What is Cloud Data Integration?
Data integration involves collecting data from human interactions, applications, machines, and other sources, then bringing it together in one place to synthesize a comprehensive business view that powers rapid, accurate decisions. However, the expanding volume, variety, and velocity of data has made it nearly impossible to integrate all this data in a timely fashion to support business needs.
Why Should I Integrate My Data in the Cloud?
In a multi-cloud IT landscape, data integration binds all the moving parts into a cohesive whole. Whether you’re combining customer data and order management data to better understand your customers or building a cloud data warehouse or cloud data lake to drive complex real-time analytics, cloud data integration delivers on these three key needs:
Cloud data integration allows you to adapt and deploy new integration patterns faster to keep up with market changes and evolving business requirements. You can connect quickly to both on-premises data sources and cloud applications to seamlessly integrate high volumes of data—and ensure that your business analysts, data scientists, data stewards, and citizen integrators have a role-appropriate user experience when they need it.
Cloud data integration enables you to build and run advanced integrations at speed and scale. Using pre-built templates and reusable mappings instead of cumbersome handcoding lets you connect hundreds of applications and data sources and build enterprise-scale integration workloads in hours or even minutes.
Cloud data integration makes it easy to process complex data integration mapping tasks, and deploy and manage modern workloads, by leveraging the enterprise-grade performance and reliability of cloud computing. A serverless Spark engine with dynamic scaling and auto-tuning lets you process big data without having to manage servers.
4 Ways to Integrate Data in the Cloud
A cloud integration hub
A cloud integration hub connects and shares data across Software-as-a-Service (SaaS) applications, cloud ecosystems, and on-premises applications. It provides greater agility and efficiency than traditional point-to-point data integration approaches while eliminating redundant and costly cloud synchronizations. An integration hub can orchestrate complex data processing, enable self-service publication and consumption of data, and decouple source and target applications.
Serverless data integration
Elastic compute clusters reduce operational costs and simplify deployment. As data integration jobs get pushed to a cluster for processing, the cluster scales up or down based on the workload and shuts down when processing is complete. This eliminates server management, allows for consumption-based pricing, and simplifies monitoring of integration jobs.
Cloud-based centralized mass ingestion works in conjunction with traditional, batch-oriented data collection processes to let you collect and manage the expanding variety of data sources, formats, and protocols. It supports multi-latency data management while filtering and managing data drift from high-performance streaming and edge data processing. Data ingestion supports sources such as files, streaming, and databases.
Business-to-business (B2B) partner integration
A cloud-based B2B gateway lets you quickly set up business partners, define communication protocols, monitor and manage EDI and other standard message exchange, and process trading partner messages in your backend systems. It helps you accelerate onboarding for customers and partners, mitigate the complexity of managing non-standard data originating in systems outside your control, cut operational costs, and reduce the need to devote developers to B2B data integration projects.
How to Start Integrating Data in the Cloud
Avoid the mistakes of the past
You already know what happens when you use handcoding, try to stitch multiple disjointed point products into a single end-to-end solution, or expect to get advanced functionality from vendors whose solutions only include basic capabilities: you create processes that are expensive, difficult to maintain, require skilled developers, aren’t reusable, and don’t deliver the results you need. You’ve learned to solve those challenges for on-premises data warehousing and data lakes. Now you need to apply what you’ve learned to the cloud.
Choose modern cloud data integration tools
Look for a comprehensive, intelligent cloud-native data integration platform that speeds data discovery, automatically parses complex files, and uses AI to analyze metadata and make transformation recommendations. This makes it easy to discover data to ingest into your cloud data warehouse and data lake, then reuse the data pipelines for other projects. This AI-powered platform should be microservices-based and API-driven, and should incorporate serverless data integration, mass ingestion, and metadata management. Additionally, it should be built for enterprise-grade deployments with a scalable, end-to-end solution that lets you add data quality and data management capabilities as needed.
Choose an independent vendor
Select an infrastructure-neutral vendor whose solution is built to integrate data from any cloud infrastructure and apply it to any ecosystem. This makes it easier to create a seamless multi-cloud infrastructure and shift workloads from one cloud data warehouse, data lake, platform, or ecosystem to another as needed.
Cloud Data Integration Success Stories
This legendary baseball team wants to deliver experiences that create lifelong fans while maximizing revenues. By using cloud data integration to create a single view of every fan across multiple systems, the team is introducing new lines of revenue, strengthening fan loyalty with more engaging experiences, and making faster, more profitable decisions about ticket and product pricing.
Leading Biotechnology Company
A leading biotechnology firm replaced the bottleneck of manual workflows and processes with the ability to integrate immunosequencing and business data in a cloud data lake and cloud data warehouse. Its agile, cost-effective, scalable cloud data integration solution enables both self-service analytics and operationalized reporting, saving hours of work each day while helping the firm recover more revenue and move innovations through the development pipeline faster.
Northern Arizona University
As a public institution, Northern Arizona University wanted to give students a seamless, connected experience across all university departments, including a more efficient, responsive student service center and real-time access for academic advisors to student information. Cloud data integration lets it unify student information across departments and between on-premise and cloud records, then load and deliver data faster to users. This holistic view of students also lets the university reach out proactively to students whose data suggests they need academic help.
Getting Started with Cloud Data Integration
Informatica’s cloud data integration solution provides the critical capabilities that are key to centralizing data in your data cloud data warehouse and cloud data lake:
Rapid data ingestion and integration with an intuitive visual development environment with Informatica Cloud Mass Ingestion
Pre-built cloud-native connectivity to virtually any type of enterprise data, whether multi-cloud or on-premises with Informatica Connectors
Critical optimization capabilities such as pushdown optimization for efficient data processing
Serverless-based Spark processing for scalability and capacity on demand with Informatica Cloud Data Integration-Elastic
Intelligent data discovery, automated parsing of complex files, and AI-powered transformation recommendations
Find out more about cloud data integration as part of our industry-leading, metadata-driven cloud lakehouse data management solution, which includes metadata management and data quality in a cloud-native cloud data management platform.
More Cloud Data Integration Resources
TDWI Checklist Report: Pathways to Cloud Data Warehouse Modernization
Solution Brief: Solving Multicloud and Hybrid Data Management Challenges with iPaaS
Blog: From Hand Coding to Automated, Intelligent Data Integration