Data Integration Architecture – The Shift in Common Patterns
Data integration architecture in a multi-cloud world is a real challenge for enterprises today. Think how you as a consumer feel when you’re juggling multiple clouds. I went through this recently when my credit card expired, and the auto-pay transactions were declined! My phone stopped saving my photos in iCloud. I couldn’t access the shared folder of my son’s school assignments in Dropbox. And Google Drive started giving me warnings every time I opened it.
At both an individual and an enterprise level, there is a constant tug of war happening. We want best-of-breed products. But we also want a one-bill-to-pay solution.
The data integration landscape is no different. In this era of perishable, high-volume data on multi-cloud, force-fitting old processes and tools to meet new business needs won’t work. It will instead lead to inefficiency and cost overruns. Gone are the days of focusing on processing data from one system. For long-term benefits, your data integration architecture should be flexible and agile. This enables you to serve new use cases with adaptable enterprise integration solutions. To do so, you need to use the most promising data integration solutions available instead of a legacy system.
What is Data Integration Architecture?
Data integration architecture is the designing of the data pipeline. It starts with identifying the data sources, the data targets and the transformations required to meet the business objective. It can be implemented for a single data flow or the entire data connectivity within and beyond the organizations. A good data integration architecture helps to create the data flow and execute the data in an optimized way, both in terms of cost and time.
Common Architectural Data Integration Patterns
Your approach to data integration should accommodate several common architectural patterns. It should also include some emerging ones. The most time-tested pattern is ETL (extract, transform and load). This is where you have a dedicated data processing server in the middle for all your data transformation and enrichment needs. It integrates data from multiple systems without taxing the source and target applications. With modern technologies and processes, applications are sharing the responsibilities of data transformation. They’re also supporting new data integration patterns.
4 Factors Influencing Data Integration Architecture
Data warehouse and lake:
The evolution of data warehouses and data lakes has had one of the biggest influences on data integration strategy. Storage and processing of data are decoupled in modern data warehouses. This enables cheaper storage and faster processing on cloud platforms. You can regulate the storage cost with a tiering system of data, based on accessibility. Massively distributed and parallel processing speeds up the processing of any amount or kind of data.
You’ll have high volumes of high-speed data pouring in from data sources. But you don’t need to immediately process all the data before collecting it in a data warehouse. Instead, transform data on-demand in your destination system. That gives rise to an ELT framework, where you're moving data from a source system and then processing it when needed. On the one hand, this gives a lot of control to data scientists and analysts to enrich and modify data as needed. On the other hand, it reduces unwanted data processing and data egress charges. This allows architects to be more efficient by saving money and time.
Real-time integration:
Today, you need to champion real-time data and application integration to meet your customers’ expectations. Time is money. A small delay can incur huge loss in terms of money and reputation. Consider fraud detection in the banking sector. Or, think of dynamic pricing and offers for retail customers. To meet your SLAs, it’s very important to understand when you need to use data integration, data ingestion, application integration, messaging, integration hubs and/or API gateways.
Multi-cloud:
This is obvious for forward-looking companies. For both design reasons and to ensure you have best-of-breed solutions, your data integration architecture needs to be cloud-agnostic and scalable. Scalability doesn’t just mean how many instances you can spin up and down. It also means how many diverse and futuristic uses cases you can support. You can opt for an architectural pattern like data fabric or data mesh. This will provide a unified view and experience irrespective of the cloud the data resides in. For complete governance and cataloging of your data, multi-cloud data management is a must.
Autonomous data integration:
It’s undeniable that infusing artificial intelligence in your overall data and integration management system will make your team more productive and your processes more efficient. But what if you could level up and achieve autonomous data integration with machine learning and predictive analysis? Imagine if you had an optimization engine that would run on auto pilot to significantly bring down your operational costs over time?
When you architect your data integration landscape, the objective is not only to minimize short term cost and to optimize performance. You’re also looking to enable digital transformation. When you adopt a comprehensive and modern data platform like the Informatica Intelligent Data Management Cloud (IDMC), you have the flexibility to automate the old repeatable processes while preparing to absorb the “new normal” in the data space.
To learn more, I highly encourage you to join the on-demand DBTA webinar, “Architectural Patterns in Data Integration: Choices and Strategies.” Hear John O'Brien, Principal Analyst and CEO of Radiant Advisors, and Makesh Renganathan, Principal Product Manager, R&D Cloud Informatica, discuss the shift in data integration architecture.
