Data Streaming

Data mirroring involves copying data from one database to another to ensure consistency and availability across different locations. This process is typically used for data warehousing, business operations and to off-load reporting. Data streaming, on the other hand, is the continuous transfer of data from a source to a destination in real-time. It is commonly used for real-time analytics, monitoring, and feeding data into Artificial Intelligence applications.

These terms can get confusing, but understanding the 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.

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 thirty 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 in to any specific database platforms.

Analyzing a company’s ongoing transactional or operational data can provide key insights that drive business decisions and processes. Many companies offload these operations to other databases, data warehouses, or data lakehouses to reduce source system load and save costs. However, replicating dynamic data from multiple source databases to one or more destination application requires real-time CDC to ensure all updates are automatically and reliably reflected. In other words, business insights are only effective if data changes are accurately captured and delivered across the connected databases over time.

CDC is typically the first stage in the data ingestion process and represents the “extraction phase” in the ETL and ELT approaches. CDC strategies often demand a tradeoff between latency tolerance and change volume requirements, but Stelo’s approach provides the ideal balance between fast, high-volume performance and lossless data capture. Our process can simultaneously propagate a source system’s information changes and structure updates to destination platforms so that the data appears in the correct sequence at the same time. In addition, the cost-efficient method merges incoming data flows into one stream, as opposed to using many streams, which becomes expensive. Read our blog posts about moving data into delta lakes or using delta lakes for future scalability to learn more.