Top 10 Oracle GoldenGate Alternatives

Luke Smith
Enterprise Solutions Architect
November 25, 2022
Matt Tanner
Developer Relations Lead
November 25, 2022
Matt Tanner
Developer Relations Lead
November 25, 2022
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The amount of data created by enterprises each day is of an unfathomable magnitude. As time progresses, the amount becomes exponentially larger. This increase in data creation has led to the need to also store this data in a highly available and easy-to-access manner. The increased need for the availability of data has led to new paradigms in how we manage, store, transform and derive insights from it. New tools have been developed from the ground up to make the management of data easier and more seamless. In enterprises, data is usually distributed across diverse systems and storage solutions. On top of this, the systems that create and store the data also require constant connectivity with one another. The entire ecosystem of data is now more interconnected than ever.

Creating and storing the data is only part of the picture. The data must also be highly available with the underlying infrastructure supporting it being highly fault-tolerant. Change Data Capture has therefore become an integral technique in the replication and management of data.Change Data Capture, often just referred to as CDC, enables the transfer, storage, and replication of data from one database system to another database or destination system. There are many database systems and data tools available in the market today that enable and support CDC. One such tool is Oracle GoldenGate.

Table of Contents

What is Oracle GoldenGate?

Oracle GoldenGate is a software tool geared towards addressing the requirements of moving data from one location to another. Oracle GoldenGate enables the filtering, replication, and transformation of data from one database to another. In terms of why Oracle GoldenGate would be used, there are several reasons why you may want to use it to move data. These reasons include if you need to move data for backup purposes, replication and database migration, or as part of an Extract, Transform, and Load process. GoldenGate allows data to be moved while still being highly available for the applications that depend on it and ensures data integrity.

Oracle Goldengate allows enterprises to filter, replicate, and transform their data from various database systems. Digging deeper, Oracle GoldenGate is actually a family of products and not just a single component. Some of these components are available on-prem, for use in your own data center, as well as being available on Oracle cloud infrastructure. The products include:

  • Oracle GoldenGate Veridata - the part of the product that compares various sets of data and determines what data is out of sync.
  • Oracle GoldenGate Studio - the GUI that enables the design and deployment of high-volume workloads through a drag-and-drop interface that can generate configurations from templates.
  • Oracle GoldenGate For Big Data - a support layer for writing records into big data formats like HDFS, MongoDB, HBase, Cassandra, Kafka, etc. 
  • Oracle GoldenGate Monitor - provides a bird’s eye view of the instances and associated databases through a web console.

Of course, if you're only requiring data replication for data recovery and protection, you may even be considering using Oracle Data Guard. Oracle Active Data Guard users are extremely limited to using only Oracle databases and other limiting factors, such as the active-passive configuration of Oracle data guard, which limit flexibility and use cases. Within Oracle's suite of products, GoldenGate has much more flexibility out of the box.

Oracle GoldenGate works on the principle of log-based Change Data Capture. This is how GoldenGate replication allows data to be moved while still making it highly available. With log-based CDC, the source databases' transaction logs are monitored to identify changes in the records of a database to replicate creates, updates, and deletes that have been committed. This enables GoldenGate to guarantee the consistency of data between the source and target system. 

The benefits of using Oracle GoldenGate as a data integration solution include low latency and real-time data replication. The platform also has a simple architecture and can be configured to meet different use cases. Oracle GoldenGate is a highly performant Oracle CDC solution that causes little or no overhead to the database infrastructure it is meant to monitor. This is because it leverages log-based CDC as the mechanism for data replication.

Why Do You Need Alternatives To Oracle GoldenGate

Despite the prominence and flexibility of Oracle GoldenGate, it is not the ideal solution for all organizations. As such, there arises the need to use other data tools and platforms to serve the modern and custom business needs of some organizations. 

Oracle GoldenGate has been a highly successful data replication tool within Oracle’s  family of products. Many enterprises use GoldenGate to replicate data across a variety of databases like Oracle, SQL Server, and other popular enterprise-grade databases.  Most of the GoldenGate installations are either a single-node deployment or a 2-node classic configuration where one is primary and one is secondary.  Database replication use cases are, however, no longer limited to only data replication across OLTP databases. Two new use cases have emerged recently that are not a very great fit for GoldenGate.

The first modern use case in data replication is to replicate transactional data into modern analytical cloud platforms. These cloud platforms and the applications using them demand real-time data feeds (via change data capture) from enterprise databases. These modern analytical applications can be classified into three categories: 

  1. Real-time business intelligence applications running on real-time cloud platforms like Redshift, Snowflake, Databricks, SingleStore
  2. Machine Learning and AI applications built and deployed on platforms like Databricks Delta Lake.
  3. Operational/Real-time analytics applications running on real-time data platforms like SingleStore, Redis, Imply, and Apache Pinot.

The second modern use case is database modernization.  Enterprises are modernizing their database stack and embracing modern open-source technologies like MongoDB, Postgres, and Yugabyte. Zero-downtime migrations from classic enterprise databases like Oracle, DB2, and Microsoft SQL Server to platforms like MongoDB, Postgres, and Yugabyte are increasingly common. Achieving a zero-downtime migration requires CDC-based data replication with first-class support for new and modern open-source platforms. 

GoldenGate was built for the previous generation of databases and is not a great solution to address the new use cases mentioned. The functionality needed to accommodate these use cases are extremely relevant for enterprises that want to move their business data to the cloud for storage and analytics. GoldenGate does not have first-class support for modern data platforms like Snowflake or Databricks, though GoldenGate Big Data Adapter mentions Snowflake as a supported target.  For such modern data replication use cases, data engineers may want to look for other alternatives. 

Top 10 Alternatives of  Oracle GoldenGate

Plenty of Oracle GoldenGate alternatives exist. They come in different flavors of on-premise, cloud, and hybrid deployment models. Of these solutions, some will be ideal for small and medium-scale businesses while others will have the capacity and scalability to fit into enterprise deployments. Like any comparison of software products, the right choice will differ from one situation to another. The trick is to fully understand the tradeoffs and choose the tool that provides the most value for the main challenge you are trying to solve. Organizations should also factor in long-term roadmap needs as well to make sure their current and future needs are serviced. The ten alternatives to Oracle GoldeGate that will be covered are:

  1. Arcion
  2. Qlik Replicate
  3. Fivetran/HVR
  4. Debezium
  5. Striim
  6. Hevo Data
  7. Talend Data Integration
  8. SharePlex
  9. Informatica PowerCenter
  10. InfoSphere

Now, let’s take a closer look at each solution!


Arcion is a zero-code, real-time Change Data Capture (CDC) platform designed for massive scalability, guaranteed data consistency, and low latency. Arcion is offered in two flavors, a self-hosted on-premise solution and Arcion Cloud. Arcion’s database connectors are 100% agentless, which means no Arcion agent needs to be installed in the database server or instance. Arcion data pipelines can be deployed without writing a single line of code (all you need is your database login info) and all deployment types can support high volume and throughput with easy configuration. 

Key Features

  • On-premise solution and fully managed cloud service
  • 100% agentless CDC connectors for enterprise databases including Mainframes
  • Guaranteed data consistency 
  • Low latency
  • Massive scalability


  • Arcion offers real-time log-CDC-based data replication for OLTP as well as OLAP systems. 
  • Arcion is the only end-to-end multi-threaded CDC solution in the market, combining with Oracle Native Reader, Arcion is 10x faster than competitors in data replication speed when Oracle is the source. Also, Arcion has native integration with Snowflake & Databricks, Arcion’s ingestion speed can reach 10k ops/sec/table. Not to mention Arcion can support daily terabyte-scale data replication that requires high throughput (read Arcion helps migrating 400M rows in minutes).
  • It supports the automatic schema conversions across a wide variety of databases.
  • It can enable zero-downtime migration from on-premise databases to a cloud database (read how Arcion works with Snowflake & Databricks)
  • Arcion has a zero data loss architecture that guarantees data consistency.
  • Arcion’s patent-pending end-to-end multithreaded architecture provides massive data replication speed as it is highly distributed and parallelized.
  • It comes with replication storage to enable single source multiple target data pipelines.
  • Arcion offers out-of-the-box, automatic schema evolution support (DDL) and SQL to NoSQL auto conversion.
  • Arcion is SOC Type 1 and Type 2, and HIPAA compliant. The enterprise-grade security and compliance standards ensure data governance.


  • Arcion Cloud currently only supports a few core connectors namely MySQL, Informix, Snowflake, Oracle, Databricks, Snowflake, and SingleStore. Select Arcion self-hosted to access all 25+ connectors.

Qlik Replicate (Prev. Attunity)

Founded in 1988, Qlik Replicate is a replication and data ingestion platform that moves data across heterogeneous data systems like a database, data warehouse, and cloud platforms. It empowers organizations and corporations to move, stream, and ingest data across various locations with minimal impact on operational efficiencies.

Key Features

  • Data Replication
  • Data Ingestion
  • Support for most enterprise data sources including mainframes


  • Qlik Replicate supports real-time data streaming for Change Data Capture.
  • Qlik Replicate supports automated replication to a cloud data warehouse without the need for manual coding.
  • Qlik Replicate has centralized monitoring of all resources through a single interface.


