Enable High-Volume Data Movement for Real-Time Analytics
Our solution enables your company to access accurate and up-to-date data without the headaches that can come with moving large volumes of data.
24 x 7 access to real-time data means better decision making for your company, giving it a competitive edge. But delivering that access is easier said than done.
Real-Time Data Challenges Include:
- Latency inherent in high-volume data movement
- Data movement between disparate sources and targets in complex environments
“We have been using HVR now more than ten years for our flight planning system business.”
– Senior Database Software Architect, Lufthansa Systems Airlines Operations Solutions
We spoke with Matt Aslett about why it is imperative for organizations to employ a continuous data integration strategy for real-time analysis.
Run Your Queries Fast on Up-to-Date Data
Our solution enables you to stay competitive by continuously analyzing changing data generated by transactional systems, machines, sensors, mobile devices, and websites.
Benefits of our Real-Time Analytics Solution Include:
- Reduce OLTP overload
- Move large volumes of data
- Accelerate queries
- Easily integrate with many different types of databases and applications
Large volumes of data are captured by our log-based change data capture capability in real-time out of commonly used OLTP source databases to a variety of analytical relational target databases. Our data compression functionality enables you to quickly run queries on up-to-date data in your data warehouse.
These features and more make data availability continuous and efficient for your business.
Lufthansa Uses HVR for Real-Time Analytics
Lufthansa uses HVR to populate hundreds of distributed databases with continuously changing data pertaining to routes, flight paths, air traffic, flight restrictions, weather conditions and more in real-time.
Lufthansa Systems, a division of Lufthansa Airlines, is one of the world’s leading providers of IT services in the airline industry. It serves roughly 300 national and international airlines comprising more than one-third of all airlines worldwide. Among its many offerings, Lufthansa Systems offers Lido/FPLE (flight planning services), which determine the most effective flight routes in terms of cost, fuel and time. By optimizing flight routes, Lido/FPLS generates millions of dollars in extra profits for its customers each year.
The data necessary for up-to-the-minute flight planning must be replicated to the central data repository at Lufthansa Systems. Optimized plans must be replicated back to the customer airlines.
- HVR replicates bi-directionally between the central source to hundreds of different target databases at customer sites all over the world – even over restricted network links.
- Some customers use HVR for HA and reporting.
Read More about how Lufthansa solved this challenge using HVR and enabled real-time reporting for flight planning.
Different Use Cases, Different Platforms
Organizations Using HVR for Real-Time Analytics
|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|
Common Question We Receive:
Why Log-Based Change Data Capture for Real-Time Analytics?
Log-based CDC is generally considered the superior approach to change data capture that can be applied to all possible scenarios including systems with extremely high transaction volumes. Transactional databases store all changes in a transaction log in order to recover the committed state of the database should the database crash for whatever reason. Log-based CDC takes advantage of this aspect of the transactional database to read the changes from the log.
The biggest benefit of log-based change data capture is the asynchronous nature of CDC: changes are captured independently of the source application performing the changes.
HVR supports log-based CDC for all supported relational database sources. Trigger-based capture is still supported on most source databases for legacy reasons, or in scenarios when the source database does not provide the required functionality to perform log-based CDC (e.g. SQL Server Express Edition does not support supplemental logging of additional columns).