Comprehensive real-time data replication. Simplified.
A subscription includes everything you need for efficient data replication and integration, whether you need to move your data between databases, to the cloud, or multi-cloud integration, into a data lake or data warehouse.
Fast
Low-impact data movement even at high volumes with Log-Based Change Data Capture (CDC) and compression. Fast data benefits with analytics tools, a stellar customer service team, and more.
Efficient
The most efficient way to replicate and integrate data in hybrid and complex environments is with HVR’s distributed, flexible and modular architecture. Design your integration flow the way you need it and stream data from one source to many, all at once, without needing to define your setup multiple times.
Secure
HVR understands the importance of data security. HVR is the only real-time data replication solution that enables routing through a firewall proxy in hybrid environments. Data is also encrypted for an added layer of protection.
With the combination of flexibility, performance and robustness, HVR has proven to be a very good choice to embed in our flight planning system.
– Senior Database Software Architect, Lufthansa
Table Creation and Initial Load
Mapping data between source and target is automated and made easy.
Log-Based Change Data Capture
Only the changes are moved between source and target, a low impact way to move data.
Data Validation and Compare
Have assurance your data is accurate. Compare data before consumption. Live Compare capabilities allows for compare on data in-flight.
Insights: Statistics, and more
This tool gives you the ability to view how data is moving in real-time, be proactive and identify chokepoints.
Broad source and target support
Whether you’re replicating your data to a data lake or data warehouse, from on-prem to the cloud, we support it.
- All
- Source
- Target
Don’t see your platform?
Please contact us to learn more about our API agent plug-in. This plug-in gives you the ability to connect to a target not listed.
HVR has proven its stability and robustness. It keeps on running and running with minimal maintenance effort. HVR guarantees secure delivery of all our data.
– Director of IT, PostNL
A flexible, distributed architecture with a central point of control
What is a distributed architecture?
In a distributed architecture, changes take place as close to the source as possible for high performance and low impact, enabled via the central point of control, the Hub, and optional agents. A distributed architecture is the most efficient solution for moving data in complex environments.
Benefits of a distributed architecture include:
- Flexibility in how you design your environment
- Performance
- Manageability
How does it work?
Log-Based Change Data Capture (CDC) takes place on or as close to the source server as possible. This is where relevant transaction data is extracted and compressed. The data is then sent across the wire to the central hub, the distributor. The hub guarantees recoverability and as needed queues the compressed transactions.
Separately from the capture, an integrate process picks up compressed changes for its destination that are sent to the target where they are unpacked and applied using the most efficient method for the target.
Example
Sources — Oracle and SQL Server On-Prem
Target — Amazon Redshift Data Lake
An organization using on-premise Oracle and SQL Server databases as sources and a Data Lake in Amazon Redshift will be able to scale to many sources with capture running on the individual database servers. These servers send compressed (and encrypted) changes into the AWS cloud to be applied to Redshift. The changes into Redshift go through S3 and copy into Redshift tables, followed by set-based SQL statements on the target tables, so that on aggregate the analytical database can still keep up with the transaction load from multiple sources.
Data integration architecture: understanding agents
The question of whether or not to use an agent when performing data integration, especially around use cases with Log-Based Change Data Capture (CDC) and continuous, near real-time delivery, is common. Through the use of agents, changes take place as close to the source as possible for low impact and performance. Agents are optional with HVR, it depends on your goals as to whether or not to deploy them.
In this video, HVR’s CTO, Mark Van de Wiel goes into detail about:
- The pros and cons of using an agentless setup, versus an agent setup
- When to consider one over the other
- Two common distributed architectures using an agent setup
Topologies supported
Uni-Directional
Real-Time Reporting Migrations
(Reverse Post Migration)
Bi-Directional
Active / Active Standby
High Availability
Multi-Directional
Multi-Way Active / Active
Geographical Distribution
Broadcast
Data Distribution
Integration / Consolidation
Data Warehouse / Data Lake
Cascading
Data Marts
Management and operations
Total cost of ownership for many software implementations is determined by the administrative effort to manage and maintain the environment once the system goes live.
HVR is extremely well-architected to limit administrative efforts to manage real-time data replication environments:
- The hub is the central point of control and all logs are stored there.
- Administrators have access to an easy-to-use with rich context-sensitive on-line help GUI to interact with the hub.
- All functionality is available through straightforward, easy-to-learn commands that can be used to script automated tasks.
- HVR is upwards and backwards compatible to allow users to adopt new features or implement fixes by only upgrading the necessary installation(s) to take advantage of the latest capabilities.
- Administrators can configure maintenance tasks for lights-out management. HVR will send alerts as needed by email or through SNMP traps into management software such as HP Open View, Nagios or Oracle Enterprise Manager.
HVR provides administrators access to rich statistics it retrieves out of the detailed data replication logs. Administrators can simply get a graphical overview of system activity over time, or slice and dice the data to drill into the detailed DML operations on individual database objects. An integrated process can store historical statistics in the database, or users can simply copy the metrics and create their own charts.