Qlik Replicate is not built for the cloud-era, meaning it requires a lot of manual effort to scale in order to handle high volumes of data with high throughput. 

  • Not able to scale: Though Qlik Replicate claims it’s a multithreaded solution, it's not an end-to-end multi-threaded architectural design. Qlik Replicate is not multi-threaded in target databases like Snowflake & Databricks and can’t scale horizontally. It might be good for project that replicate less than 1TB of data per day.
  • Doesn’t have in-built high availability (HA): Qlik Replicate requires a third party clustering solution to achieve HA which will add management complexity.  
  • Can’t guarantee data consistency: Qlike replicate can do DDLs but it can’t guarantee data consistency. It can lead to missing data & errors in target databases.
  • Lack of log storage (staging area for CDC logs): Qlik Replicate doesn’t have the capability to have a staging area. Not only can it lead to data loss, but also need to restart the replication from the beginning, which can result in projects being delayed and making the project more expensive. 
  • Deployment method: only self-hosted deployment model. No managed cloud SaaS offering.

Fivetran (acquired HVR in 2021)

Fivetran is a Software-as-a-Service (SaaS) product that allows companies/organizations/enterprises to move data stored in data silos into more accessible storage locations like data warehouses in the cloud. Fivetran allows users to connect to multiple databases and applications without having to build out data pipelines. 

Fivetran acquired HVR in September 2021. Founded in 2012, HVR is a data integration platform to connect and replicate data across various sources and destinations. HVR supports end to end data replication which means the product can do initial data migration followed by real-time data replication. HVR’s replication technology for most sources is powered by CDC. 

With the acquisition, Fivetran indicated future plans to integrate HVR with its products, which includes to stop all on-prem support by 2026. For current on-prem HVR customers, it would be a good idea to start evaluating on-prem CDC alternatives. 

Key Features

  • No-code platform
  • Prebuilt data connectors 
  • Managed service


  • The main benefit of Fivetran is its ease of use as no manual code is needed to get data pipelines running.
  • Fivetran has a wide range of pre-built SaaS connectors that allow users to tap into various data sources. The data sources available are not limited to databases alone, as users can connect to Customer Relationship Management (CRMs) and social media apps.
  • Fivetran supports data transformation capabilities through the SaaS platform.


  • No self-hosted deployment option, both for SaaS & database connectors.
  • Very limited database connector support.
  • Before the acquisition, HVR was on-prem only. It can take a long time for Fivetran to integrate HVR’s on-prem only technology and make it available for cloud-only.
  • DDL Replication is limited to some sources and targets. It is very well supported for Oracle as a source but not so great support for other sources as described in their docs.  
  • For teams that want more fine-grained control over their data specification needs, Fivetran will be too rigid.
  • End users can only rely on the prebuilt data connectors that are available on the platform.
  • No support for streaming column transformations like computing new partition columns for analytical platforms.


Debezium is an open-source tool for Change Data Capture (CDC) that is based on Apache Kafka. The main functionality of Debezium is that it can capture row-level changes made to a database in the order in which the changes occur. It does this through the use of transaction logs and applications can then react to these changes. The order of events recorded by Debezium is the same as how changes were made to the database. These events are then published as topics to Apache Kafka.

Key Features

  • Change data capture functionality
  • Data monitoring
  • Event streams
  • Speed and scalability


  • Debezium is open source software so it’s free.
  • Debezium allows applications to respond to changes that happen in databases in the order that they occurred.
  • Debezium is fast and can handle large amounts of data as it relies on Apache Kafka which is proven to be a scalable solution (but comes with serious limitations).
  • Debezium supports the monitoring of a wide range of popular databases such as MySQL, MongoDB, PostgreSQL, SQLServer, etc.


