Cloud data integration is the process of assimilating data from disparate public cloud services such as Amazon Web Services, Microsoft Azure, or Google Cloud into a single service. Public cloud providers offer fully managed services like Amazon Redshift for data projects. However, such services are aimed at software developers and require organizations to create customized data-driven applications.

Customized data integration projects can become very expensive. For example, a 2022 Foundry study found that organizations report an average annual budget of $12.3 million for data-driven initiatives in the cloud. Expenses add up because you must transform and clean large volumes of data before consolidation. A new class of no-code tools targets enterprises interested in data integration without expensive software development projects.

In this article, we look at detailed examples of the two different approaches to data integration. We also review the top four benefits of the no-code approach and discuss some integration best practices.

Code-based cloud data integration example

Code-based cloud integration requires software developers to build applications that process data using various third-party cloud services.

Consider a manufacturing process where factory equipment generates instrumentation data, like measurements indicating a machine’s performance. In our example, this is implemented using services provided by Amazon Web Services (AWS) services. 

Even though the architecture of this solution relies only on cloud-based services, its implementation requires a team of developers to code and maintain it over time. 

Architecture explained

1. The data source is a piece of manufacturing equipment that generates instrumentation data.
2. AWS IoT Core, a serverless integration for IoT Greengrass devices, computes and caches the instrumentation data.
3. Amazon Kinesis Data Streams is a real-time data streaming service that continuously captures data updated in IoT Core. 
4. Amazon Kinesis Data Analytics further processes Kinesis data streams in real time with SQL or Apache Flink.
5. Amazon S3 stores the analytics data store for long-term persistence.

After the data is in S3, other purpose-built systems ingest it depending on the use case. For example, Amazon SageMaker uses the data for machine learning, Redshift for data warehousing, or Athena for ad-hoc queries.

No-code cloud data integration example

No-code and low-code tools allow data integration without the need for complex software projects.

The following example involves ingesting data from call center applications and social media platforms such as Twitter to monitor customer satisfaction and user sentiment. The app analyzes customer messages for specific keywords to observe how consumers respond to price changes or new products. The diagram below provides a simple high-level depiction of the systems involved in this scenario. 

The implementation of this example uses Nexla’s no-code data integration platform with prebuilt connectors and an interactive UI that lets business analysts configure data ingestion, transformation, and processing easily. No coding is necessary. 

Architecture explained

1. A prebuilt integration allows analysts to filter Twitter messages, combine them, count them, and analyze them. 
2. The system autogenerates connectors for different data sources as required. For example, analysts can use prebuilt connectors to connect with Salesforce Service Cloud Voice applications. 
3. AI software scans the data and metadata to generate logical data units called Nexsets. It then helps materialize the data into a system of your choice.
4. The conclusions resulting from this analysis are shared with customer support via service tickets. 

This automated, no-code approach to data integration shortens the time to value and lowers expenses. 

Summary of benefits: no-code cloud data integration tools 

This table summarizes the benefits of no-code cloud data integration; they are described in more detail below.

Key Benefit	Value
Decrease data integration efforts.	Ingest data from new sources with minimal setup.
Enable automation of data pipelines.	Shorten ingestion time, and ensure that data is relevant and reliable.
Optimize for modern use cases.	Provide seamless, reliable access to real-time data for modern distributed architectures.

Benefit #1: Decrease data integration efforts

Data platform engineering teams are frequently tasked with integrating new data sources to increase the data sets for analysis. These data sources are often loosely distributed among several cloud services and have very different data integration requirements. 

No-code cloud data integrations are faster to set up and shorten the time to value (TTV) of new data sources for business intelligence teams. For example, you can integrate data warehouses, such as AWS Redshift and Google BigQuery, with documents stored on Azure Blob Storage or AWS S3. 

There are two main setup methods.

Metadata discovery

The data integration tool connects the data source and target via metadata. It automatically discovers integration configurations reducing time and effort by data platform engineers. 

Prebuilt connectors

Vendors provide prebuilt connectors for integrating data sources and analytics systems. These prebuilt connectors are no-code or low-code solutions that directly integrate data from cloud services, APIs, databases, and various other sources. For example, Nexla autogenerates data connectors to mix and match data systems of any type.

Benefit #2: Enable automation of data pipelines

No code cloud-based data integration solutions enable the automation of data pipelines. For example, Nexla provides a unified data operations platform for managing data flows regardless of format or source type. It has the capabilities of automated and continuous data validation, error management, monitoring, notifications, and built-in retry mechanisms. You get benefits like:

• Real-time or near-real-time ingestion
• Relevant and reliable data that is always available for analysis
• The ability to meet service-level agreements (SLAs) for analytics

DataOps support for data pipelines

No-code automation also supports your DataOps practices. DataOps is a set of agile practices that improve the quality of data pipelines. They offer a process-oriented perspective on data and lifecycle automation methods borrowed from the software engineering discipline DevOps. DataOps focuses on improving data quality and ingestion velocity in the analytics pipeline.

Benefit #3 Optimize for modern use cases

Streaming data, Internet of Things (IoT) device fleets, and data meshes require scalable, highly available, and self-service integration solutions. No-code cloud integration tools provide seamless, reliable access to real-time data for complex use cases like the following:

• Providing training datasets for feeding machine learning models to improve their accuracy.
• Making critical connections among disparate data sources, like sensor logs and metrics, for security.
• Collating data from various touchpoints to create a 360-degree unified customer profile.

Cloud data integration best practices

We give below four best practices to get the most out of any data integration project. 

Ensure that you meet regulatory and compliance requirements

Many companies must adhere to industry-specific regulations, like GDPR, PCI, and HIPAA. For example, logging is required for HIPAA compliance, and credit card data encryption is required for PCI compliance. 

Cloud data integration allows you to create automated custom processes and enforce requirements within data pipelines. Examples of security requirements include access control, encryption, strong security measures, and security certifications. This enforcement ensures that you meet requirements before data ingestion. 

Empower business users with data mesh

A data mesh is a framework that emphasizes democratized data ownership where producers and consumers of data collaborate according to a federated governance model. Producers present their data sources as a data product and control consumer access. Prebuilt data connectors to familiar data sources make it easy to get started, while collaboration tools enable consumers to access the data on a self-service basis.  

Adopt a rigorous approach to product selection

Each business is different, so its data analytics requirements are unique. When procuring cloud integration tools, validate that the solution aligns with your use case, budget, and development capabilities. Once the product list is narrowed down to final solutions, perform data analytics operations using each product in a demonstration environment before finalizing your selection. A solution with a short learning curve helps decrease training and integration implementation time.

Implement a data integration platform

A data integration platform helps with the integration strategy of an organization by providing a centralized platform for connecting, transforming, and managing data across multiple source systems. It also helps enforce governance policies, manage solutions, and provide real-time integration capabilities while reducing costs.

Conclusion

Current and predicted cloud adoption rates and the increasing use of analytics in business decision-making indicate a strong need for cloud data integration solutions. Integrating disparate data sources is a complex challenge you can solve by utilizing no-code cloud data integration platforms. No-code solutions transform and automate data processing to decrease data project efforts and enable businesses to produce superior insights quickly.