Cloud Data Integration and Why You Might Need It
While a business generates more and more data, the need to extend data storage capacity and extract insights from unclassified information grows as well. Cloud data integration is a viable strategy for gathering all your data in a single place and making it work to benefit your business.
What is Cloud Data Integration?
Cloud data integration is when you gather all the data you have in your public, private or hybrid clouds and on-premise storage in one place. In more specific terms, cloud integration is a system of tools and technologies used to connect various applications, systems, repositories and IT environments and ensure the real-time exchange of data and processes. Other terms are cloud data integration, cloud system integration, cloud-based integration and iPaaS.
The goal of cloud data integration is to create a unified, easily accessible, secure and effective storage system for all the data. Unified data and integrated cloud applications and services can be accessed by multiple devices over a network or via the internet, ensuring a streamlined data flow.
To configure many application programs to “talk to each other” or share data in the cloud, you have to use a cloud integration platform (for example, the Oracle cloud data integration solution), plus get in touch with a cloud data integration solutions vendor for professional assistance and support.
The possible uses and purposes of cloud data integration are:
- Breaking down data silos to improve the visibility of information in the organization. Unreadable or inaccessible data means that your employees cannot see a broader picture of your business and cannot get insight out of the data your company has already collected. Cloud integration can be a good optical tool to see the whole picture.
- Unifying information components to normalize the data and provide business intelligence tools to access marketing data and enhance productivity and efficiency.
- Improving connectivity and visibility to adapt to changes and innovations in the business world.
- Optimizing business processes using accurate real-time data necessary for timely reaction to challenges, as well as adaptation to changes in customer needs and behaviors.
Cloud Data Integration Types
There are several approaches to cloud integration. In each case, the process of integration in the cloud can involve creating cloud-to-cloud data integration, cloud-to-on-premises integration or a combination of both, addressing different business components, including data and applications. Choosing the right strategy depends on the data itself, your cloud data integration project specifics and your future vision for data management and analysis.
There are three main types of data integration.
- Data consolidation. This approach is based on extract, transform, load (ETL) technology. In this case, the data is consolidated from several sources, converted to a single format and loaded to new storage.
- Data propagation. This process is simply transferring data from one storage place to another; it can be synchronous or asynchronous.
- Data virtualization. In this case, the data can only be accessed from a single place, but it will still be stored in separate locations.
There are four common ways to integrate data in the cloud:
- A cloud integration hub – connects and shares data across Software-as-a-Service (SaaS) applications, cloud ecosystems and on-premises applications. Pros of this model: greater agility and efficiency than traditional point-to-point data integration, elimination of redundant and costly cloud synchronizations, synchronization of complex data processing, the possibility of self-service publication and consumption of data, and decoupling source and target applications.
- Serverless data integration – reduces operational costs and simplifies deployment as the data integration job is performed by computer clusters. Pros of this model: no server management, consumption-based pricing, simplified monitoring of integration jobs.
- Data ingestion – allows collection and management of the expanding variety of data sources, formats and protocols. This model refers to files, streaming and databases. Pros: multi-latency data management, filtering and managing data drift from high-performance streaming and edge data processing.
- Business-to-business (B2B) partner integration – cuts operational costs, reduces the need to devote developers to B2B data integration projects. Pros of the model: accelerates onboarding for customers and partners, mitigates the complexity of managing non-standard data originating in systems outside your control.
Benefits of Cloud Data Integration
Cloud to cloud integration, as well as cloud systems integration, delivers the following business benefits:
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- Financial and operational cost reduction. The employees save a lot of time with the simplification of data access and management. Plus, smart data storage in a single place allows for consuming only the capacity and computing power your business needs.
- Data synchronization. This is the core goal of cloud data integration, which is to make all your data classified, synchronized and stored in one place. Such a practice adds a lot of clarity to cloud data management since you get complete control over your data, access rights and use cases. Cloud data integration ensures that the same information on all systems is present and updated in real-time with no duplicate records on different systems having inconsistent data. Data synchronization ensures the elimination of errors in analysis and decisions based on incorrect data.
- Data compliance. Companies that deal with sensitive information need to store and maintain customer data according to industry standards such as HIPAA, GDPR and PCI DSS to ensure the security of this sensitive information. Cloud integration solutions allow businesses to set up workflows that help meet the requirements for enterprise data.
