Article

What is Data Replication: Examples, Techniques & Challenges

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February 23, 2022

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20 min read

Today, the average technology company generates data from dozens of sources, which needs to be consumed by different teams and applications. Each team within an organization has unique requirements, and data engineers need to find solutions to support all use cases.

Backend engineers are used to replicating databases to support high availability, backup, and disaster recovery. For data engineers, things are more complex. Data needs to be searched, analyzed, queried in real-time, merged with different sources, and even returned to the source after being enriched. Supporting such diverse operations increases the need for teams to replicate data between applications, databases, data warehouses, and data lakes.

This article will teach you all you need to know about data replication, including its use cases, most prevalent techniques, and everyday challenges.

What is Data Replication?

Data replication is the process of copying data from a data storage (source) to another data storage (destination) to serve operations, analytics, or data science purposes. Related processes to data replication include data synchronization (continuous harmonization between the source and destination), data ingestion (collecting data from the source), and data integration (bringing data from disparate sources together in a unified view).

Data replication should not be confused with data migration, which implies that you decommission data from the source after copying. In this case, replication can be used as an intermediate step to support the migration since it keeps the source and destination in sync until the migration is complete. At that point, you may reliably transition to the new data storage.

Data replication can be done in batches or in real-time, in which case the source is often referred to as the publisher and the destination as a subscriber.

How Does Data Replication Work?

Data replication is the process of copying and distributing data from one database or storage system to another. It ensures that the same data is available across multiple locations, enhancing data availability, reliability, and accessibility. The process typically involves three main steps:

Capturing Changes: Data replication systems monitor the source database for any changes, such as inserts, updates, or deletes.
Transmitting Data: Once changes are detected, the replication system transmits the data to one or more target databases or storage systems.
Applying Changes: The target databases receive the replicated data and apply the changes, ensuring that they mirror the source database.

Data replication can occur synchronously or asynchronously, depending on the requirements and constraints of the system. Synchronous replication ensures that changes are applied to the target databases immediately, providing real-time data consistency. On the other hand, asynchronous replication introduces a slight delay between the source and target databases, allowing for greater flexibility and scalability.

Benefits of Data Replication

Enhanced Data Availability: By replicating data across multiple locations, data replication ensures high availability and accessibility, reducing the risk of data loss due to hardware failures or disasters.
Improved Performance: Replicating data to multiple locations allows for localized access, reducing latency and improving application performance, particularly in distributed environments.
Disaster Recovery: Data replication serves as a critical component of disaster recovery strategies by providing redundant copies of data that can be quickly restored in the event of a system failure or disaster.
Business Continuity: With replicated data readily available, organizations can maintain business operations even in the face of disruptions, minimizing downtime and ensuring continuity.
Scalability: Data replication supports scalability by distributing data across multiple servers or storage systems, enabling systems to handle increased loads and accommodate growing data volumes.
Geographic Redundancy: Replicating data to geographically dispersed locations ensures geographic redundancy, protecting against regional outages or disasters and improving data resilience.
Data Localization: Data replication allows organizations to comply with data localization requirements by replicating data to servers located in specific regions or countries, ensuring compliance with local regulations.
Load Balancing: By distributing data copies across multiple servers, data replication facilitates load balancing, ensuring optimal resource utilization and performance across the infrastructure.
Data Analysis: Replicated data can be used for data analysis, reporting, and business intelligence purposes, enabling organizations to derive insights and make informed decisions based on up-to-date information.

In summary, data replication plays a crucial role in ensuring data availability, reliability, and resilience, providing organizations with the flexibility and agility to meet evolving business requirements and challenges.

Data Replication examples

Data can be replicated from one form of storage to another, and data engineers are frequently confronted with many combinations. This section discusses data replication examples between different types of storage like applications, databases, data warehouses, and data lakes.

Replication Example	Description
Database to Database	Homogenous: Replicating data between databases of the same type for business continuity or performance optimization. Heterogenous: Replicating data between databases of different types to leverage specific functionalities.
Database to Data Warehouse	Replicating data from databases to data warehouses for analytics, business intelligence, and data transformations.
Database to Data Lake	Replicating data from databases to data lakes for storing raw and unstructured data before transforming it for analytics and modeling.
Database to Search Engine	Replicating data from databases to search engines for enhanced text search capabilities, scalability, and efficiency.
Application to Data Warehouse	Integrating data from external applications exposed via APIs into data warehouses for analytics, reporting, and insights generation.
Data Warehouse to Application	Analyzing and enriching data in data warehouses before replicating it back to applications for improved decision-making and operational processes.

