Businesses want to integrate data from many sources into a single, up-to-date dataset for use in business intelligence (BI), data analysis, and other applications and business processes. The choice of a data integration platform significantly impacts the effectiveness and expense of efforts, including data replication, ingestion, and transformation. This blog discusses the significance and critical ingredients of a data integration platform.
What Is A Data Integration Platform?
More than just a database migration tool, a data integration platform includes technologies that make it possible to build flexible data pipelines. Database replication software can be used to copy and convert database objects into other formats and data transport architectures. Model-driven configuration tools can design, build and manage a database or a data warehouse. A smart, enterprise-scale data catalog is also a data pipeline’s necessary components.
Key Features To Look For In A Data Integration Platform
Rather than depending on DBMS or cloud vendor tools and manual ETL coding, more companies use database and cloud-agnostic data integration platforms to accomplish data integration activities. A single data integration platform enables you to perform a wide range of operations on numerous data sources more quickly and effectively. Following are a few essential qualities to look for in a data integration platform:
- A low-impact method of gathering data on changes: Low-latency data replication and real-time data input are made possible by log-based change data capture technology, which has no negative impact on the performance of source systems.
- Affordability: An easy-to-use graphical user interface (GUI) and task-oriented wizards can speed up implementation, reduce the total cost of ownership, and allow for continual refinement. There shouldn’t be any need for manual coding.
- Ability to grow and change: Integrating large amounts of big data necessitates scaling up as data volumes increase and keeping pace with new use cases and technologies as they emerge. Enterprise-wide process monitoring and control are both included in this.
Why is Data Integration Important?
If you want to be relevant and competitive, you’re going to have to embrace big data. Business intelligence, consumer analytics, data enrichment, and real-time information delivery are all enhanced by data integration, which enables queries across these massive datasets.
Customer and business data management is a common application for data integration services. Enterprise reporting, business intelligence, and advanced analytics are all supported by enterprise data integration, which feeds integrated data into data warehouses or virtual data integration architectures.
Customers, financial risks, manufacturing, supply chain operations, regulatory compliance activities, and other areas of corporate processes can all be viewed as a whole thanks to customer data integration.
In healthcare, data integration is critical as well. Doctors can better diagnose medical disorders and diseases using integrated data from various patient records and clinics.
This data is organized into a single perspective of relevant information from which helpful insights can be drawn. Medical insurers benefit from reliable claims processing and a consistent and accurate record of patient names and contact information due to effective data capture and data integration. Interoperability is a term used to describe the capacity of two or more systems to communicate with one another.
What is Big Data Integration?
When we talk about “big data integration,” we’re referring to the advanced data integration processes that have been developed to deal with the massive amounts of data generated by the Internet of Things (IoT) and other sources, such as web data, social media, machine-generated data, and other sources of big data.
Scalability and high performance are essential for big data analytics platforms that require a common platform that supports profiling and data quality, allowing the user to get the most up-to-date perspective of their organization.
Traditional ETL procedures are supplemented by real-time integration techniques used by big data integration services, which continuously stream dynamic data context. There are several best practices for real-time data integration that deal with its dirty and moving and temporal nature, such as more testing and stimulation upfront, real-time systems and applications should be adopted, ingestion engines implemented, resiliency established in each phase of the pipeline, and APIs used for better insights into the data source.
To sum it up, big data integration has many benefits, from data integrity to data quality to a seamless knowledge transfer between different systems. Data integration platforms ensure easy availability and faster connections between data stores. Such integration increases efficiency and Return On Investment (ROI) for a better partner and consumer experience.
Best Data Integration Platform
HazenTech continues to reinvent the term “data integration,” inventing new tools and technologies that are powerful, easy to use, and versatile for data-driven enterprises. A data integration tool must be able to handle a wide range of data types and storage platforms—in dynamic, hybrid computing environments—and speed data pipeline construction for enterprises to collect and analyze more data than ever before. It’s precisely what HazenTech’s data Integration does.
Incorporating a robust data ingestion framework, next-generation change data capture, automated data warehouse, lake creation and operation, and a self-service catalog that provides on-demand access to analytics-ready data, our universal and real-time data integration platform does all of the above.
Thanks to HazenTech’s data analytics integration services, big data integration is a breeze, unequaled in the industry. When a billion entries are added to the HazenTech open-source SQL engine every second, business analysts may access it in real-time. Using HazenTech’s data integration utility, you can connect to a topic and consume messages in real-time, with data being quickly loaded into an HazenTech target database.
Conclusion
A word of advice to companies considering upgrading to a better data integration platform: do your homework first before settling on a solution that works for your company’s unique data needs. Nevertheless, if you combine HazenTech’s real-time, analytics-ready cloud data with your own, you’ll have access to the most up-to-date, accurate data in the cloud of your choosing. Head to our website and learn more about data integration protocols.
Our team at HazenTech makes validated suggestions and gives direction for more intelligent integration. Get in touch with HazenTech and allow us to help you to make more intelligent organizational decision-making.
