Best Practices in Leveraging a Staging Area for SaaS-to-Enterprise Integration

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1 white paper Best Practices in Leveraging a Staging Area for SaaS-to-Enterprise Integration David S. Linthicum Introduction SaaS-to-enterprise integration requires that a number of architectural calls are made around the process of integration, including the frequency of data movement, the mechanism to maintain data integrity, and the underlying integration technology and location of that technology. When approaching SaaS-to-enterprise integration, there are many architectural options. These options include the direct migration of information from source to target, or the use of a staging area to process information more thoroughly from source to target. A staging area is a relational database that exists within the firewall. Staging areas are a place to store temporary data for import, and thus allow you to check and alter this data before committing it to the final target database. Clearly, the use of a staging area as an approach to integrating SaaS-based systems to enterprise-based systems provides a number of advantages, including the ability to better maintain data integrity and data quality, as well as providing better control over the data. However, a staging area approach to integration requires that the data integration architect understand both the core notions and best practices of SaaS-to-enterprise integration. Moreover, the architect must leverage the right technology. In this paper we ll look at the notion of leveraging a staging area for SaaS-to-enterprise integration, which includes suggesting a methodology that leverages best practices. We ll examine the value of this approach, as well as provide information around the proper enabling technology. Integration Patterns Those who deal with SaaS-to-enterprise integration need to deal with a number of issues, including semantic mediation, data quality, interface mediation, routing, filtering, and other logical operations on the data moving from SaaS-to-enterprise, or enterprise-to-saas. The idea is to make the data as useful as possible when it reaches the target system, including any business intelligence operations that need to occur and/or any business operations on that data, on-premise or SaaS-resident.

2 Thus, in order to figure out the best data integration approach, you first need to understand your own integration requirements and your existing application and data infrastructure. This includes the usage of the data when placed on the target system, and how that maps from the source. To that end, there are two basic patterns emerging around data integration for SaaS-to-Enterprises. They include Direct integration and Staging integration. Direct Direct integration, as depicted in Figure 1, is really about moving information from one data source and data schema to another, and translating the differences in semantics from the source to the target system. While we depict databases here, this could as easily be data bound to enterprise applications, such as ERP or CRM, or data bound to SaaS-based applications, such as Salesforce.com. The idea is to extract the information found in the source system, such as customer information, and place it in the target system using whatever native schema the target system uses, and translate the differences in schema and content, as needed and on the fly. This allows information to flow from one system to another without having to change either the source or the target system. The Direct integration approach accounts for the differences in schema using a translation and routing engine that exists between two systems. This is more along the lines of traditional integration, as has been defined for years. Figure 1: Direct integration simply moves information from source to target (e.g., SaaS-to-Enterprise), translating and transforming the data on the fly. While this is okay for simple data integration needs, it falls short when the integration requirements are more complex. [ 2 ]

3 Direct integration is typically leveraged when quick and dirty information integration needs to occur on operational-type data, usually from one system to another. For instance, updating the on-demand CRM system with customer data entered in the sales order entry system, which is on-premise. Direct integration is characterized by: Limitations in supporting complex data operations, generally doing very simple data operations such as transformation and routing. More fine grained, meaning the data sets are usually small and repeat. Operational focused, or when data needs to flow between systems to support ongoing light transactions. Staging In contrast to direct integration, a staging approaching to integration supports more complex and valuable data integration operations, including support for many large data sets and data operations that are more complex and of higher value. Using a staging area, or a temporary location where the data from the source system or systems is replicated, provides a logical location to perform complex operations on the data that would be difficult if not impossible to do when using direct integration approaches. An example would be extracting data from multiple enterprise data stores, manipulating the data as to structure and content, performing complex operations such as data replication, data aggregation, and data cleansing, and then posting the data to a SaaS-based application such as Salesforce.com. Or, perhaps doing the same type of data integration in reverse order, from SaaS to the enterprise. Leveraging a staging area for integration is characterized by: The ability to perform more complex operations on data, including complete transformation of semantics and the data content using any number of dimensions since, in essence, you operate on an intermediary database that you control completely. The ability to leverage more coarse grained and complex data sets that may not always repeat. Informational focused, supporting valuable information externalization approaches, including business intelligence. More flexibility around business cycles, data processing cycles, widely disbursed systems, and hardware and network limitations, where it may not be feasible to extract all operational databases at the same time. The ability to better support complex database functions, including replication, cleansing, and aggregation. [ 3 ]

