Over the course of the last few years, companies steered through dynamic waters in deciding whether to have their data integration architecture on-premise or in the cloud. Many are slowly transitioning to cloud-based architecture by deploying a hybrid model. Every model has its pros and cons, but one thing is inevitable – on-premises is becoming the new legacy.
Data integration is the process of combining data from different sources into a single, unified view and is an important part of any organization’s IT infrastructure. Integration begins with the ingestion process and includes steps such as cleansing, ETL mapping, and transformation. Data integration ultimately enables analytics tools to produce effective, actionable business intelligence.
It all comes down to quality data and data completeness.
The old legacy systems are often difficult to replace, so companies prefer to add new applications, rather than replace old ones. This led to an explosion of apps being used in a business environment. Today, companies of all sizes and from all industries deal with increasingly hybrid and distributed data environments with siloed data.
As a result, every company now becomes a “software” company. In January 2020, we reported that a typical mid-sized company houses 900 applications, which leads to a problem of app fragmentation. The trend didn’t stop and as of April 2020, that number jumped to 1000. Some of that is also due to epidemic and a sudden change in work models, where many employees had to work from home and use more applications than before.
Cloud data integration combines the power of traditional data integration capabilities with modern and agile approaches to data-driven solution development while focusing on natively supporting the cloud – but it comes with many more perks.
Cloud will always be cheaper:
Server and storage resources tend to cost less on cloud platforms compared to traditional on-premises resources. Furthermore, the cloud provider handles server capacity planning, optimization, upgrades, and maintenance, taking those time-consuming distractions off the plates of data management professionals Finally, by using cloud-based servers and storage, data management staff need not devote time to system integration or burn up the budget on capital expenditures.
Cloud is scalable:
Many data integration workloads ramp up quickly, demand considerable server resources, and then subside just as quickly Common examples include data ingestion, data transformations, and preprocessing data prior to loading targets When these occur, an elastic cloud can automatically marshal needed resources, then reallocate resources after intense data integration workloads complete.
Cloud houses a single version of the truth:
Centralizing shared resources and services makes data management consistent and governable while increasing developer productivity and collaboration. Resources and services can thus be shared even more broadly among geographically dispersed people and departments, as well as applied in production among the multiple platforms of hybrid data integration workflows.
Business intelligence, analytics, and competitive edges are all at stake when it comes to data integration. That is why it’s critical for a company to have full access to every data set from every source.