What is database integration?

Database integration is the process used to aggregate information from multiple sources—like social media, sensor data from IoT, data warehouses, customer transactions, and more—and share a current, clean version of it across an organization. Database integration provides the home base, to and from which all shared information will flow.

database integration diagram

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For example, when two businesses merge, their previously individual databases contain essential data for operating the new, combined organization. Database integration can help ensure that data is deduplicated, stored according to defined rules, cleansed, and securely shared with stakeholders.

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As the cloud becomes the new standard for operations, and big data continues to drive business intelligence and the ability to compete in an increasingly fast-paced digital marketplace, database integration takes on a critical role in ensuring businesses are efficiently harnessing their data rather than being overwhelmed by it.

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Benefits of database integration

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Data is the backbone of modern business, where digital interactions replace brick-and-mortar locations and physical infrastructure like servers, routers, and more.

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Properly managed database processes convert those challenges to measurable improvements in operations, including:

• Universally reliable business data – Ingesting, cleaning, securing, and re-sharing data with an unlimited amount of heterogeneous sources, organizations can maintain a single source of business truth across even a global enterprise.

• Holistic operations oversight – Managing business-wide intelligence from a central, visualized operations screen provides a powerful tool for identifying bottlenecks, improving user experience and customer service, shortening delivery cycles, and more.

• Simplified security – As high-visibility hacks dominate the news, companies know they face more points of access and greater security threats than ever existed in isolated, on-premises network environments. With a central database integration deployment, the final versions of data enter into and emanate from a single source, which greatly simplifies securing critical information.

• Easier compliance – Modern, digital business comes with increasing responsibilities to comply with national and international operating standards, including HIPAA, PCI, and GDPR. Database integration provides central management for ensuring compliance within the enterprise.
   

In these and other ways, organizations are using database integration as the backbone of their data integration platform and turning raw information into business intelligence.

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Database integration in a modern IT environment

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The old days of running organizations ‘from the server room’ aren’t quite over, but cloud technology is poised to power the next wave of database integration.

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They deployment style that an organization chooses is primarily dependent upon existing operations. For example, a company with a legacy, on-premises data center will probably choose a local database integration solution to impact operations with minimal retooling. Newer businesses, though, are taking advantage of cloud-native environments, which offer efficient pricing structures, infinite scalability, and no upfront hardware capital. For companies somewhere in the middle, hybrid approaches bridge the gap between local legacy architectures and the cloud.

 

On-premises database integration

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On-premises database integration supports traditional, on-site network infrastructures. Often sold as stand-alone products, on-premises solutions install locally and interact with existing hardware and databases to cleanse, monitor, and transform data for business intelligence.

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Since on-premise solutions handle all data operations locally, they reduce network overhead. Additionally, they aim to operate out of the box, with pre-built connectors for interfacing with common data sources. On-premises solutions generally require working agreements with developers from common product lines to frequently upgrade and secure connections.

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Cloud database integration

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Cloud database integration solutions are cloud-native, and run as part of an infrastructure—interacting in the background with all data transactions occurring across the enterprise.

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This approach brings the standard advantages of cloud architecture, which include auto-scaling and pay-per-usage pricing. A key advantage of cloud database integration is the near-seamlessness with which the SaaS solution interacts not just with other databases in the environment, but also with virtual infrastructure and security, providing real-time looks at the entire operation.

 

Hybrid database integration

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Combining elements of on-premises and cloud, a hybrid database integration approach leverages a cloud-based SaaS that synchronizes and manages data between local and remotely hosted resources.

Good database integration solutions correlate and cleanse cloud-based and on-premises data, providing a uniform working information set across the mixed environment. The best ones interact seamlessly with other SaaS solutions and provide simple GUI interfaces, providing decision-makers with a 360-degree view of all operations and interactions.

Whichever approach companies choose, careful planning, strong partnerships, and the right tools make the difference between bogged networks and real-time business intelligence.

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Database integration tools

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The cloud itself was born from collaborative, open source data technologies that make distributed storage, processing, and data management accessible and affordable. Many of the core components most utilized in cloud or hybrid computing are based on open source technologies.

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Perhaps no organization has done more to promote and secure the growth of the cloud than the Apache Software Project. Many of its community-developed projects are the foundations of the world’s largest big data operations, including Netflix, GitHub, and the European Organization for Nuclear Research (CERN).

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Open source software

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Apache Hadoop – Hadoop is a framework for distributing processing, allowing up to petabytes of information to be divvied among a limitless number of physical or virtual servers, bulk processed, then returned as clean, reliable data.

Hadoop is based on the Java language but is open-sourced, with libraries of extensions and mods to accommodate any business need. Hadoop provides a native file management system, as well as linear scalability and failover protection so failures in one data stream are compensated for by parallel nodes.

 

Apache Spark – Spark is sometimes thought of as a newer replacement for Hadoop, but in reality it’s a companion tool. Spark improves upon the distributed processing framework in Hadoop—known as MapReduce—by processing data up to 100 times faster.

Spark achieves this by processing most data tasks in memory, rather than relying on transfer to a physical or virtual location for conversion. But what Spark offers in speed is somewhat limited by its functionality, as Spark does not include a file management system of its own. Spark can integrate with other file management systems, or works seamlessly with its Hadoop sibling.

 

Apache Cassandra – Perhaps the root of big data is the NoSql database, an information processing evolution that removed the constraints of columnar and relational databases by allowing for heterogeneous storage types—making database integration between file formats like text, image, multimedia and more possible.
Open-source and flexible for unlimited scalability, Cassandra serves the needs of even giant corporations like Apple, which relies on Cassandra as a distributed framework for integrating more than 10 petabytes of data.

 

 

Choosing the right database integration partner

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A challenge of integration is the need for customized coding to integrate connectors and SaaS dependencies with Apache (or similar) frameworks. Choosing the interface tools with which organizations will build from open sourced data platforms, then, becomes critical, as difficulties writing compatible code by hand can slow or derail operations.

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The best database integration tools save IT teams countless hours by simplifying custom coding. Rather than frequent, hand-coded patches to keep connectors functioning through updates and security revisions, powerful but simple GUI tools can process data integration tasks up to 10 times faster—and at about a fifth of the cost of hand-coded jobs.

 

The right integration approach offers the flexibility of free, open-source licensing options that give developers the opportunity to explore and test the power of Apache Hadoop, NoSQL databases, and other big data tools.

 

For organizations that lack the internal expertise or infrastructure to manage database integration, holistic partnership solutions are another option, including a top-ranked integration-platform-as-a-service (iPaaS) option that manages all aspects of big data flow and transforms environments into interactive business intelligence machines.

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