Stelo + Amazon Web Services

The short answer is yes.

Stelo takes full advantage of open standards such as DRDA, SQL, ODBC, and JDBC to maximize compatibility and interoperability within an enterprise network. We are an active member of The Open Group software industry consortium, which was responsible for the adoption of DRDA as an industry standard for database interoperability.

Currently, Stelo supports more than 30 ODBC databases and our Kafka interface can also be used to communicate with cloud-based streaming services such as Azure Event Hubs for Kafka, the Oracle Cloud Infrastructure Streaming service, Amazon Managed Streaming for Apache Kafka, and IBM Event Streams. Stelo can also populate Azure Data Lake Storage Gen2 (ADLSg2) and similar NoSQL data repositories.

Stelo continues to use our open standards approach to ensure that we meet emerging replication requirements. We are continually adding support for new technologies while supporting legacy systems. Stelo is designed to grow with your organization rather than lock you into any specific database platforms.

Stelo offers simple installation and GUI-based replication. Our user-friendly, browser-based GUI does not require a programming background to set up or operate. The easy-to-use interface comes standard across all Stelo solutions for snapshot, incremental, and bi-directional replication. Once running, Stelo reliably operates in the background without needing dedicated engineering support to maintain and manage.

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.

Once replication source and destination databases have been defined, Stelo uses change data capture techniques to capture and apply only change data. Our multithreaded processing is far more efficient than other replication tools on the market, not only to provide near real-time data availability, but also best in terms of reliability and robustness. Stelo’s custom technology blurs the overhead of the WAN connection and effectively delivers near real-time (under 1 second) replication, regardless of whether it's going into cloud or on-premise locations. If your use-case requires sub-second latency at all times, we will size the replication instance according to your high-water mark of DML activity to ensure near real-time data availability.

Yes, Stelo has an automatic method to build out data warehouses, so we can do the heavy lifting for you. It works well whether you're doing a migration up to the cloud or are continuing with ongoing replication.

We encourage you to configure Stelo to create the destination tables on the SQL Server. In part this is to ensure the proper mapping of change data captured on the source system and the destination table. Also, if the customer's High Availability (HA) software utilizes *AFTER images only for capturing change data, then Stelo must rely upon a synthetic column (the Relative Record Number or "RRN") to uniquely identify the row in the destination table, and it is unlikely that some tools such as the JDE tool would provide this column. Therefore, it is best to allow Stelo to create the destination, including this column for each table. Alternatively, if the HA software configures *BOTH images in the journals then we can dispense with the RRN column and just utilize any unique index (including a primary key) to identify rows.

Another issue that should be addressed in the planning stage is the use of Unicode data types on the destination SQL Server. We strongly encourage customers to use NCHAR/NVARCHAR data types for SQL Server as this provides the greatest fidelity when mapping data from IBM i EBCDIC to Windows and other non-EBCDIC environments.

Our recommendation is to allow Stelo to create the tables and exploit UNICODE data-types, and then use the tool to create any additional indexes.