- Data modernization. Legacy systems present at many companies have accumulated many years worth of data pools, and that data must be transformed and migrated to shift to modern cloud systems. Cloud data integration services and tools can transform legacy data and load it to the desired cloud destination.
- Removing duplicate data. When a business uses several data storages, some pieces of data are duplicated. Cloud application integration allows you to remove data duplicates and decrease the storage capacity.
- Workflow automation. Managing your data from single storage allows your staff to avoid manual data copying and data entry, which is prone to human error and time-consuming. It also allows you to generate analytical reports automatically from all the available data.
- Better opportunities for data analysis. When all your data is classified and stored in a single place, it opens up opportunities for comprehensive data analysis and allows you to extract insights on the effectiveness of your business and customer behavior that may be used for business growth development strategies.
- Business scalability. Cloud data integration ensures automation of processes and allows businesses to manage any volume of data with workflows and powerful ETL engines by elimination of data silos.
Cloud Data Integration Challenges
No matter how attractive the concept of integrating cloud applications and your data is, you should be aware of particular challenges you may face. Here are the most common issues when integrating data between cloud systems or between cloud and on-premise systems:
- Data movement accuracy. Moving high volume data to or from the cloud or between cloud and on-premise systems while ensuring data accuracy is not as easy as it may seem. Cloud data integration won’t work without comprehensive strategies, so the migration can be error-free while meeting the frequency requirement of the data transfer.
- No standardization. Integrating data between cloud systems or cloud and on-premise systems has no standard approaches or protocols. Each cloud platform, service or resource has different data schemas and formats. Of course, there are data connectors or adaptors. However, you or your integration partner have to update them constantly with every new cloud service introduction, application update, or modification.
- ETL. With traditional data integration projects, there are complex Extract Transform Load (ETL) workflows to clean data and transform it into the precise format needed by target systems. Many cloud systems work with unstructured data or provide a flexible data model for structured data by using a cloud-based data integration service or software that replaces manual jobs with automation and helps simplify the complete ETL process. However, they still need data to be cleaned, treated and converted into the desired format, so you must have integration strategies that consider how ETL can be performed without slowing down the integration or adding a lot of complexity.
- Choosing the right cloud integration software. Choosing the right tool for a business use case is one of the most significant matters when setting up a cloud data integration automation platform. You have to choose the cloud integration solution that meets every requirement of your particular use case so that the company does not need another tool to meet the remaining needs.
Cloud Data Integration FAQs
Cloud Data Integration vs Cloud Integration – Is There a Difference?
From a technical perspective, cloud data integration is used when companies need to combine and analyze data in databases that are located in a cloud, or to combine such databases in a cloud. Cloud integration is used when you need to combine cloud services to interact with data that is changing in real-time, and often it is performed using out-of-the-box third-party solutions. So there is a difference.
Is Cloud Data Integration the Same As Cloud-Based Data Integration Platform as a Service (IPaaS)?
No, not quite. Cloud data integration is a data management strategy aimed at giving the business more flexibility and control over their data, while IPaaS is a tool used in the process of cloud data integration, a kind of engine that drives cloud data integrations.
An iPaaS, or integration Platform as a Service, is a platform that standardizes how applications are integrated into an organization, making it easier to automate business processes and share data across applications. As a rule, iPaaS solutions feature guaranteed data delivery, error handling, endpoint connectors for specific applications and universal communication protocols, data governance, lifecycle management and developer tools enabling deep customization and complexity when building integration flows.
The global Integration Platform as a Service (iPaaS) market value is approximately $1.9 billion, and it is expected to reach $10.3 billion by 2025, with a growth rate of more than 40% per year.
Is Cloud Data Integration the Same as Cloud Surfing?
Cloud surfing is the process of transferring data from one cloud to another, while cloud data integration is consolidating data from different clouds and on-premise storage.
Conclusion
While the process may seem like an easy task, it requires a clear plan and strategy in practice, plus strong technical expertise to help you create a holistic data infrastructure from several data sources. If you consider transitioning to a hybrid environment of cloud, SaaS or moving to the cloud totally, you need assistance from partners who understand the technical aspects and how to create a new organizational model.
Cprime has a lot of hands-on experience in cloud data integration, data migration, and legacy system modernization projects. We would be happy to assist you with these projects, so get in touch with us to discuss your business data needs!
