What is Cloud Data Integration? Guide, Benefits, and Examples

Learn what cloud cloud integration is and how it can rocket your business leaps and bounds ahead of the competition. Build and run advanced integrations swiftly and at scale.


What is cloud data integration?

Cloud data integration is the process of combining data from disparate sources into a cloud-based storage system (e.g., a data lake, data warehouse, relational or non-relational database, etc.). This previously fragmented data could have come from other cloud-based databases or apps, an on-premises system, or a combination of both. 

The step-by-step process for cloud-based data integration will vary depending on the business and their specific needs, but it'll often involve some form or combination of: batch processing, real-time event streaming, APIs, and ETL or ELT pipelines. 

The importance of cloud data integration right now

Data integration is essential to avoid inaccuracies in reporting, run large-scale analytics, fuel AI and machine learning models – there’s no shortage of upsides (but quite a few downsides to having data silos). But you may be wondering about the specific benefits of integrating data in the cloud. In other words, what makes it so important? 

There’s a few reasons for this shift to cloud-based systems (which seem to have become a staple in modern tech stacks). The first being: we’re in an era of Big Data. The amount of data being generated each day has grown exponentially in the past decade. It’s estimated that in 2025, each person will generate 181 zettabytes of data – that’s 181 followed by 21 zeros for some mental visualization. Another interesting tidbit here is that 80% of companies surveyed said that 50-90% of their data is unstructured, (e.g., audio files, images, etc.). 




The chart above shows the exponential growth in data volume we've seen in roughly the past decade

With so much data being generated each day, the cloud offers businesses scalability and flexibility when it comes storing, processing, and analyzing data. Cloud-based data lakes and warehouses in particular are well-suited for handling large volumes of data, and these storage systems are elastic. Meaning, you can scale storage or memory up or down depending on fluctuating data volumes, which can have huge cost benefits (especially for pay-as-you-go cloud services). It also makes businesses more agile and adaptable to evolving situations. (For example, there may be periods of time that see spikes in data, like Black Friday sales for e-commerce and retail companies.) 

Cloud services are also well suited to a more remote-friendly workforce, allowing access to IT resources and services anywhere in the world, with systems also coming equipped with important security features like built-in backup or encryption. However, it’s important to note that on-prem systems mean data is stored on-site, or in company-controlled servers, giving businesses complete control and customization over security and storage – which has been appealing to highly regulated industries. 

In short, the cloud provides businesses with an adaptable infrastructure to stay nimble in a changing environment, lower costs of maintenance, and built-in features that help with running advanced analytics on large data sets (e.g.,  machine learning models, data visualizations).  

4 best practices for successful cloud data integration

As we mentioned above, every data integration strategy will look different. But here are some best practices for integrating your data in a cloud store. 

Have a clear understanding of your objectives, limitations, and current data flows. 

This might seem like a trick question considering we just went over the benefits of cloud data integration, but before getting started businesses need to ask themselves: why are we doing this? 

Like most things, cloud adoption comes with its own caveats. As McKinsey noted, businesses can easily fall into the trap of thinking “lift and shift” will work (or the idea that simply moving legacy systems over to the cloud will count as digital transformation). For businesses in highly regulated industries like healthcare and financial services, they’ll need to think through data security and broader compliance issues (e.g., protecting personally identifiable information, potential requirements to store and process data locally). 

Before kicking off cloud data integration, businesses should also have a clear understanding of what data they’ll be consolidating in the cloud (i.e., their current data sources and destinations, how data is formatted, etc.). 

This is where data mapping comes into play. As its name suggests, this is the process of mapping out the relationship between data that exists in different sources, databases, or formats.  A data map is a crucial step because it identifies corresponding data fields and provides a set of instructions on how data should be transformed to prevent redundancies or errors during integration. 




It’s important to understand what data you’re collecting, how it’s formatted, its source(s) and destinations. 

Choose the right tool and target system. 

There are several tools and vendors on the market to help with data integration and  for cloud-based storage. While this is by no means an exhaustive list, a few popular options for cloud storage include Amazon S3, BigQuery, Google Cloud Storage, Snowflake, Microsoft Azure, or Segment Data Lakes. 




A comparison of a few cloud-based data storage options on the market

When considering which tools are right for your business, take into account: 

  • Your data volume, complexity, and velocity (e.g., can these tools perform at scale?)

  • Data processing capabilities (e.g., data validation)

  • Data security (e.g., how will data be protected? Are there protections in place like encryption and multi-factor authentication)

  • Cost (e.g., pay-as-you-go models, subscription based, etc.) 

Ensure data cleanliness

A crucial part of data integration is ensuring data cleanliness. Duplicate entries and errors completely undermine the point of data integration – which is to provide accurate, holistic, and reliable data. There are tools on the market that can help ensure data cleanliness at scale, like Segment Protocols. With this feature, businesses are able to:

  • Align teams around a universal tracking plan

  • Reduce implementation errors with pre-built integrations

  • Automate the QA process 

  • Automatically block invalid events

  • Correct bad data without touching code, via Transformations

Enforce data governance policies

Data governance refers to the policies and standards a business establishes around how it ingests, processes, stores, and activates its data. These policies and best practices create internal alignment within an organization to reduce errors and inconsistencies that could otherwise crop up throughout the data lifecycle. 

A part of enforcing data governance policies is creating documentation, like a universal tracking plan or a record of data’s origins, transformations and target destination (i.e., data lineage). 

Data governance helps delegate responsibilities and ownership across cross-functional teams, and aims to protect data’s integrity and security.

Unlock your data’s full potential with Segment

Segment’s customer data platform assists with cloud data integration in a few fundamental ways. For one, with Connections, businesses are able to easily integrate new tools and technology into their tech stack in a matter of minutes – saving hours of manual engineering time. Many cloud-based data warehouses are included in these pre-built integrations, allowing businesses to seamlessly set one of these storage systems as a destination for their data. 

Second, Protocols allows businesses to validate data at scale, and enforce a tracking plan to ensure data integrity. Automating QA checks allows businesses to proactively block bad data before it’s  integrated and used for decision making. Segment also allows you to assign role-based access to data and automatically block personally identifiable information, for added security and compliance. Here are a few other important features:

  • Replays: This allows businesses to send a sample of their existing data to a new tool(s) for testing purposes and verify the accuracy of the output. 

  • Debugger: Allows you to confirm (in real time) that API calls (made from servers, mobile, websites, etc.) arrived to your Segment Source in the expected format. 

  • Regional Segment in the EU: Infrastructure hosted in the EU for data ingestion, processing, storage, and audience creation to comply with Schrems II. 

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Frequently asked questions

An example of cloud data integration is extracting data from on-premises solutions and cloud-based apps (e.g., social media feeds, Dropbox, etc.), transforming this data so that it's in the same format, and then loading it into a cloud-based data warehouse.

Cloud app integration is when you connect and integrate different applications, so they can work together (sharing functionality and data). Cloud data integration is when you combine data from disparate sources, either in the cloud or on-prem, and consolidate this data into a cloud store (like a data warehouse or lake).

There are numerous benefits to integrating data in a cloud store, including flexibility and reliability, elasticity (e.g, many storage systems offer “pay as you go” models and can scale up and down depending on data volume). The cloud is also able to store large data sets from disparate sources (in a variety of formats), and allows teams to run advanced analytics to foster deeper understanding and innovation.

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