Data lakes present a cost-effective option for keeping data in any format and going beyond data capture for deeper analysis. For destinations such as Confluent, Stelo injects data into the cloud using KAFKA to make data available for Confluent Consumers. Stelo efficiently streams data into delta lakes with a focus on fidelity, scalability, and cost.
Stelo easily connects and distributes change data to Confluent so users can engage with their data for cutting edge analysis, machine learning (ML), and artificial intelligence (AI). Stelo is compatible across all data ecosystems to efficiently propagate changes into delta lakes while maintaining the same sequence between the source and the destination. Optimize your data with Confluent in tandem with your traditional data warehouse and scale your data pipeline into a cost-effective solution.
Customizable
Anywhere-to-Anywhere
Avoid vendor lock-in. Stelo uses heterogeneous replication for bi-directional support across all source and destination types. Our open-standards approach allows us to remain vendor-agnostic while providing highly flexible deployment models.
Quick Setup
Rapid Deployment
Streamline your deployment plan without costly delays. Stelo typically deploys in less than a day and cuts production time down from months to only weeks.
Easy-to-Use
Set It and Forget It
Simple installation with GUI interface, configuration wizard, and advanced tools makes product setup and operation straightforward, with no programming needed. Once running, Stelo reliably operates in the background without requiring dedicated engineering support to maintain and manage. Alter, add, and drop schema changes are replicated automatically.
Low Impact
Near-Zero Footprint
Our process provides ultra-low CPU load (less than 1% typical) to minimize production impact and avoid operational disruption. No source or destination software installation required. Only transfer data you need thanks to Dataset Partitioning.
Cost-Efficient
Unlimited Connections
A single instance can support multiple sources and destinations without additional licensing. The Stelo license model is independent of the number of cores to either the source or destination, so you only pay for the capacity required to support the transaction volume. Your data ecosystem can change over time without additional costs.
Reliable
Automatic Recovery
If a connection is broken, no data is lost. Stelo will automatically resume replication without needing to re-baseline in the event of a connectivity failure.
Penn Foster is an educational institution whose mission is to help students gain the knowledge and skills they need to advance in their field or start a new career. With growing enrollment, the institution decided it was time to transition from a traditional, relational data management solution to a cloud-based, big data solution that works for both their current structured data and their anticipated unstructured data.
After all files were initially dropped into Microsoft Azure Data Lake Storage (ADLS), it became clear that the coding of individual files downstream would be a strain on their resources. In anticipation of their future needs, Stelo offered a pre-release deployment of Stelo V6.1, allowing Penn Foster to leverage the software’s new delta lakes support functionality.
This functionality allowed Penn Foster to:
- Prove their cloud-based architecture at scale
- Combine technologies for faster access, faster updates, and improved reliability
- Minimize the hands-on effort required to transfer and access data
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