  • Debezium requires a lot of engineering effort and technical know-how to be set up and used correctly.
  • There is no guarantee of zero data loss or transactional integrity if any of the components in the setup fails. Maintaining integrity is the sole responsibility of the development team here. 
  • Even though Debezium advertises many connectors, some of them have scalability issues. Even in the case of good old Postgres, it is not uncommon to run into out-of-memory exceptions while using plugins, like wal2json, to convert write-ahead log output to JSON. Another example is the lack of ability to snapshot tables while still being open for incoming events. This means for large tables, there could be long periods of time where the table is unavailable while snapshotting.
  • Debezium does not handle schema evolution in a graceful manner. Even though there is support for schema evolution in some of the source databases, the procedure to implement this is different for different databases. This means a lot of custom logic has to be implemented in Kafka processing to handle schema evolution.
  • Some of the connectors have limitations when it comes to supporting specific data types. For example, Debezium’s Oracle connector has a limitation in handling BLOB data types since they are still in an incubation state. It is not wise to use BLOB data types in Debezium-based production modules. 
  • Debezium Change Data Capture pipelines have to be designed and implemented optimally for them to scale as the data volume grows, if not pipelines can get overloaded rather quickly.
  • Debezium also comes with a lot of hidden costs. Even though it is free to use, a large amount of engineering effort and time is required to set up production CDC pipelines using Debezium. The availability of trained engineers to implement these pipelines is also another challenge.
  • In many cases, architects often design data pipelines that just solve today’s problem and do not consider the long-term implications or needs of the system. When the business grows, their data volume grows. This means that pipelines can become overloaded very quickly. That is why it is wise to explore solutions with long-term scalability and support in mind.

Interested in learning more about Debezium alternatives? Read our Debezium Alternatives: The Ultimate Guide. 


Striim is an in-memory platform for collecting, filtering, aggregating, and delivering data in real-time. The platform runs through an end-to-end network of built-in adapters that collect data from SQL and NoSQL databases, data warehouses, messaging systems, and more. Once the data is collected, Striim moves it to a supported target location.

Key Features

  • Data ingestion from enterprise sources
  • Smart data pipelines
  • Real-time analytics
  • SQL-based transformations


  • Striim features real-time data streaming and integration.
  • Striim supports streaming SQL operations such as transform, join and enrich.
  • Striim supports the evolution of schemas for data.
  • Striim includes a robust alert and monitoring service.


  • It's not an end-to-end multi-threaded architecture. The single-threaded components would cause delay and increased latency.
  • It requires significant engineering resources to be parallel and distributed.
  • Lack of log storage for CDC logs. It requires Kafka and custom code.
  • It doesn't have out-of-the-box schema evolution support (DDL), and unable to do SQL to NoSQL conversion automatically.
  • It is not a zero-code platform; developers have to write applications/jobs to migrate and replicate data 
  • Does not support CDC for a few enterprise databases like IBM DB2, Mainframes, Informix, ASE, Cassandra etc.
  • Striim was designed as a coding platform to do ETL. It can take up to a heavy lifting of custom code (e.g., thousand lines of code) to get a data pipe to work. 

Hevo Data

Hevo Data platform is a no-code data integration platform that provides end-to-end support for data pipelines that pull data across various sources to a data warehouse for transformations and other business intelligence analytics. Hevo Data is a fully managed cloud solution and users operate with the platform entirely through a simplified User Interface (UI).

Key Features

  • No-code platform
  • Fault tolerance architecture
  • Real-time data load
  • A wide array of data connectors (majority are SaaS connectors).


  • The main advantage of Hevo Data is that it is a no-code platform. What this means is that users do not need to write code or scripts to manage data pipelines but can interact with data sources through a point-and-click interface.
  • Hevo Data can connect to and read data from over 150 sources that mainly are SaaS applications and Business Intelligence (BI) platforms, etc.
  • Hevo Data supports the use of data models and workflows that can be used to prepare data for analytics.
  • The infrastructure behind Hevo data can scale with minimal latency without any input from the end user in terms of managing the underlying infrastructure.


  • Hevo Data only supports 8 databases and 2 data warehouses (Amazon Redshift & Google BigQuery). 
  • Hevo can't do log-based CDC on Oracle 19c and above and recommends using time-based CDC. This limitation can be major for enterprise Oracle users, especially in production environments that require high volume and high velocity.
  • Moreover, time-based CDC queries every table. It impacts the source databases, which can be challenging for large enterprises that have strict uptime and performance requirements.
  • Hevo's log-based CDC uses Logminer, which leads to running expensive LogMiner queries inside Oracle and could impact the performance of the Oracle server and consume a lot of PGA memory.
  • For SQL Server, Hevo uses Change Tracking (CT), which is different from log-based CDC. It writes to the production databases, increases compute cost and impacts production database performances. Also, it couldn't do it for real-time streaming. Normally, CT is not recommended for the production environment due to these reasons.
  • Change Data Capture is not supported universally across all data connectors but is limited to a few database sources like PostgreSQL, MyQSL, and Oracle.

Talend Data Integration

Talend is a data integration platform that enables one to extract, transform and load data across various sources and destinations. Talend provides solutions for both cloud and on-prem deployments. With its drag-and-drop interface, Talend boasts a claimed increase in productivity that’s 10 times faster than hand-coding.

