In its simplest form, enterprise data integration combines data from various sources and loads it into targets using ETL tools. Data warehouses are the most popular targets. However, enterprise data integration also extends to use cases like data quality and MDM.
As analytic techniques improve, the speed of gathering and sharing data becomes the new driver for enterprise data integration. However, speed is not a design goal of legacy enterprise data integration tools. On the contrary, users must slowly and carefully establish rules for mapping and integrating data according to strict requirements.
Transition to the cloud
Cloud operations further accentuate the need to quickly synchronize data with a multitude of systems and services. Speed is important because self-service analytics and big data services make more ‘ad hoc’ requests for data than ever before.
How do you fulfill the need for speed and agility with traditional enterprise data integration tools like Informatica or Talend? David Linthicum, Chief Cloud Strategy Officer at Deloitte Consulting, recommends looking at the new generation of tools emerging in this domain. For example, Griddable has brought to market a solution to fill a capability gap in the enterprise data integration market.
A common scenario
Consider a scenario with one data source copying data to three targets in different clouds. Further, each target requires different replication rules and policies. Reporting runs on the first target database, partners access the second, and new digital production apps run on the third. The reporting and partner databases are in AWS and the production app database is in Azure.
For this use case, traditional enterprise data integration products will require three projects. Further, each project will need different mappings and rules. This cumbersome approach works with limitations for batch updates.
However, what if a last-minute request for some marketing campaign analytics appears for the reporting database? Or, what if the data needs to be updated in real time? These scenarios are not complex. In both, data needs to move in real-time from source to targets with transactional integrity for correct analytics. Unfortunately, the approach taken by traditional enterprise data integration tools adds unnecessary layers of complexity and delay.
Another option is a replication tool such as Golden Gate or Attunity. These tools also originate from the pre-cloud era. As a result, users must compensate for the lack of cloud elasticity, high availability and in some cases, schema migration. As a workaround, users can build scripts or modify code to accomplish ‘one to many’ replication. Unfortunately, licensing costs of these products don’t fit the cloud model either. Thus, these tools become an expensive and over-complicated way of accomplishing a simple result.
The strength of Griddable resides in its combination of a robust and versatile architecture with a phenomenal ease of use. Consequently, Griddable setup is complete and data is flowing in about 10-15 minutes. Enterprise data integration tools cannot approach this ease of use.
Griddable does not replace traditional enterprise data integration tools; however it complements them nicely on use cases they do not address well. Griddable’s modern architecture brings scale, robustness, versatility and transactional integrity. This 2-minute video explains how Griddable distinguishes itself:
This video illustrates how, without any training or particular set up, a user can start moving data.
To see Griddable in action, click the “Live demo” button at the top of this page.
Best to you with your transition to the cloud!