Technology Solutions

Stelo + Databricks

Stelo helps you connect data from Databricks

Businesses seeking further data processing and exploration are adopting Databricks Lakehouses, Apache Spark, or similar machine learning tools for structuring heterogeneous data to enable big data applications. Stelo works with Databricks for easily connecting and distributing information to these destinations. If your target supports open-standard frameworks, then you’re set to go: Stelo's delta lakes connector is compatible across your technology stack to efficiently populate your data lake to work in tandem with your traditional data warehouse and scale your data pipeline into a cost-effective solution.

For no-SQL destinations, such as Databricks, Stelo employs a specialized Stelo Spark Connector to efficiently propagate changes  while maintaining the same sequence between the source and the destination.  Stelo supports many source database products on the market, so customers requiring high-speed access to legacy system change data for driving information into new-generation data engines, such as Microsoft Azure, will find their requirements well-met by Stelo’s real-time replication.


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Flexible Deployment Icon_Noun 4351350_Green Customizable


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.

Rapid Deployment Icon_Noun 3967969_Green 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.

 Time and Labor Icon_Noun 4636078_Green 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.

Near-Zero Footprint Icon_Noun 1465960_Green 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.

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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.

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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.

Stelo and Penn Foster Partner to Create an
Adaptable Data Lakehouse

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


What happens if data connectivity is lost?

Unlike other replication software, there is no need to re-baseline in the event of a connectivity failure. In either a disaster scenario or planned downtime, all unaffected sources and destinations continue to be processed by Stelo. For the affected server or servers, Stelo checkpoints replication and will automatically restore replication as soon as connectivity is restored. This process is automated and requires no user intervention.

Data lake, delta lake, data lakehouse: what's the difference? And where do data warehouses fit in?

Data Lake vs Data Warehouse vs Delta Lake vs Data Lakehouse: The terms can get confusing, but understanding these underlying pieces is critical for ensuring you set up a cost-effective data integration architecture.

A data warehouse is a relatively limited-volume data repository and processor of aggregated structured data from relational sources. The replicated data mirrors the source database to provide traditional query processing. Common applications include data analytics and business intelligence (BI).

A data lake is a large-volume repository of aggregated structured and unstructured data from relational and non-relational sources. Key applications include machine learning (ML) and artificial intelligence (AI).

A data lakehouse is a big-data architecture that combines benefits of both data warehousing and data lakes, supporting data analytics, BI, ML, and AI applications. A delta lake is an open-source storage layer placed above a data lake to create a data lakehouse, providing critical data governance and scalability for future-proofing your organization.

Stelo's delta lakes connector is compatible across your technology stack to efficiently populate your data lake. Our process can work in tandem with your traditional data warehouse to scale your data pipeline into a cost-effective data management solution. Read our "5 Questions to Answer Before You Start Moving Your Data to Delta Lakes" blog post to learn more about how to get started.

Can Stelo be deployed on-prem and in the cloud?

Yes. Whether you want to deploy either entirely in the cloud or used between on-prem and cloud databases, Stelo’s deployment models are designed to maximize performance without sacrificing flexibility.

Cloud technologies enable choice. Some companies prefer to stream data into cloud-based delta lakes while maintaining their existing data warehouse; that way, they can take advantage of new technologies from companies like Synapse while maintaining their existing applications. Others would prefer to get rid of their in-house data center all together.

Stelo encourages customers to make improvements by integrating technologies that allow them to use their data better. Advancing data management strategy is not about displacing current software and hardware investments; it’s about making it easier to leverage new technologies that can unlock your data’s embedded potential.

Support Features

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Accessible Support

Quick support is available for training, troubleshooting, version updates, and data replication architecture. 24/7 Urgent Incident Support is included in annual subscriptions.

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Highly Experienced Team

Stelo’s technologists have more than 30 years' experience developing reliable data software. Whether you need basic support or have a tricky technical challenge, we can work with you to solve any problem.

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End-to-End Proficiency

Our team has detailed knowledge of every data platform we support and can troubleshoot end-to-end replication pairing in heterogeneous environments to ensure the pairings are working properly.

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Constant Evolution

Unlike some other solutions, Stelo won't go out of date. New source and target types are continuously added through active updates to stay compatible with emerging market requirements.

The Latest from Our Blog

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