appears. A daily batch mechanism is most suitable if intra-day freshness is not required for the data, such as longer-term trends or data that is only calculated once daily, for example financial close information. Batch loads might be performed in a downtime window, if the business model doesn’t require 24 hour availability of the data warehouse. Different techniques such as real-time partitioning or trickle-and-flip1 exist to minimize the impact of a load to a live data warehouse without downtime.

Batch Mini-Batch Micro-Batch Real-Time Description Data is loaded in full

or incrementally using a off-peak window.

Data is loaded incrementally using intra-day loads.

Source changes are captured and accumulated to be loaded in intervals.

Source changes are captured and immediately applied to the DW.

Latency Daily or higher Hourly or higher 15min & higher sub-second

Capture Filter Query Filter Query CDC CDC

Intialization Pull Pull Push, then Pull Push

Target Load High Impact Low Impact, load frequency is tuneable

Source Load High Impact Queries at peak times necessary

Some to none depending on CDC technique

1 See also: Real-Time Data Warehousing: Challenges and Solutions by Justin Langseth (http://dssresources.com/papers/features/langseth/langseth02082004.html)

Best Practices for Real-time Data Warehousing

IMPLEMENTING CDC WITH ODI

Change Data Capture as a concept is natively embedded in ODI. It is controlled by the

modular Knowledge Module concept and supports different methods of CDC. This chapter

describes the details and benefits of the ODI CDC feature.

Modular Framework for Different Load Mechanisms

ODI supports each of the described data warehouse load architectures with its modular

Knowledge Module architecture. Knowledge Modules enable integration designers to

separate the declarative rules of data mapping from their technical implementation by

selecting a best practice mechanism for data integration. Batch and Mini-Batch strategies can

be defined by selecting Load Knowledge Modules (LKM) for the appropriate incremental

load from the sources. Micro-Batch and Real-Time strategies use the Journalizing

Knowledge Modules (JKM) to select a CDC mechanism to immediately access changes in

the data sources. Mapping logic can be left unchanged for switching KM strategies, so that a

change in loading patterns and latency does not require a rewrite of the integration logic.

Methods for Tracking Changes using CDC

ODI has abstracted the concept of CDC into a journalizing framework with a JKM and

journalizing infrastructure at its core. By isolating the physical specifics of the capture

process from the process of detected changes, it is possible to support a number of different

techniques that are represented by individual JKMs:

Non-invasive CDC through Oracle GoldenGate

Oracle GoldenGate provides a CDC mechanism that can process source changes non-

invasively by processing log files of completed transactions and storing these captured

changes into external Trail Files independent of the database. Changes are then reliably

transferred to a staging database. The JKM uses the metadata managed by ODI to generate

Figure 1: GoldenGate-based CDC

J$ T

Source

Log

ODI Load

GoldenGate

Target Staging

Real-Time Reporting

Best Practices for Real-time Data Warehousing

all Oracle GoldenGate configuration files, and processes all GoldenGate-detected changes in

the staging area. These changes will be loaded into the target data warehouse using ODI’s

declarative transformation mappings. This architecture enables separate real-time reporting

on the normalized staging area tables in addition to loading and transforming the data into

the analytical data warehouse tables.

Database Log Facilities

Some databases provide APIs and utilities to process table changes programmatically. For

example, Oracle provides the Streams interface to process log entries and store them in

separate tables. Such log-based JKMs have better scalability than trigger-based mechanisms,

but still require changes to the source database. ODI also supports log-based CDC on DB2

for iSeries using its journals.

Database Triggers

JKMs based on database triggers define procedures that are executed inside the source

database when a table change occurs. Based on the wide availability of trigger mechanisms in

databases, JKMs based on triggers are available for a wide range of sources such as Oracle,

IBM DB2 for iSeries and UDB, Informix, Microsoft SQL Server, Sybase, and others. The

disadvantage is the limited scalability and performance of trigger procedures, making them

optimal for use cases with light to medium loads.

Figure 3: Trigger-based CDC

Trigger

S J$ T

Source Target

ODI Load

Figure 2: Streams-based CDC on Oracle

S J$ T

ODI Load

Log Oracle Streams

Target Source

Best Practices for Real-time Data Warehousing

Oracle Changed Data Capture Adapters

Oracle also provides separate CDC adapters that cover legacy platforms such as DB2 for

z/OS, VSAM CICS, VSAM Batch, IMS/DB and Adabas. These adapters provide

performance by capturing changes directly from the database logs.

Source databases supported for ODI CDC

Database Log-based CDC Trigger-

based CDC

JKM Oracle

GoldenGate

Database

Log Facilities

Oracle CDC

Adapters

Oracle � � �

MS SQL Server � � �

Sybase ASE � �

DB2 UDB � �

DB2 for iSeries �2 � �

DB2 for z/OS �2 �

Informix, Hypersonic

�

Teradata, Enscribe,

MySQL, SQL/MP,

SQL/MX

�2

VSAM CICS, VSAM

Batch, IMS/DB,

Adabas

�

2 Requires customization of Oracle GoldenGate configuration generated by JKM

Best Practices for Real-time Data Warehousing

Publish-and-Subscribe Model

The ODI journalizing framework uses a publish-and-subscribe model. This model works in

three steps:

1. An identified subscriber, usually an integration process, subscribes to changes that

might occur in a datastore. Multiple subscribers can subscribe to these changes.