Key Features

  • Data integration
  • Data integrity
  • Data quality
  • Data governance


  • Talend supports Change Data Capture with the most common relational databases like MySQL, Oracle, etc. 
  • Has great community support and a long history of supporting enterprise data pipelines. 
  • Talend Open Studio provides a user interface to configure the data source and destination. Most implementations can be done without writing code using this tool. 
  • The platform is available for on-premise and cloud-based deployments. 


  • Talend supports log-based CDC only for Oracle. For other databases, CDC is trigger-based and is complex to set up. This is because a backup is needed to take the load of the main source database.
  • The setup is not completely no-code based. Users will have to write queries and code in most cases.
  • The Talend Open Studio is free only for development. The server installation is licensed and expensive.
  • Talend pricing is not transparent and requires multiple conversations with the sales team


Shareplex is a data replication software for Oracle databases. One of its main advantages is that it is a low-cost replication solution for the Oracle environment when compared to the price of native tools. Shareplex supports high availability and disaster recovery. It also supports migrations, patches, and upgrades of databases.

Key Features

  • High availability
  • Centralized reporting
  • Migrations
  • Data Accuracy


  • Shareplex is a cheaper replication solution targeted for use with an Oracle database.
  • It supports data distribution and distributed processing.
  • Shareplex supports analytics of data.
  • The reporting feature is an integral part of Shareplex. It provides offload reporting, operational reporting, and consolidated reporting.
  • It allows for impact-free migration and upgrades of an Oracle database without downtime or loss of data.


  • It only supports an Oracle database as a source (recently it has announced a beta version for Postgres)
  • It is not easy to customize the installation and configuration of Shareplex to suit the various DevOps processes.
  • Shareplex requires some level of expertise to be implemented correctly and the documentation is not always clear.
  • The monitoring requirements on Shareplex may increase the load on functioning systems.

Informatica PowerCenter

Informatica PowerCenter is part of a suite of tools provided by Informatica for data replication, data masking, data visualization, and data management. It is a data integration tool that performs ETL operations across several sources and target database systems. The main workhorse of Informatica PowerCenter is its client tools, repository, and server which are used to connect and collect data from different heterogeneous data systems.

Key Features

  • Data Management
  • Data Replication
  • Data Quality
  • Data Processing


  • Informatica PowerCenter has features for running bulk data jobs and Change Data Capture.
  • It has a feature that can analyze the sources and structure of data.
  • Informatica PowerCenter provides a data validation option to ascertain that data is in the right format and range.
  • It also enables the creation of data tasks that can be executed and monitored.


  • The wide variety of product editions offered by Informatica PowerCenter can lead to decision paralysis for less experienced teams.
  • The workflow monitor does not include enough sorting options to organize data.


InfoSphere Information Server is a data integration platform by IBM that enables organizations to derive insights from complex data which may be spread across various data systems in an organization. It is made up of several additional offerings which are part of the suite of products, including InfoSphere DataStage. InfoSphere DataStage is an Extract, Transform, and Load tool that utilizes graphical notation to construct data ingestion strategies that run data jobs that transform data. InfoSphere can connect directly to enterprise databases, extract information from multiple sources, and optimize the delivery cycle for data projects.

Key Features

  • Graphical framework for data jobs
  • Data integration
  • ETL/ELT Operations


  • InfoSphere is capable of integrating data across multiple systems.
  • With InfoSphere a common business language can be developed for your data to ensure standardization across the organization.
  • InfoSphere allows for the analysis, assessment, and monitoring of data stored on heterogeneous systems to improve insights into data.
  • It offers access to a wide variety of additional data tools in the IBM ecosystem.


  • InfoSphere is primarily an IBM product and as such, it plays nicely with other IBM tools but could lead to vendor lock-in if the outcomes are not considered properly.
  • It requires knowledge of the IBM ecosystem for it to be used optimally.


In this comprehensive comparison, we discussed many different Oracle GoldenGate alternatives. We first walked through Oracle GoldenGate and shown the capabilities it offers for data integration and why you need alternatives. Next, we dived into ten alternative solutions, including their features, pros, and cons of each option. At this point, you should be able to settle on an option that best satisfies the data requirements of your project and matches your team's ability to implement it. 

However, some of the options presented may be technical, require a specialized skill set, or be less flexible than others. Arcion is one of the Oracle GoldenGate alternatives that meet the requirements for a flexible, intuitive, easy-to-manage data integration solution. It is available in both on-premises and cloud offerings and has connectors to the most popular database systems. Arcion is also incredibly quick to configure and delivers extremely performant data pipelines. To get started today, schedule a personalized demo today with our database expert.

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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