What Is Real-Time Data Replication?
Do You Need It, and How Do You Do It?
The term “real-time” is used extensively in the information technology industry, but the definition is somewhat ambiguous. Since we describe our product offering as “real-time data replication software”, we thought it would be worthwhile to devote some space on our blog to sharing our definition of the product category. In addition to defining this term we also want to share some insights about who uses HVR’s replication software, how they use it, and the benefits they realize.
1.What is real-time data replication software?
It implies that data is instantaneously copied to one or more places as it is being generated. In practice, there is always some amount of time that elapses between the moment data is generated, and the moment it is copied. That time lag, often referred to as data latency, can be measured in sub-seconds, seconds, or even minutes. So technically, real-time replication is a relative term.
2. Who needs real-time data?
The answer is many companies of all sizes and in all industries. Analyzing our customer base of dozens of companies it is apparent that these companies are using our software to address a range of data related needs in their production environments. The most common use cases include analytics, big data, hybrid cloud computing, geographic distribution, database and application migrations, and data integration.
Examples of Common Real-Time Data Uses Cases
|Industry||Customer||Real-Time Need||Databases Involved|
|Services||Epsilon||Deliver the right message at the right time to the right prospect. Real-time data consolidation into a data warehouse||Oracle production databases, Microsoft SQL Server-based data warehouse|
|Travel/Aviation||Lufthansa||Real-time flight Planning. Bi-Directional Data Movement. Real-time updates to a central repository||Ingres and Oracle|
|Government||United States Coast Guard||Replicate data in real-time from the production system it uses to maintain its fleet of aircraft to a second database for use in reporting||Ingres and Oracle|
|Financial Services||Investment Services Firm||Deliver up-to-the minute stock info to customers||Oracle to Oracle Data Warehouse|
3. What are the most common approaches to data replication?
The most common approach to implementing real-time data replication is based on log-based changed data capture. Some customers use event-based triggers. There are pros and cons to each approach, however HVR believes that log-based change data capture is most appropriate for most use cases.
4. What are the different types?
As with many different software technologies, IT organizations can take either a best-of-breed or a suite approach. In the best-of-breed approach, IT will evaluate their detailed replication needs; select the best tool for these needs, and then integrate it into their IT environment. In the suite-based approach, IT may trade off in-depth functionality in order to deploy a solution from one of their existing major vendors that is pre-integrated with other data management tools.
5. Want to learn more about real-time data replication?
Read about HVR’s Real-Time Data Replication Software. On this page, you will learn about how our software simplifies the data replication process, see the many different database topologies in which HVR’s software can be used, and gain a deeper understanding into the technology behind real-time data replication software.
A great way to learn more about real-time data replication is by reading our Real Time Analytics Whitepaper. In this whitepaper we discuss the shortfalls of traditional data integration techniques, explain the advantages many organizations gain by implementing real-time data integration techniques, and give an overview of HVR’s solution for real-time data integration.
Join a weekly live demo if you would like to learn more about real-time data replication and to see if HVR is right for your organization.
Watch this video where I discuss the history of analytics, and how HVR’s real-time data replication software allows for many organizations to grow beyond predictive analytics and into prescriptive analytics: