HVR Technology

Welcome to our HVR Technology Resources

Looking to deepen your understanding of how HVR technology works? We created this page so that you can take a deeper dive. If you are interested in learning more, we invite you to join one of our technical live demos or contact us.


On this page you will find:

  • Technical White Paper
  • Product Videos
    • Log-Based Change Data Capture
    • Bi-Directional Data Movement
    • Big Data Compare-Ensuring Data Correctness
    • HVR Real-Time Data Replication Topolgies
    • Data Lake Consolidation Using HVR
    • Agent and Agentless Data Integration Architecture
  • Demo Videos
    • Three Data Lake Deployments Examples Using Different Technologies
  • HVR Technology Blogs
    • HVR 5.2 Data Lake Release
    • Making Sure Your Data is in Sync
    • Change Data Capture and Impact on Database Processing

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Looking to see if HVR is the solution for your data integration challenge? Visit our solutions page to see how HVR solves many common data integration challenges.

 

HVR Technical Whitepaper

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Gain control of your growing volumes of data and move it faster than ever before . . . simply and in real-time.

In this whitepaper we will cover how HVR can help you move, integrate, and replicate data in your unique environment for real-time analytics.

Learn:

  • How you can gain better control of your growing volumes of data
  • How you can integrate high volumes of data between legacy and new systems for real-time availability
  • Topologies and platforms HVR supports

 

 

Product Videos

Video: Log-Based Change Data Capture


Benefits of Log-Based Change Data Capture

In this video, Learn about the benefits of Log-Based Change Data Capture and why it is an efficient and necessary feature for high volume data movement.

Video: Bi-Directional Data Movement

Bi-directional data movement need not be feared when using HVR for real-time data integration. In this video, Glenn Goodrich, Director of Enablement, explains how bi-directional data movement can be accomplished efficiently, accurately, and in real-time with HVR.

HVR includes a rich feature set that enables customers to move their data bi-directionally with a single channel definition:

  • Hub and Spoke Architecture
  • Data Compression
  • Encryption
  • Loop Avoidance
  • Collision Detection


Video: Big Data Compare—Ensuring Data Correctness


How do you make sure your data is bit correct in the source and target systems? In this video, learn how the Big Data Compare feature in HVR enables you to make sure your data is correct and in sync. VP of Field Engineering, Joe deBuzna, explains how the Big Data Compare function works in HVR, why it is important for your business, and how it can identify and mitigate errors.

Video: HVR Real-Time Data Replication Topolgies

In this video, learn about the many different ways HVR can move data between systems while keeping the data safe, secure, and accurate. HVR can not only move data from one source system to another but can move data bi-directionally and multi-directionally while maintaining efficiency and accuracy.


Data Lake Consolidation Using HVR


How HVR Feeds Your Data Lake from Traditional Sources in Real-Time

A Data Lake is a destination for a variety of data types. IoT, Streaming Data, and data from traditional systems are commonly deposited into the data lake for reporting. HVR is a solution for moving data from traditional sources into your data lake for real-time updates.

In this video, learn how HVR enables high volume data movement into data lakes. Most of all, HVR includes features that allow you to validate and monitor your data so that you always know that you are sending your teams accurate and reliable reporting data.

Video: 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.

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

 

Demo Video: Three Data Lake Deployment Examples using Different Technologies




Data Lake is a concept, not a technology” is a common quote we hear about Data Lakes. Data Lakes are most definitely a concept in how companies manage growing volumes of data sources and types. The technologies selected are a realization of this concept. So where to start?

In this webinar, learn about technologies that have served as a data lake for some of the largest organizations in the world. These organizations have used different technologies and strategies for managing their data lake. At HVR, we help these organizations integrate their data from multiple sources into their data lake.

In this webinar, HVR VP of Field Engineering, Joe De Buzna and CTO Mark Van de Wiel, share customer successes, challenges, and technologies used in their data lake projects.

What you will learn:

  • Three different technologies used for “data lakes”
  • Considerations when deploying a data lake in the cloud
  • How to continuously integrate data into your data lake
  • How to create a data lake that can be trusted

If you would like to discuss the different technologies for your data lake project, we welcome you to contact us.

 

HVR Technology Blogs

HVR Technology Blog Related Posts

Making Sure Your Data is In Sync

When you have data stored in multiple applications and want to consolidate it into a central data warehouse or data lake, you need to make sure the data in the source and the target remain consistent. But while consolidating data seems like it should be easy, the process is deceptively difficult. Successfully keeping data synced requires you to consider a number of factors, including…

HVR 5.2 Data Lake Release

HVR 5.2: Data Lake Release

In the last 8-12 months, we have seen an increased interest in continuous integration into data lakes implemented on file systems like Hadoop and S3. When early data lakes may have been centered around sensor-generated data, logs, or social media, we see an increased interest in building solutions on top of data lakes also using data from traditional relational database applications like ERP systems.

Change Data Capture

Change Data Capture and Impact on Database Processing

Most databases focused on transactional database processing write changes to a transaction log to ensure they can meet the availability and consistency requirements prescribed by principles of ACID (Atomicity, Consistency, Isolation, and Durability) database processing, going back to the early 1980s. Over time the database…

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