2. The Changed Data Capture framework captures changes in the datastore and then

publishes them for the subscriber.

3. The subscriber—an integration process—can process the tracked changes at any

time and consume these events. Once consumed, events are no longer available for

this subscriber.

ODI processes datastore changes in two ways:

- Regularly in batches (pull mode)—for example, processes new orders from the

Web site every five minutes and loads them into the operational datastore (ODS)

- In real time (push mode) as the changes occur—for example, when a product is

changed in the enterprise resource planning (ERP) system, immediately updates the

on-line catalog

Processing the Changes

ODI employs a powerful declarative design approach, Extract-Load, Transform (E-LT),

which separates the rules from the implementation details. Its out-of-the-box integration

interfaces use and process the tracked changes.

Developers define the declarative rules for the captured changes within the integration processes

in the Designer Navigator of the ODI Studio graphical user interface—without having to code.

In ODI Studio, customers declaratively specify set-based maps between sources and targets, and

then the system automatically generates the data flow from the set-based maps.

Figure 4: The ODI Journalizing Framework uses a publish-and-subscribe architecture

Order #5A32

Orders

Order #5A32

CDC

Consume

Capture/Publish

Consume

Integration Process 2

Target 1

Target 2

Integration Process 1

Best Practices for Real-time Data Warehousing

The technical processes required for processing the changes captured are implemented in

ODI’s Knowledge Modules. Knowledge Modules are scripted modules that contain database

and application-specific patterns. The runtime then interprets these modules and optimizes

the instructions for targets.

Ensuring Data Consistency

Changes frequently involve several datastores at one time. For example, when an order is

created, updated, or deleted, it involves both the orders table and the order lines table. When

processing a new order line, the new order to which this line is related must be taken into

account.

ODI provides a mode of tracking changes, called Consistent Set Changed Data Capture, for

this purpose. This mode allows you to process sets of changes that guarantee data

consistency.

Best Practices using ODI for Real-Time Data Warehousing

As with other approaches there is no one-size-fits-all approach when it comes to Real-Time

Data Warehousing. Much depends on the latency requirements, overall data volume as well

as the daily change volume, load patterns on sources and targets, as well as structure and

query requirements of the data warehouse. As covered in this paper, ODI supports all

approaches of loading a data warehouse.

In practice there is one approach that satisfies the majority of real-time data warehousing use

cases: The micro-batch approach using GoldenGate-based CDC with ODI. In this

approach, one or more tables from operational databases are used as sources and are

replicated by GoldenGate into a staging area database. This staging area provides a real-time

copy of the transactional data for real-time reporting using BI tools and dashboards. The

operational sources are not additionally stressed as GoldenGate capture is non-invasive and

performant, and the separate staging area handles operational BI queries without adding load

to the transactional system. ODI performs a load of the changed records to the real-time

data warehouse in frequent periods of 15 minutes or more. This pattern has demonstrated

the best combination of providing fresh, actionable data to the data warehouse without

introducing inconsistencies in aggregates calculated in the data warehouse.

Best Practices for Real-time Data Warehousing

Customer Use Case: Overstock.com

Overstock.com is using both GoldenGate and ODI to load customer transaction data into a

real-time data warehouse. GoldenGate is used to capture changes from its retail site, while

ODI is used for complex transformations in its data warehouse.

Overstock.com has seen the benefits of leveraging customer data in real time. When the

company sends out an e-mail campaign, rather than waiting one, two, or three days, it

immediately sees whether consumers are clicking in the right place, if the e-mail is driving

consumers to the site, and if those customers are making purchases. By accessing the data in

real time using Oracle GoldenGate and transforming it using Oracle Data Integrator,

Overstock.com can immediately track customer behavior, profitability of campaigns, and

ROI. The retailer can now analyze customer behavior and purchase history to target

marketing campaigns and service in real time and provide better service to its customers.

Conclusion

Integrating data and applications throughout the enterprise, and presenting a consolidated

view of them, is a complex proposition. Not only are there broad disparities in data

structures and application functionality, but there are also fundamental differences in

integration architectures. Some integration needs are data oriented, especially those involving

large data volumes. Other integration projects lend themselves to an event-oriented

architecture for asynchronous or synchronous integration.

Changes tracked by Changed Data Capture constitute data events. The ability to track these

events and process them regularly in batches or in real time is key to the success of an event-

driven integration architecture. Oracle Data Integrator provides rapid implementation and

maintenance for all types of integration projects.

Figure 5: Micro-Batch Architecture using ODI and GoldenGate

Operational Source(s)

Log

Periodical ODI Load

GoldenGate

Real-Time Data Warehouse

Staging Area for Operational BI

Operational BI / Real-Time Reporting

Best Practices for Real-time Data Warehousing

August 2012

Author: Alex Kotopoulis

Oracle Corporation

World Headquarters

500 Oracle Parkway

Redwood Shores, CA 94065

U.S.A.

Worldwide Inquiries:

Phone: +1.650.506.7000

Fax: +1.650.506.7200

oracle.com

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