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Data replication techniques

Data can be replicated on demand, in batches on a schedule, or in real-time as written, updated, or deleted in the source. Typical data replication patterns used by data engineers are ETL (extract, load, transform), and EL(T) pipelines.

The most popular data replication techniques are full replication, incremental replication, and log-based incremental replication. Full replication and incremental replication allow for batch processing. Meanwhile, log-based incremental replication can be a technique for near real-time replication.

Full Replication: This technique ensures complete data consistency between the source and destination databases by copying all records. It's suitable for smaller datasets or initial replication tasks but may become resource-intensive for larger datasets.
Incremental Replication: Incremental replication transfers only the changed data since the last update, reducing network bandwidth usage and replication times. It's efficient for large datasets but requires a more complex setup and may lead to data inconsistencies if not implemented properly.
Log-based Replication: Log-based replication utilizes database transaction logs to identify changes and replicate them in near real-time. It efficiently captures and replicates changes, minimizing data latency. However, it requires support from the source database and is limited to specific event types.
Snapshot Replication: Snapshot replication captures the state of data at a specific time and replicates it to the destination. It facilitates point-in-time recovery but increases storage requirements and replication times for large datasets.
Key-based Replication: This technique focuses on replicating only data that meets specific criteria or conditions, such as changes to specific tables or rows identified by unique keys. It's useful for selective replication but may require more complex configuration.
Transactional Replication: Transactional replication replicates individual transactions from the source database to the target database, ensuring data consistency across both systems. It's commonly used in scenarios where data integrity is critical, such as financial transactions or order processing.
Merge Replication: Merge replication allows changes to be made independently at both the source and destination databases, with the ability to reconcile differences and merge conflicting modifications. It's suitable for distributed environments where data is updated at multiple locations but requires careful conflict resolution mechanisms.
Change Data Capture (CDC): CDC involves capturing and tracking changes made to the source data, including inserts, updates, and deletes, and replicating these changes to the destination in near real-time. It provides granular visibility into data modifications and is commonly used for real-time analytics, data warehousing, and maintaining data synchronization between operational systems.

Bi-directional Replication: Bi-directional replication ensures data consistency between multiple databases or systems by replicating changes bidirectionally. It facilitates decentralized data synchronization and fault tolerance. However, it requires careful conflict resolution mechanisms to handle data conflicts and increases complexity and management overhead compared to uni-directional replication.
Near Real-time Replication: This method replicates changes almost instantly, providing up-to-date data for real-time analytics and decision-making. It minimizes data latency but requires high network bandwidth and low latency connections for efficient replication, potentially increasing system overhead and resource utilization.
Peer-to-peer Replication: Peer-to-peer replication enables decentralized data synchronization and fault tolerance by distributing data processing and reducing single points of failure. However, it requires complex conflict resolution mechanisms to handle data conflicts and increases management overhead.

Data Replication Techniques

Technique	Usability	Pros	Cons
Full Replication	Suitable for smaller datasets or initial tasks	Ensures complete data consistency between source and destination databases Simple implementation for small datasets	Resource-intensive for larger datasets May cause network congestion and longer replication times
Incremental Replication	Efficient for large datasets	Reduces network bandwidth usage and replication times Ideal for continuous replication and frequent updates	Requires a more complex setup to track and manage incremental changes May lead to data inconsistencies if not implemented properly
Log-based Replication	Provides near real-time replication	Offers efficient and low-latency replication Minimizes data latency and ensures timely updates	Requires support from the source database for log-based change data capture Limited to specific database event types
Snapshot Replication	Facilitates point-in-time recovery	Simplifies incremental updates by capturing changes since the last snapshot Provides a consistent view of data at a specific moment in time	Increases storage requirements for maintaining snapshots of data May lead to longer replication times for large datasets
Bi-directional Replication	Ensures data consistency between multiple databases or systems	Allows for independent changes at both source and destination databases Facilitates decentralized data synchronization and fault tolerance	Requires careful conflict resolution mechanisms to handle data conflicts Increases complexity and management overhead compared to uni-directional replication
Near Real-time Replication	Provides up-to-date data for real-time analytics and decision-making	Minimizes data latency by replicating changes almost instantly Supports efficient and low-latency replication	Requires high network bandwidth and low latency connections for efficient replication May increase system overhead and resource utilization
Peer-to-peer Replication	Enables decentralized data synchronization and fault tolerance	Distributes data processing and reduces single points of failure Facilitates decentralized data synchronization and fault tolerance	Requires complex conflict resolution mechanisms to handle data conflicts Increases management overhead and complexity

Using a data replication tool to solve challenges

Implementing a data integration solution doesn’t come without challenges. At Airbyte, we have interviewed hundreds of data teams and discovered that most of the issues they confront are universal. To simplify the work of data engineers, we have created an open-source data replication framework.

