Data Integration is a comprehensive process that brings together data from diverse and diverse interacting sources in one place and presents a unified view in order to make faster and accurate decisions.
Today, the increasing volume and diversity of data has made it almost impossible to integrate all this data in a timely manner to support situations with business needs.
Informatica Cloud Data Integration allows you to do all these processes faster, more agile and flexible. With Informatica, you have access to everything you need to streamline Data integration with up to 65% less cost of ownership and up to 80% less time.
Why should I integrate my data into the cloud?
Data integration in an Information Technology environment on a multi-cloud harmoniously combines all moving parts from sources into a single whole. Whether you're consolidating customer data to better understand your customers, or building a cloud data warehouse or cloud data lake to run complex real-time analytics, cloud data integration meets these two key needs.
What Are the Benefits of Cloud Integration?
Agility
Cloud data integration allows you to adapt and deploy new integration patterns faster to keep pace with market changes and evolving business requirements.
You can quickly connect to both on-premises data sources and cloud applications to seamlessly integrate high-volume data, enabling your business analysts, data scientists and data officers to have a role-appropriate user experience when they need them.
Velocity
Cloud data integration allows you to build and run advanced integrations at high speed and scale. It allows you to connect hundreds of applications and data sources and build enterprise-scale workload integrations in hours or even minutes using pre-built templates instead of lengthy and cumbersome hand-coding.
Flexibility
Cloud data integration makes it easy to deploy, and manage complex data integration mapping processes, workloads by leveraging enterprise-level performance and reliability of instances. A serverless Spark engine with dynamic scaling and auto-tuning features allows you to process big data without having to manage servers.
How to Integrate Your Data into the Cloud
Cloud Integration Center
A cloud integration center connects and shares data between SaaS applications and on-premises applications. It provides greater agility and efficiency than traditional data integration approaches while eliminating unnecessary and costly cloud synchronizations. A cloud data integration center can organize complex data processing, enable self-service processes, and separate source and target applications.
Serverless Data Integration
Elastic compute clusters reduce operating costs and simplify your deployment processes. When sending data merge jobs to a cluster, the data scales to the workload. This process allows consumption-based pricing while eliminating server management and simplifies monitoring of integration processes.
Business to Business (B2B) Partner Integration
A cloud-based B2B gateway allows you to quickly establish partnerships, define communication protocols, monitor, manage EDI and other standard message exchanges, and process business partner messages on your back-end systems. It helps you reduce the complexity of managing data arising in systems beyond your control, reducing operating costs.
What are Cloud Data Integration Use Scenarios?
A leading biotech firm has overcome bottlenecks caused by manual workflows and problems by integrating existing business data into a cloud data warehouse. The agile, cost-effective, scalable cloud data integration solution enabled both self-service analytics and operationalized reporting, while saving business hours every day, while helping the company generate more revenue and move updates faster across the development line.
Northern Arizona University, a public institution, wanted to offer a seamless, connected experience across all university departments, including real-time access to student information by its academic staff. Cloud data integration allowed student information to be consolidated between departments and on-premises cloud records, then upload the data and deliver it to users faster.
İlginizi Çekebilecek Diğer İçeriklerimiz
Veri analisti (Data Analyst), verileri toplayan, analiz eden ve bu verilerden anlamlı içgörüler çıkararak işletmelere stratejik kararlar almalarında yardımcı olan bir profesyoneldir.
Makine Öğrenimi Mühendisi (Machine Learning Engineer), veri analizi ve yapay zeka algoritmalarıyla çalışan, makinelerin öğrenmesini ve veri odaklı kararlar almasını sağlayan sistemleri geliştiren bir profesyoneldir. Bu mühendisler, istatistik, programlama ve veri bilimi becerilerini kullanarak, iş süreçlerini otomatikleştiren ve optimize eden çözümler oluşturur.
