Trapped in a Data Silo? Check out Cloudera DataFlow

Decades ago, Caesars Palace spent millions to erect a flashy, elevated moving sidewalk to snare tourists off the Las Vegas Strip and into the casino. With dramatic music, dancing statues and other attention-grabbing enticements, the gaudy entrance played out more like a Disney attraction than a pedestrian pathway.

When it was time to leave, though, tourists discovered that the iconic gambling resort didn’t invest a dime to upgrade the exit. So they still had to hike out on foot.

CIOs are hip to the resort’s technique. It’s top of mind every time a data scientist or business analyst comes to them, demanding the latest machine learning tool or visualization package. Because they understand that, like the foot traffic on Caesars’ sidewalk, data invariably flows in one direction. Extricating is another matter entirely.

Indeed, for all the promise of working with any data anywhere in the hyper-scalar age, data silos are just as prevalent as ever. Arguably by design.

Which is why it’s so refreshing to see more and more enterprises adopt Cloudera DataFlow to incorporate disparate data sources into their business analytics and data science projects. It’s a no-code/low-code data service that gives folks the power to pull in any data set, set it up once and then run with it.

At first blush, that might sound to CIOs like one of the many alluring proclamations that coax businesses to hop onto a one-way data path. Hard to blame them for being suspicious. Lots of cloud-native services boast that they can tame data, no matter the format or location.

“The user experience is seamless,” claims one. Our service “allows data consumers to seamlessly access the data products,” declares another. And a third says it “enables a seamless and secure data experience.”

Cloudera DataFlow is different. Because it is just as straightforward, for example, to set up a point-of-sale stream to flow into Cloudera Data Platform, or CDP, as it is to point it into a data warehousing point solution like Snowflake.

With DataFlow as the foundation, in fact, there are already more than 450 customer-requested connectors available that businesses can use to quickly aggregate data from different formats and locations for specific workloads.

Many of the available connectors don’t even move data into or out of other Cloudera services. One DataFlow connector routes data from Confluent Cloud into Snowflake. Another moves information from AWS’ S3 into Microsoft’s Azure Data Lake Storage.

The list of available traffic routes is growing precipitously, as Cloudera accommodates customer requests for more connectors. DataFlow is now supporting data from cloud-native services like Dropbox, HubSpot, Spotify and Salesforce. And the latest connector automates flows from Salesforce into S3 or Azure ADLS.

DataFlow does more than facilitate data traffic between far-off waypoints. For example, it can also help enterprises save money by restricting on-demand resources. Without DataFlow automation, businesses risk poor performance from under-provisioning or unnecessary cost overruns by allocating too many resources.

One-way moving walkways on the Las Vegas Strip have all but disappeared. For its part, Caesars Palace shut down its signature pathway a decade ago, and removed it a few years later during renovations that made the resort more accessible from the street. The same could happen someday in cloud-native services, as the demand for egress that’s as seamless as ingress forces providers to respond. In the meantime, business analysts and data scientists should consider Cloudera DataFlow to help ensure they can work on the data they need, wherever they need it.