So you’re on the cloud or plan to move there very soon.

You are well past the research phase, cost calculations, selecting a cloud provider and, setup of supporting systems and services you need. You are joining a rapidly growing community that is reaping the benefits of elasticity, speed and agility.

Data is many times one of the drivers to move to the cloud, and with data comes the need of moving it and integrating it with a plethora of other data sources. So you will need to plan for this as well. But, there are a number of misconceptions associated with cloud data integration and we are going to debunk the top 3.

Myth #1: I need to write scripts and code in order to move data around.


Indeed, there are many challenges moving data from one platform to another. Because of the many formats that data needs to be ingested and then processed, you had to resort to coding to move and process your data. That’s no longer the case. Cloud services like (ahem) Xplenty can help you perform these data integration tasks, without writing a single line of code. ETL on the cloud is still a challenge but, with the right tools, you can save yourself massive headaches, time and money setting up and running your data processes.

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Myth #2: It’s difficult to move data from one cloud platform to another.


You can and sometimes should use more than just one cloud platform. Sometimes a certain cloud provider may offer one service that others don’t (or a superior service) or, you may want to spread your activity over more than one platform for redundancy and backup purposes. Most cloud providers have utilities that help you move your data from other cloud platforms and, you can also use data integration services that perform these tasks.

Myth #3: I need to be 100% on the cloud.


You can be, if it makes the most sense for your business and technical requirements. But, it’s completely optional. These days, most companies opt for the hybrid approach. You store data in an on-premise data center or servers and then use a public cloud for data processing and analytics. Some use it the other way around, operational data is stored on the cloud and once integration and processing of the raw data is complete, analytics is processed on a different platform.

For example, companies that store data in an on-premise operational data store, may choose to use Amazon Redshift or Google BigQuery for their analytics needs. They move the raw data to one of those services and run processing and integration as part of this process.

Bottom line, cloud data integration doesn’t have to be a complex endeavor. Most of the time you will not need a special skillset to perform the most common ETL tasks and requirements. Just be sure to pick the right tools.