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Cloud Data Integration: Benefits, Examples, and Why it Matters

By Posted in Data Integration March 10, 2022

This post updated on November, 29, 2023

Try and imagine removing the cloud from business. It would bring the world to a screeching halt. Yet only a decade ago, most enterprise leaders were resisting the move. The stats show us clearly who won that debate.

We’re all aware that the growth in cloud is explosive and that most businesses today are rapidly moving to a hybrid-cloud approach. However, that doesn’t mean there aren’t challenges for businesses incorporating cloud into their business.

Following a massive push for moving operations into the cloud, many organizations are realizing their critical data has become siloed into various cloud environments. These silos prevent businesses from using this vital data at scale. Uh oh.

With such exponential growth in cloud adoption and unfathomable amounts of data residing in disparate environments, what exactly is a business to do to ensure data is accessible and usable?

It all comes down to cloud data integration.

What Is Cloud Data Integration?

Cloud data integration is the process of consolidating data from disparate systems – public cloud, private cloud, and on-premise – into a unified data store like a data warehouse. With the pace of today’s business, the goal is to make that data easily accessible to key users and systems in real-time

The Benefits of Cloud Data Integration

Businesses that can seamlessly integrate data in cloud to cloud environments (AWS, Google Cloud Platform, and Microsoft Azure) and between cloud to on-premise environments stand to benefit in a major way. In an increasingly competitive world, the more effective data analytics brought by accessing unified data can make all the difference. 

Cloud data integration brings with it all of the benefits of traditional data integration solutions. You can:

Remove Data Silos 

One of the biggest challenges for large organizations today is data sprawl. As organizations grow and data resides in various locations throughout the enterprise (and outside of it), in numerous formats and multiple environments, it becomes more and more challenging to use in a meaningful way. Cloud data integration tools help companies address these data access and usability challenges by bringing data together in a ‘single source of truth’ like a data lake or cloud data warehouse. 

Gain a Competitive Edge

One of the greatest challenges for large organizations at scale is ensuring that data is accessible for everyone for those who need it. Business productivity halts to a grind when data is inaccessible for collaboration between departments. By implementing a cloud data integration strategy that lowers the barrier for collaboration within their organization, they gain a major competitive edge. The ability to leverage high-value data to make meaningful business decisions in real-time is essential

Eliminate Redundant Data

When organizations become siloed, they often replicate data in various locations to ensure the data is accessible in that respective domain. However, this replication process becomes costly and induces challenges around data synchronization. Fortunately, a strong data warehouse strategy aligned with a cloud data integration approach removes redundant data residing in these disparate cloud locations, saving on cloud storage costs, physical storage costs, and cloud compute. 

Improve Operational Efficiency

By creating a unified interface for disparate data, organizations can consolidate various platforms, tools, and services that perform tasks on these siloed data stores into a handful of key services that operate on this single aggregate data store. Organizations not only save on removing redundant data stores, but they can also greatly reduce the number of redundant services that operate on these disparate data stores.

business value of data engineering

The Additional Benefits of Cloud Data Integration

On top of these benefits, there are three big pluses to using a cloud data integration approach specifically. Cloud data integration is:

Agile to Deploy Design Patterns Quickly

Deploy new design patterns faster to keep up with today’s ever-changing business environments. Bring streaming data to your business users and help them shift into the real-time world.

Fast and Scalable Integrations

Build and run advanced integrations quickly using templates and reusable data pipeline components instead of starting from scratch each time. Easily extend workloads throughout the enterprise with a single experience for all design patterns – enable your entire team across all patterns with a simple on-ramp to scalable data integration.

Flexible to Handle Change

Cloud data integration with the right solution automatically handles constant changes in data structure, semantics, and infrastructure. Decoupled pipelines and stages minimize the impact of this ‘data drift’ leading to significantly reduced breakages.

What Is an Example of Cloud Integration?

Cloud integration connects multiple cloud services to each other and to on-premises systems to share data and processes in a coordinated manner. Let’s look at an e-commerce business as an example to see how it can streamline operations with cloud integration.

An e-commerce business will have a(n):

  • Cloud storage service like Amazon S3 to hosts product images and descriptions
  • Cloud inventory management solution to track stock levels in real-time
  • E-commerce platform like Shopify, which acts as the storefront
  • Payment processing service like Stripe for transaction processing
  • CRM solution to manage customer data

By integrating these solutions, data flows smoothly from one system to another. The customer places the order on Shopify; transaction data automatically flows to the payment gateway for processing. After payment processing, the order information flows immediately from Shopify to the inventory management system to update stock levels. At the same time, the transaction details flow to the customer record in the CRM. 