4 As Figure 2 depicts, the use of a staging area is really about the gathering of information from many different sources that leverage different structures, data content, and operations on that data. For instance, within some enterprise systems you maybe able to extract sales data on a daily basis. However, when considering financial data, daily extracts are not suitable or not possible, since financial data typically requires a month-end reconciliation process. You may find similar things in geographically disbursed data, such as extracting sales data from London and New York at the same time, when the data is posted at different times of the day due to time zone differences. Figure 2: The use of a staging area is really about gathering the information from many different sources and creating a single, unified view of the data for integration to a SaaS-based systems, such as Salesforce.com. [ 4 ]

5 Defining the Value Considering the advantages of leveraging staging for SaaS-to-enterprise integration, there are many business advantages over more traditional integration approaches, including: The cost advantage of keeping volatility within a single domain. The value of agility, as related to the ability for the integration patterns to change as the needs of the business change. The value of customized complex views for specific systems and users. The value of leveraging clean, reliable data. The cost advantage of keeping volatility within a single domain refers to the fact that, since a staging area is where all of the data from different sources is temporary housed, using different formats and different time sensitivity, then any changes made to the source and/or target systems are handled within the staging area. This is especially useful when considering enterprise-to-saas integration, since the dynamic nature of the information on both the enterprise and SaaS sources and targets constantly changes, in terms of structure, content, and the interfaces. All of these changes can be abstracted within the staging area which is able to adjust using a configuration mechanism, rather than force expensive redevelopment. The value of agility is an outcome of keeping volatility in the single domain of the staging area, since the more a company changes, and thus requires changes to the enterprise and SaaS-delivered systems, the more value agility brings. Remember, it s agility that allows enterprises to adjust to take advantage of new market trends, or quickly reposition the business to avoid the negative and take advantage of the positive. Thus, the value of agility is substantial, considering that the ability to quickly move into a new market could make the company millions of dollars in the forthcoming year, or provide the ability to move quickly away from an unprofitable business process to avoid huge losses. The value of customized, complex views for specific systems and users refers to the fact that, when you use a staging area, you re able to provide the information that s needed to the system that needs it, customized for users specific needs. An example would be the ability to extract customer sales information from a SaaS-based system and credit information from a credit reporting system. Through the use of a staging area where the information is combined, it is then updated using a specific format into a sales reporting system that is leveraged by the executives within the company. This allows the information to be combined and presented to the target system, in this case, the target customer reporting systems, using views of the information that are specific to that system. Finally, the value of leveraging clean, reliable data that goes through a cleansing and validation process insures there is no missing or invalid information. The value of this is obvious. [ 5 ]

6 Leveraging a Staging Area Best Practices In order to do effective staging for enterprise-to-saas problem domains, we need to follow a few basic steps. These steps include: Understand the source and target data. Define the data staging approach. Design the data staging operations. Implementation and testing (see Figure 3). Figure 3: For SaaS-to-Enterprise data staging integration, you need to carefully consider the approach and the design, after understanding your own requirements and existing infrastructure. Testing needs to occur as well, in order to validate the solution. [ 6 ]

7 Understand the source and target data refers to the process of understanding all source and target systems at a semantic level, meaning what the data means in the larger context of the enterprise architecture. This is important since integration requires that we have an understanding of the source information in order to perform the operations that will be completed within the staging area, including aggregation, replication, cleansing, and transformation. Define the data staging approach refers to the process of creating a high-level approach to the data staging for your particular problem domain, including definition of the core purpose of the data staging operations, and a description of the transformation, replication, cleansing, and aggregation operations that need to occur. This describes the What needs to be done step. Design the data staging operations refers to what happens to the data as it s fed from the operational data sources, including consumption of the information into a common format, and the manipulation of the data for use by the target system, as defined by the approach we created in the previous step. This describes the How something is done step. Implementation and testing refers to implementing the staging area and making sure that it works as designed, and is able to maintain integrity of the data as it s manipulated within the staging area. This typically requires the creation of a test plan, test data, and acceptance testing before the staging area is placed into production. [ 7 ]

8 Leveraging Informatica PowerExchange for Salesforce CRM Informatica PowerExchange for Salesforce CRM lets you use Informatica PowerCenter to migrate, replicate, cleanse, and synchronize your Salesforce CRM or Force.com application data, with your on-premise enterprise business applications and databases (see Figure 4). PowerExchange takes advantage of the staging approaching to integration, as defined in this paper. Figure 4: Informatica PowerExchange for Salesforce CRM [ 8 ]