This section covers common challenges and how a data replication tools like Airbyte can help data engineers overcome them.

Airbyte Connection UI to replicate data from Salesforce to BigQuery

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Using, maintaining, and creating new data sources

If your goal is to achieve seamless data integration from several sources, the solution is to use a tool with several connectors. Having ready-to-use connectors is essential because you don’t want to create and maintain several custom ETL scripts for extracting and loading your data.

The more connectors a solution provides, the better, as you may be adding sources and destinations over time. Apart from providing a vast amount of ready-to-use connectors, Airbyte is an open-source data replication tool. Using an open-source tool is essential because you have access to the code, so if a connector’s script breaks, you can fix it yourself.

If you need special connectors that don’t currently exist on Airbyte, you can create them using Airbyte’s CDK, making it easy to address the long tail of integrations.

Reducing data latency

Depending on how up-to-date you need the data, the extraction procedure can be conducted at a lower or higher frequency. At higher frequencies, the higher processing resources and more optimized scripts you require to reduce the data replication latency.

Airbyte allows you to replicate data in batches on a schedule, with a frequency as low as 5 minutes, and it also supports log-based CDC for several sources like Postgres, MySQL, and MSSQL. As we have seen before, log-based CDC is a method to achieve near real-time data replication.

Increasing data volume

The amount of data extracted has an impact on system design. As the amount of data rises, the solutions for low-volume data do not scale effectively. With vast volumes of data, you may require parallel extraction techniques, which are sophisticated and challenging to maintain from an engineering standpoint.

Airbyte is fully scalable. You can run it locally using Docker or deploy it to any cloud provider if you need to scale it vertically. It can also be scaled horizontally by using Kubernetes.

Handling schema changes

When the data schema changes in the source, the data replication may fail as you haven't updated the schema on the destination. As your company grows, the schema is frequently modified to reflect changes in business processes. The necessity for schema revisions can result in a waste of engineering hours.

Dealing with schema changes coming from internal databases and external APIs is one of the most difficult challenges to overcome. Data engineers commonly have to drop the data in the destination to do a full data replication after updating the schema.

The most effective way to address this problem is to sync with the source data producers and advise them not to make unnecessary structural updates. Still, this is not always possible, and changes are unavoidable.

Normalizing and transforming data

A fundamental principle of ELT philosophy is that raw data should always be available. If the destination has an unmodified version of the data, it can be normalized and transformed without syncing data again. But this implies a transformation step needs to be done to have the data in your desired format.

Airbyte integrates with dbt to perform transformation steps using SQL. Airbyte also automatically normalizes your data by creating a schema and tables and converting it to the format of your destination using dbt.

Monitoring and observability

Working with several data sources produces overhead and administrative issues. As your number of data sources increases, the data management needs also expand, increasing the demand for monitoring, orchestration, and error handling.

You must monitor your extraction system on multiple levels, including resources consumption, errors, and reliability (has your script run?).

Airbyte’s monitoring provides detailed logs of any errors during the data replications so that you can easily debug or report an issue to the community, so other contributors help you solve it. If you need more advanced orchestration you can integrate Airbyte with open-source orchestration tools like Airflow, Prefect, and Dagster.

Depending on data engineers

Building custom data replication strategies requires experts. But being a bottleneck for stakeholders is one of the most unpleasant situations a data engineer can experience. On the other hand, stakeholders may feel frustrated by relying on data engineers to set up a data replication infrastructure.

Using a data replication tool can solve most of the challenges described above. A key benefit of employing such tools is that data analysts and BI engineers can become more independent and begin working with data seamlessly and as soon as possible, in many cases, without depending on data engineers.

Airbyte is trying to democratize data replication and make it accessible for data professionals of all technical levels. When working with Airbyte, you can use the user-friendly data replication UI.

Conclusion

In this article, we learned what data replication is, some common examples of replication, and the most popular data replication techniques. We also reviewed the challenges that many data engineers face and, most importantly, how a data replication tool can help data teams better leverage their time and resources.

The benefits of data replication are clear. But the number of sources and destinations continues to grow, companies need to be prepared for the challenges associated with it. That’s why it’s essential to have a reliable and scalable data replication strategy in place.

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About the Author

Thalia Barrera is a data engineer and technical writer at Airbyte. She has over a decade of experience as an engineer in the IT industry. She enjoys crafting technical and training materials for fellow engineers. Drawing on her computer science expertise and client-oriented nature, she loves turning complex topics into easy-to-understand content.