This automated integration flow reduces errors and saves time, resulting in cost savings internally and better customer service and satisfaction.

Cloud Data Integration Use Cases

Cloud data integration typically occurs by extracting source data, transforming the data (with ETL tools or data integration platforms), and loading the data into a unified data store. In the enterprise, it’s common to find cloud to cloud data integration and cloud to on-premise.

Cloud to Cloud Data Integration

Cloud-to-cloud data integration allows users to connect between different SaaS / cloud applications and/or cloud platforms. The right cloud data integration platform makes it easier to support a multi-cloud strategy.

On-Premise and Cloud Integration

On-premises to cloud integration, also known as hybrid integration, allows organizations to connect cloud systems to legacy systems – both databases and applications.  

The State of Cloud Data Integration

Cloud adoption has been on a tear over the past few years. Gartner has found that upwards of 81% of public cloud users leverage more than one cloud provider. To better understand some of the significant changes in cloud data integration, let’s explore some of the recent trends gathering momentum in the cloud data integration space. 

Connected Applications

By consolidating data into a single cloud-native data store, organizations can more easily integrate various applications dependent on this single data source. Comparing this to more siloed operations, removing data source boundaries for apps can significantly improve application performance and help development teams gain crucial insights into all stages of the application integration journey. Further, it’s more secure without the risk of data being accessed by someone else.  

Creating a connected application ecosystem can allow organizations to tailor a more customized customer experience and deliver crucial insights about customer awareness, leading to more data-driven business decisions and an improved customer experience. 

Growing Data Types

Long gone are the days that data online resided on-premises and only took on the form of a few data types. Data is structured, semi-structured, and unstructured; today’s ecosystem has wholly transformed compared to the state of the data only a decade ago.

And with varying data sets comes obstacles to creating streamlined data analytics on comprehensive datasets. For instance, one of the most powerful tools businesses can access today is performing sophisticated data analytics on internal and external datasets. It’s a critical tool for business. Without accessing unified datasets that are agnostic to the data type at hand, they are missing out on an array of business opportunities.

Metadata On the Rise

Peripheral to varying data sets is the growth in metadata. Metadata – or data that provides information about other data – helps power big data analytics and business intelligence, leading to more sophisticated and informed decisions regarding customers, internal operations, and general business processes. As this ecosystem of data grows and competition grows more fierce, it’s becoming increasingly crucial for businesses to use and make sense of metadata and the nuanced information it provides.


Cloud Data Integration vs Cloud Application Integration: Is There a Difference?

Yes! Cloud data integration is data-centric, while cloud application integration is process-centric.

This comparison chart of cloud data integration vs. cloud application integration explains the differences clearly:

Cloud Data Integration Cloud Application Integration
Definition Involves combining data from different sources into a single, unified view. Entails linking various applications within the cloud to work together seamlessly.
Focus Primarily on data – its consolidation, transformation, and delivery to users or systems. On processes and interactions between different cloud-based applications.
Objective To ensure that data across the organization is accurate, consistent, and accessible. To streamline business processes by allowing different applications to communicate.
Integration Points Data repositories, databases, data warehouses, etc. SaaS applications, cloud services, APIs, etc.
Data Movement Usually involves batch processing or real-time data streaming. Often includes real-time data exchange and process orchestration.
Tools/Technologies ETL (Extract, Transform, Load) tools, data pipelines, data virtualization tools. Integration platforms as a service (iPaaS), API management platforms, middleware.
Usage Scenarios Data analytics, business intelligence, data warehousing, reporting. CRM, ERP, marketing automation, e-commerce, supply chain management.
Complexity Can be complex due to the volume, variety, and velocity of data. Complexity arises from the need for different applications to work together cohesively.
Maintenance Focus on data quality, data cleansing, and schema changes. Focus on API versioning, process updates, and application upgrades.
Key Challenges Data silos, data quality issues, data transformation challenges. Application silos, API management, process integration challenges.

Both are crucial for a holistic cloud strategy. Enterprises often use them together to ensure maximum efficiency and effectiveness of data and applications.

As your organization grows and looks to remain competitive with real-time decision-making, SteamSets platform provides the cloud data integration platform that gets you there.

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