Data is a crucial component of modern business operations, and businesses generate vast amounts of data that need to be stored and analyzed to extract valuable insights. While it is true that businesses generate massive volumes of data, it is not necessarily the case that they generate data every second of every minute. The frequency of data generation can vary widely depending on the business and its operations.
One solution to the need for effective data storage and analysis is cloud data integration, which involves integrating data from various sources into a centralized cloud environment for analysis and processing.
“Data is a precious thing and will last longer than the systems themselves,” said Tim Burners-Lee, inventor of the World Wide Web. Currently, data integrated on a cloud is more invaluable than ever.
Cloud data integration is the process of bringing together data from various sources including public, private or hybrid clouds, as well as on-premise storage, to create a unified and centralized view. This involves using a range of tools and technologies that connect different applications, systems, repositories, and IT environments to facilitate the seamless exchange of data and processes in real-time.
Cloud integration can be referred to as cloud system integration, cloud-based integration or iPaaS (Integration Platform as a Service).
Cloud data integration plays a critical role in a multi-cloud IT landscape by consolidating all the different parts into a unified system. It is necessary for various tasks, such as merging customer data and order management data to gain a better understanding of customer behavior, or constructing cloud data warehouses or data lakes to support complex real-time analytics.
Cloud data integration delivers on several key requirements:
Improves business agility
As market changes and business requirements evolve, you can adapt and deploy new integration patterns faster with cloud data integration. Integrate cloud and on-premises data sources seamlessly for supercharged business analytics, data science, data stewardship, and citizen integration.
Enables integration speed
The cloud data integration platform enables you to build and run complex integrations rapidly and at scale. Connecting hundreds of applications and data sources with prebuilt templates and reusable mappings takes only hours or minutes instead of cumbersome hand coding.
Breaking down data silos
Data silos are omnipresent and obstruct free access to critical information when necessary. Integrating cloud data reduces the number of data silos within an organization, allowing information to be viewed more easily. Having accessible data means your employees get a broader perspective on your business and gain insight into what's already been collected. Simply put, integrating the cloud can be a good way to see the big picture.
Allows ample flexibility
By leveraging the enterprise-grade performance and reliability of cloud computing, cloud data integration enables enterprises to process complex data integration mapping tasks.
Adapting to changes and innovations in the business world is possible by improving connectivity and visibility.
Is it possible to integrate multiple clouds to form a single data source? Yes. Is it possible to integrate the cloud with an on-premise data source? Yes, that’s possible too. In other words, there are several approaches to data integration, but choosing the process depends on these three factors:
Some of the leading cloud data integration types include:
Data propagation
By definition, data propagation is the process of transferring stored data synchronously or asynchronously from one place to another. It integrates various platforms across multiple environments, including on-premises, hybrid, and multi-cloud. Additionally, it is possible to integrate Software-as-a-Service and on-premises applications to share and synchronize data.
B2B integration
The Business to Business cloud integration allows you to create robust communication protocols to exchange data with your business partners, irrespective of what Application Programming Interfaces (APIs) they use.
Data consolidation.
Data consolidation is based on Extract, Transform, and Load (ETL) technology. As the term implies, the data is collated from various sources, reorganized into a new format, and reloaded in new storage. The data is cleansed and processed during the ETL procedure, preparing it for Business Intelligence and data analytics querying.
While there are many objectives to data integration in the cloud, the process serves two primary purposes:
Platforms and software programs are used to connect data from source systems to target systems and automate data integration. Cloud data integration involves the following techniques:
1. ETL or Extract, Transform & Load
ETL is the globally-accepted standard for processing massive volumes of data. The process combines different software applications to extract data from multiple sources. The collated data is consolidated into a format and reloaded into a new database or warehouse. ETL cost-effectively enables cloud data integration.
Here’s a glimpse of how ETL works:
Extract
Data is gathered from disparate databases, hardware and software applications, network appliances, etc., in real-time.
Transform
The transformation phase is where many things happen:
Load.
ETL ends with the data being deposited into the desired destinations, such as data warehouses, databases, data lakes, analysis tools, cold network repositories, or other applicable uses.
