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Everything you need to know about cloud data integration

Everything you need to know about cloud data integration

As most aspects of living and working get digitized, the use of cloud computing and data-based systems has also grown. These are important elements of the modern IT structures in all workspaces. Investing in one or more cloud-based systems has become more imperative for various organizations as their dependency on data-driven functions grows. Cloud data integration is a data management practice that is followed especially when such organizations deal with data from multiple sources and clouds.

What is cloud data integration?
Cloud data integration refers to a cloud-based integration platform that is secure and uses traditional data integration functionalities paired with contemporary and agile approaches for data-driven developing solutions. Additionally, it also looks at expanding the capabilities of data integration infrastructure to offer native support to the cloud. These platforms can be run on cloud and on-premises with ease. It makes migration to the cloud much easier and exists to help with the various aspects of this migration process.

In simple words, cloud data integration software helps organizations take care of their data management, cleansing, and integration functionalities from any web-based application. Those using cloud data integration software are not required to obtain any additional hardware or software elements for accessing the same.

How do cloud-based systems make data integration easier?
Data integration can improve by several notches with the help of cloud capabilities. Here are some ways it does so:

  • Cloud systems are flexible and offer elastic scalability. It helps take care of the sudden amped up need for server resources in the process of data integration. Some of the examples of processes where the data integration workload shoots up include data ingestion and data transformation. With the help of the cloud, the resources can be marshaled and allocated back once the workload subsides.
  • Using cloud data integration also proves to be cost-efficient. The cost incurred for server and storage resources while using it is much lesser as compared to the resources required for traditional on-premise integration processes.
  • Cloud data integration does not have many hassles as compared to traditional systems. The service provider takes care of the functionalities of managing server capacity planning, upgradation and maintenance processes, and server optimization, which otherwise needs to be taken care of by the organization itself.
  • The practice of centralizing collaborative resources and services to make data management easier was initially only available for on-premises operations. This is now available on cloud data integration platforms, which makes it easier to merge resources and services even with professionals in a different location.

Benefits of Cloud Data Integration
These are some benefits of adopting cloud data integration, and these are as follow:

  • Cloud data integration helps in automating workflows, leaving professionals with more time to focus on other productive tasks. It does not require data to be copied or entered manually. The automation also leads to setting up standardized protocols for how data is treated.
  • Organizations can tackle wasteful use of storage by eliminating redundant data using cloud data integration. It facilitates the use of shared data storage to eliminate any duplicate data. This can help save costs as well as synchronization efforts at later stages.
  • With cloud data integration, professionals are offered more opportunities to scale the processes, improve them, and introduce new practices.

Categories of Data Integration Support for Cloud
There are two categories of data integration support for the cloud:

  • Running data integration natively in the cloud

Enabling new practices becomes easier with cloud-based systems for data integration. This is because of the scalable, neutral, and affordable characteristics of cloud.

  • Data integration involving interoperation with multiple clouds

Working in hybrid data environments requires a wide range of services and tools. This includes Big Data resources, open-source tools, and ample cloud storage. Additionally, to ensure the highest speed and greatest functionality, cloud data integration also needs to support interfaces to widely used SaaS applications.

Challenges of Cloud Data Integration Systems
Despite the various benefits provided, there are some challenges that cloud data integration poses. These are as follow:

  • The process of moving data around different cloud systems and between cloud and on-premises systems can be time-consuming. There is also a good chance of an error occurring during these processes. These issues are inevitable in many cases and can only be avoided with carefully planned strategies for data movement. The lack of planning can also sometimes make cloud data integration unfeasible if the errors are too many.
  • Several cloud systems deal with unstructured data, this makes it important for the data to be cleaned and converted to the format required for the integration. These Extract Transform Load (ETL) workflows can slow down integration and make it more complex.
  • Lack of standardization is another challenge as there is no protocol or universal approach set for data integration, irrespective of whether it is between cloud systems or between cloud and on-premise systems. This calls for constant practices of updating data schemas and connectors as the available cloud applications evolve or as new ones are introduced. The data schemas and formats vary across each cloud service provider.
  • Despite being scalable, cloud data integration can be difficult when it comes to synchronization with external systems.

Common characteristics of comprehensive data integration platforms
If you plan to use a cloud data integration platform, here are some features you must look out for.

  • Ability to Interoperate with Popular Clouds

A good cloud data integration platform or enabler will have strong capabilities for inter-operation with multiple clouds. Since many organizations today are midway on their journey to adopting cloud-based systems fully, data integration may happen across different clouds and also on-premises. The ability to interoperate with popular clouds will help to tackle working with both traditional and modern systems simultaneously in such organizations.

  • Diverse Toolset in a Single Place

Cloud data integration involves various processes and requires different toolsets depending on the existent systems. This makes it extremely important that all the tools required to work with various data disciplines are available in one place to reduce hassle and save time.