9 Benefits of using PowerExchange for Salesforce CRM include the reduction of development costs due to the configuration and visual approach that this technology provides. Thus, there is an increase in IT productivity since it reduces or eliminates coding to the Salesforce.com API. Because you capitalize on the value of data integration through the use of staging, you take full advantage of your investment in Salesforce.com. Other features of PowerCenter include: Execute bidirectional Salesforce integration without requiring code. Bring Salesforce data within your corporate firewall. Use one tool to transform and cleanse data before delivering it to your on-premise enterprise software and databases. Pass Salesforce data directly from source to target without intermediate staging in message queues. Simplify the tasks of migrating, replicating, cleansing, and synchronizing Salesforce data across the Internet with business analytics applications or master data hubs. Distill new and updated Salesforce data from unchanged background data, and send only the changes for further processing. Receive Salesforce data in a batch on any chosen schedule, or as a real-time stream of updates, all with a common architecture, and all without coding or scripting. Support any enterprise data integration initiative involving Salesforce data. Scale to support Web services in SOAs, event-driven architectures, and traditional data integration techniques on multiple platforms. Choose your preferred technology and evolve gradually as conditions change. [ 9 ]

10 Leveraging Informatica On-Demand When it comes to populating the staging area, you should consider Informatica On-Demand. Informatica On-Demand allows you to leverage a staging area, either to or from the SaaSdelivered applications, such as Salesforce.com. The facilities of Informatica On-Demand allow you to use this staging area without a significant amount of data transformation, and it is easily leveraged by the application administrator. Moreover, by leveraging Informatica On-Demand, the application administrator is able to focus on the staging area, and not the transformation. The transformation is carried out by mechanisms introduced by the developer, leveraging tools such as PowerCenter (discussed previously). This allows for the implementation of strict validation rules and logic required by the target systems. Therefore, the use of both Informatica On-Demand and PowerCenter frees IT from the requirements of understanding the natives of a particular application, and thus changes that are requested by the end user (see Figure 5). Informatica is the only vendor that provides the technology to serves both needs, and thus is one-stop-shopping for those looking to do data integration, from SaaS-to-enterprise, leveraging a staging area. Figure 5: The use of both Informatica On-Demand and PowerCenter frees IT from the requirements of understanding the natives of a particular application, and changes that are requested by the end user. Informatica is the only vendor that solves both of those problems. [ 10 ]

11 Data Staging Taking Center Stage As we learned in this paper, the need for data integration to support integration from and to SaaS-based applications, such as Salesforce.com to on-premise information systems, is a core need as the interest in SaaS and cloud computing grows. However, those who look to address this integration using more traditional, tactically-oriented tools are doomed to replicate improperly processed information to the target system that receives it. The use of staging as an approach to data integration provides the data or enterprise architect with the ability to perform complex processing on an intermediate form of the data, which in turn provides a location to gather the data when it s ready to be gathered, aggregates the data so it has better business meaning, and cleans the data where needed to insure data quality. In essence, staging provides a location where value can be added to the data as it moves from on-premise to SaaS, and from SaaS to on-premise. [ 11 ]

12 About the Author David Linthicum (Dave) is an internationally known Enterprise Application Integration (EAI), Service Oriented Architecture (SOA), and cloud computing expert. In his career, Dave has formed or enhanced many of the ideas behind modern distributed computing including EAI, B2B Application Integration, and SOA, approaches and technologies in wide use today. Currently, Dave is the founder of David S. Linthicum, LLC, a consulting organization dedicated to excellence in SOA product development, SOA implementation, corporate SOA strategy, and leveraging the cloud computing. Dave is the former CEO of BRIDGEWERX, former CTO of Mercator Software, and has held key technology management roles with a number of organizations including CTO of SAGA Software, Mobil Oil, EDS, AT&T, and Ernst and Young. Dave is on the board of directors serving Bondmart.com, and provides advisory services for several venture capital organizations and key technology companies. In addition, Dave was an associate professor of computer science for eight years, and continues to lecture at major technical colleges and universities including the University of Virginia, Arizona State University, and the University of Wisconsin. Dave keynotes at many leading technology conferences on application integration, SOA, Web 2.0, cloud computing, and enterprise architecture, and has appeared on a number of TV and radio shows as a computing expert David S. Linthicum, LLC 6986 (06/04/2009) [ 12 ]

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