2. Cloud integration hub
The core function of the hub is connecting and sharing data across SaaS applications, cloud ecosystems, and on-premises apps. For example, the hub can aggregate data from multiple cloud applications and databases to create a single dashboard view of data from different sources.
Cloud integration hub has several advantages.
3. Serverless data integration
The term is self-explanatory: There is no need for managing operational redundant server infrastructure, containers, or virtual machines. Additionally, serverless integration allows data developers to create better applications that enhance operational productivity. Technology innovators and enterprise architects can use this technology's agility and versatility since it doesn't require infrastructure management.
4. Data ingestion
Data ingestion is the initial process in a data pipeline. It is the process of migrating structured and unstructured data from its original source to a more suitable storage destination. Ingested data is information-rich and ideal for immediate usage or future analysis.
In data ingestion, cloud data ingestion pipelines must set the necessary parameters before transporting data to their preferred destination. The transportation can only be done through a single data pipeline after the parameters are established.
5. B2B or Business-to-Business Partner Integration
B2B data integration is a promising gateway that allows businesses to:
Whether it is a public, private, or hybrid cloud, businesses are effectively cutting costs AND ensuring regulatory compliance, thanks to B2B integration.
Hence, it is not for nothing that cloud data integration is gaining traction at lightning speed. Whatever the mode of integration, the process delivers multiple benefits for everyone who invests in it.
1. Simplified integration
In addition to providing a unified and streamlined infrastructure for managing all current and future integrations, cloud-based integrations provide an easier way to integrate data between different systems. Cloud-based solutions enable easy integration of new source systems and provide a holistic data view instead of a patchwork approach.
2. Cost reduction
Cloud-based data integration is a one-time investment that eliminates unnecessary expenses such as:
3. Employee benefits
The simplification of data access and management saves employees a lot of time. Cloud integration:
4. Workflow automation
The benefits of managing your data from one location are immediate. With single storage, your staff no longer has to copy and enter data manually, which is prone to human error and time-consuming. Furthermore, you can automatically generate analytical reports from various data sources by using this application.
5. Increased scalability
Organizations can quickly access large amounts of data without manually executing SQL series or inputting data into databases. While this process not only helps save time and money, it also allows businesses to scale data usage as per their needs.
6. Data compliance
When it comes to anything data-related, there are three regulatory standards that all companies must adhere to:
Companies must adhere to these regulations to store, maintain, or use customer-related sensitive data. Cloud integration ensures that your business is compliant with these standards and other laws of the land. Regulatory compliance also enhances data governance and security.
7. Eliminates duplication
Since integration results in consolidating and storing data in a single data warehouse, teams do not need to reuse the data for applications and organizational processes. Integration eliminates the need for duplication and unnecessary costs.
8. Better data analysis
One of the significant end uses of cloud data integration is the analysis of data that’s easily accessible from a single location. Since the data is already classified and consolidated, it enables easy assessment of business growth, customer behavior, and future business opportunities.
Cloud data integration is undoubtedly extremely viable, beneficial, pragmatic, and result-oriented. However, it does not come without some challenges. Let’s explore some of the most common drawbacks of this technology.
Lack of standardized approaches and protocols for cloud-to-cloud or cloud-to-on-premise integration.
As such, there already exists a confusing diversity of data schemas and formats for each cloud service, platform, and resource. Yes, data connectors do exists. Yet, the necessity to constantly update integration partners about modification, cloud service adoptions, and application updates is indispensable.
Picking the most appropriate tool for a business use case to set up the integration automation platform.
Even though you initially deploy a single integration platform, you will need to scale your solution to accommodate the expansion of cloud integration tools to serve your needs and use cases.
Complex ETL workflows to clean, process, and consolidate data to a precise format.
Many cloud systems can work with unstructured data or provide a flexible data model for structured data by replacing manual tasks with automation and simplifying the entire ETL process. Yet, they must be brought to the desired format without the cleansing and converting processes slowing down the integration.
The constant need to address common challenges such as:
Wrapping up
Cloud data integration is a considerable teamwork that requires technical, strategic, and administrative expertise to ensure that every step is done perfectly. You will need:
Additionally, enterprises can ensure that their cloud-based and on-premise applications consistently synchronize valuable data and make it available to data consumers.