  • Excellent Security Features

The loss of data can prove catastrophic for organizations, bringing down several business processes. Therefore, a good cloud data integration platform should have all the functionalities to ensure the security of data. This includes both data at rest and in motion. The availability of robust features like data encryption, data masking, and digital certificates are essential elements of a good cloud data integration platform.

Popular Cloud Data Integration Software
Cloud data integration falls into various categories depending on the type of data and the industry. Here are some top software brands that are used by cloud data integration professionals.

  • Qubole
    This open data lake company has created a secure and easy data lake platform that can be used for machine learning functionalities, streaming, and ad-hoc analytics. It aids in cloud data integration by offering end-to-end data lake surfaces including data management, cloud infrastructure management, data analytics, and engineering, and machine learning with hardly any supervision. Patronized by industry leaders like Oracle, Gannett, Expedia, and Adobe for their Big Data consumption and practices. The cloud data integration software for Big Data offers unmatched data workload flexibility and improves data lake adoption and significantly brings down the costs.
  • IBM InfoSphere® DataStage®
    This cloud data integration software is a leader in the ETL platform category known for helping integrate data from multiple enterprise systems with ease. The high-performance framework it uses for achieving this is made available on-premises and on-cloud. Its scaleable characteristics help in offering extended metadata management and enterprise connectivity. IBM InfoSphere ® DataStage ® is capable of integrating heterogeneous data – Hadoop Big Data at rest and stream-based Big Data in motion. This can be done across distributed and mainframe platforms. The software is a hit among cloud data integration professionals as it allows for real-time data integration on a simple deployable and scalable platform.
  • Azure Data Factory
    With Azure Data Factory, developers can perform cloud data integration using disparate data sources. Users can access on-premises data in the SQL server and cloud data from the Azure Storage and Azure SQL Database. The Azure Storage types include blob and tables.
  • PieSync
    PieSync is a cloud data integration tool offered by Hubspot. The software focuses on the transfer of data in real-time between cloud-based apps. It supports two-way data syncing and has been created for syncing of contacts. This can help users save time and costs of getting data entered manually. The process takes less than ten minutes and is not error-prone.
  • Dell Boomi
    Dell Boomi helps in simplifying the process of transferring data across legacy and cloud systems. It achieves this with the help of a low-code graphical interface, pre-built connectors, and APIs. The company offers excellent customer service and is capable of integrating data from data repositories from different companies. Users can take care of data integration, data quality services, and data management all in a single interface with Dell Boomi.
  • Pentaho
    Pentaho offers functionalities for application development, systems migration, and dealing with various data science functions. It has intelligent architecture and can be customized to a user’s cloud data integration needs. Some of the processes that can be completed with Pentaho include OLAP services, data mining and extraction, and data reporting, among others.
  • TIBCO Cloud™ Integration
    Cloud data integration professionals can achieve an array of tasks including connecting cloud apps, building hybrid integration platforms for different on-premises systems, and the development of IoT edge and microservices applications. This integration-platform-as-a-service can be used by cloud data integration professionals with diverse skill levels. Apart from cloud data integration professionals, the software can be used by business application owners as well. It is a one-stop solution for capturing data and integrating it for further functionalities. This is a popular product in the e-commerce data integration and enterprise service bus categories.
  • Cloudera
    Cloudera helps with cloud data integration by offering an enterprise data cloud for diverse kinds of data from any source. This software company aids in the transformation and consumption of data for drawing various actionable insights. This is a Big Data cloud data integration software.
  • IBM App Connect
    The IBM App Connect is another leading cloud data integration platform. It adopts a model-driven approach helping users connect an array of applications and data. The integration styles that are supported on the IBM App Connect include real-time event-based and scheduled batch data copy/synchronization. It features a series of integration characteristics making it ideal for a variety of enterprise requirements, even the most complex ones. IBM App Connect can be deployed on-premises as well on-cloud, making it easier to work with data on most applications.
  • Snaplogic
    Snaplogic creates specialized integration-as-a-platform tools and services through a no-code interface for helping the integration of data across different repositories. Some functionalities it supports include conditional operations, parameterization, reuse, complex transformation, triggers, and aggregation, among others. It has an advanced interface comprising of AI and machine learning and advanced monitoring features. Snaplogic has more than 400+ pre-built connectors to offer.

As the data-driven systems grow and as companies move ahead with the adoption of multiple such systems, cloud data integration seems like the best option to make this transformation easier and more secure. Cloud-based processes offer the advantage of being used remotely. Cloud can help organizations transfer data seamlessly even in hybrid environments without interoperable characteristics. Cloud and data integration can work in a symbiotic way, enhancing benefits for a brighter future for cloud and data-driven systems.

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