Stelo + Big Query
Transactional databases rely on data mirroring to populate data warehouses or to off-load reporting, but this is not always appropriate for no-SQL environments like Google Big Query where it is crucial to stream large amounts of data quickly and reliably, for data warehouses, machine learning (ML) and real-time analytics. With MERGE SQL functionality, compressed change data can be delivered using micro-batches, which reduces latency and exploits Relational Database Management System (RDBMS) optimizations like column-store indexing.
Stelo is designed to complement current data management infrastructure by skillfully mirroring and streaming data into both relational and No-SQL environments without any complex programing. For No-SQL destinations, data streaming doesn’t require additional licensing add-ons. Stelo includes this capability for all deployments because design for scalability is one of the best ways to futureproof data management systems.
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.
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.
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.
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.
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.
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.
Evolving Your Data Management Strategy Beyond Data Warehousing
Data is much more alive and dynamic than ever before. It’s not just information: It’s action. Data feeds machine learning (ML) and artificial intelligence (AI) algorithms, connects workflows, and provides meaningful insights. The “new frontier” of data ingestion goes beyond warehousing to enable a lot more choice. Opting for an open-standard system, in which data is independent from the software used to analyze it, allows companies to leverage a range of different tools while maintaining high performance.
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 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.
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.
Quick support is available for training, troubleshooting, version updates, and data replication architecture. 24/7 Urgent Incident Support is included in annual subscriptions.
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.
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.
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
How Stelo V6.3 Helps You Master Data Integration
Sunsetting: What to Do When Your Data Replication Tool is No Longer Supported
Unboxing Stelo V6.1: MERGE Support
Unboxing Stelo V6.1: PowerShell Scripting, Support for Linux and Container-Based Deployment
These three steps will help you ensure Stelo works for your needs, then seamlessly deploy your solution.
Schedule a Demo
Our expert consultants will guide you through the functionality of Stelo, using your intended data stores.
Test the full capability of the software in your own environment for 15 days. No obligations.
When you're ready, we can deploy your Stelo instance in under 24 hours with no disruptions to your operations.