Why Organizations Need Data Integration Tool?

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There are many providers of data integration tools. However, ideally, some key features in the data integration tool or tech would be in need by organizations.

FREMONT, CA: It is important to understand where data came from, it’s owner, and it’s relevance to the business along with ensuring trust in it, without which, trust the outcomes derived from that data will also become null.

There are methods to pull data out of a database and place it in a data warehouse. Proven vendors like Oracle, IBM, SAP, SAS, Microsoft, and Informatica have been using the ETL space. But there are newer vendors wanting to transform the data integration market. Google (via the Alooma acquisition), Salesforce (via MuleSoft), Qlik (via Attunity acquisition), and Matillion among others are growing their bases by giving speed, simplicity, automation, and self-service.

Clients want technology that is secure, reliable, scalable, and cost-effective. But ideally, there are some features in the data integration tool or tech that are wanted by organizations.

A container-based architecture provides consistency in modern environments in microservice-based applications as flexibility, portability, and agility is crucial.

There are diverse people who want access to data. In organizations, GUI is for the generalists while the codes are for experts, usually. But now, from evolving modern tools with more automation having no-code/low-code environments to drag-and-drop workflow interfaces, GUI and coding have grown.

An engineer will not want to write unique codes for different tables, as it is very difficult to maintain. Thus, mass sets with common logic or semantic layers are desired.

There can be good outcomes if one uses batch and ad-hoc on historical and streaming data as organizations want to meet real-time needs, and this call for real-time data.

Codes change over time; hence source control should be handy to understand how and why a code changed. Along with this, the ability to support branching and/or merging of codes to cater to newer use cases, new data sources, or new APIs are needed

Focusing on groups, new workflows should be ensured so that there is easy going from source control to dev/test or production. Knowing that deployment is first, enterprises should not forget management due to the iterative nature of data integration processes as users, apps, and data evolve.

As there is fragmented analytics space, integrations with processing engines, BI platforms, visualization tools, prove to be better. Thus, incorporating advanced technology to feed data science teams, like AI and ML services, are necessary.