Real-Time Data Integration Solution

HOW? — Log-Based Change Data Capture


Minimal impact

Log-Based CDC captures changes and moves only the changed data, creating little impact on the source systems.


Fast performance

HVR directly reads the logs on the file system allowing highly efficient change data capture, supporting large volumes of data.


More flexibility

Log-Based CDC capture supports more data operations such as truncates, and enables support for DDL capture.

Challenges batch processes present to modern organizations


Growing data volumes

Growing volumes of data are making it increasingly difficult to load all data within the available quiet period. This means data available for analysis can be out of synch with operational data.


Decisions on old data

Decisions have to be made based on the analysis of historical data alone.


Uptime is critical

As organizations increasingly serve customers 24×7, time windows available to update data on a batch basis have decreased. Many organizations have reached a point where they can no longer stop their operations for any amount of time to update the data warehouse.

HVR enables continuous, real-time data integration with log-based CDC


Data in sync

Log-based CDC reduces the risk that data in production databases and data warehouses will be out of sync


Decisions on the freshest data

Ensures that decisions can be made based on analysis of the latest data


Zero downtime

Supports 24×7 operations

HVR overview

Broad platform support

HVR was built for hybrid environments and enables a continuous data integration strategy.

Whether you’re moving data to a data lake or data warehouse, from on-prem to the cloud, we support it.

Organizations need to be able to integrate data sources where it makes sense and to have to manage that integration in hybrid scenarios. While it is possible to use traditional ETL solutions to integrate data from cloud-based solutions, they were not designed for this purpose. Similarly, traditional ETL systems are unable to take advantage of native functionality in streaming environments.

HVR was designed from the ground up for hybrid environments. It is therefore more streamlined and requires less manual coding than an alternative solution that bolts this functionality onto a traditional data integration product.

  • All
  • Source
  • Target
Amazon Redshift
Amazon RDS Replication Solution
Amazon Aurora on PostgreSQL
Amazon S3
Amazon Aurora on MySQL
Amazon Kinesis
Snowflake on AWS
Snowflake on Google Cloud Platform
Snowflake on Azure
Google Cloud SQL
Google Cloud Storage
Google BigQuery
Apache Kafka
Apache Hive
Apache Cassandra
Microsoft SQL Server
Azure SQL Database
Azure Synapse Analytics
Microsoft Azure DLS
Microsoft Azure Blob Storage
Azure Event Hub
Databricks Delta Lake
IBM DB2 on z/OS
IBM DB2 iSeries (AS400)
Hive ORC
Actian Vector
HVR Agent Plug-in

Don't see the platform you're looking for? Learn more about our API agent plug-in.

Webinar: goeasy’s road to real-time analytics

goeasy, a financial services company based in Canada, provides consumer loans with a promise of fast decision making. They needed a faster way of integrating data between their SQL Server systems.

In this webinar, Anu Oladele, Director of Data Science & Business Insights—goeasy and Joe DeBuzna, VP of Field Engineering—HVR, discuss how they worked together to architect a solution that enabled goeasy to achieve real-time business insights and faster loan processing speeds. They discuss the business use case, their process, and share a technical overview of the solution developed as well as plans for scaling their solution.

Case study – CACI: real-time data integration into a data warehouse

About: CACI provides information solutions and services in support of national security missions and government transformation for Intelligence, Defense, and Federal Civilian customers.


  • Integrate student administration data with government systems
  • Provide real-time data integration for CACI and third- party data to a data warehouse.


  • Oracle
  • Microsoft SQL Server

Use Case: On-premises and cloud-based data integration

Whitepaper: continuous real-time data integration solution for real-time analytics

Understand why ETL data integration approaches are insufficient and how log-based CDC enables continual data integration

Because companies once made business decisions based on reports of historical data, they could only address events after the fact. Today, business leaders are increasingly looking to improve responsiveness by using real-time analytics to understand and address business challenges in real-time. To achieve real-time analytics, businesses need to integrate data from a variety of sources in real-time.

In this whitepaper you will learn:

Why traditional extract, transform, and load (ETL) data integration approaches are insufficient to meet the demands of real-time analytics

How organizations can achieve business agility through real-time analytics by taking advantage of solutions that integrate data in a highly flexible manner

How to keep up with growing data volumes and shrinking processing windows

Why Log-Based Change Data Capture is an optimal solution for real-time data integration


Resources from our blog

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