How swiftly does your company leverage data to improve business processes? Minutes, hours, days or months after the data originated? In our increasingly fast-paced world, the rate at which consumers perform transactions, subscribe to services and experience content is only increasing. Therefore, companies are seeking more rapid ways to utilize data to make faster business decisions and gain a competitive advantage.
To effectively utilize data, companies need to move data from multiple isolated systems to large data stores for consolidated reporting and analytics purposes, and this is where data replication helps. Data replication techniques ensure fast, reliable data access to the users who rely on it to make decisions and the customers who need it to perform transactions.
This post will focus on logical database data replication, in which database operations from the source are replayed on the target. There are many other types of data replication such as physical database replication, file replication and data replication from SaaS applications.
What is data replication?
Data replication is the process of copying or updating data from one location to another, often in real-time or near real-time. Data replication can be homogeneous, between identical technologies, or heterogeneous, between different technologies. The goal of data replication is business continuity – to ensure that data is readily available for the multiple users (and use cases) who require it.
For example, data can be copied from on-premises systems to cloud-based environments to support near real-time analytics. Data can also be copied between operational systems to support uninterrupted operation and recovery of mission-critical and customer-facing applications and data in the event of a data breach or system outage.
Benefits of data replication
Data replication lets you increase the availability and durability of your data by copying it across multiple locations. This produces several benefits, which include:
Support for real-time analytics
Data replication supports advanced analytics by synchronizing cloud-based reporting and enabling data movement from numerous sources into data stores, such as data warehouses or data lakes, to fuel business intelligence and machine learning. In addition, because data replication is a continuous real-time process, it allows businesses to get immediate insights from their data. A simple example is populating a dashboard. A more advanced example is using data replication to pull user behavioral data from various data sources to analytical data stores, then running a predictive model to provide real-time personalized recommendations to improve the customer experience.
Faster data access
As data is stored in multiple locations, users can retrieve data from the servers closest to them and benefit from reduced latency. For instance, users in Africa may experience a delay when accessing data stored in North America-based servers. But the latency will decrease if a replica of this data is kept closer to their location.
Optimized server performance
Data replication allows companies to share traffic across several servers, leading to better-optimized server performance and less stress on individual servers. For instance, companies can move complex analytical queries to data warehouses and data lakes, thereby reducing the burden on operational databases and improving overall system performance.
Data replication enables efficient data protection and recovery in the case of a disaster. Critical data source stoppages can run into the millions of dollars per hour of data unavailability. The disruption to the business can be even worse if data is permanently lost due to system and process failures. Data replication is a proven approach to mitigating unexpected data losses. For example, in the case of network disruptions to one cloud region, the company can quickly switch to a different region and maintain normal business operations.
How to implement data replication
Companies can adopt several data replication techniques, depending on use case and existing data infrastructure. Some common data replication strategies include:
Log-based change data capture
Change data capture (CDC) is a data replication technique that identifies changes made to data in a database and then delivers those changes in real time to target systems. Log-based CDC, in which changes are captured asynchronously from a database's transaction log, is widely considered to be the preferred method. In a log-based CDC approach, the data replication technology identifies record modifications, such as inserts, updates and deletes, from the database transaction log. It then propagates those changes in real time to the data destination.
Log-based CDC can be a good fit if:
- The source database processes high volumes of changes frequently and you want to keep processing overhead on the source database to a minimum.
- Transactional data is required for streaming and real-time analytics use cases. Log-based CDC is well suited to moving data to stream-processing solutions like Apache Kafka, Amazon Kinesis, Azure Event Hubs or Google PubSub.
- There is a need for zero-downtime database migrations with minimal latency, especially across geographically separated systems.
Trigger-based change data capture
This data replication technique defines trigger functions using SQL syntax such as "BEFORE UPDATE" or "AFTER INSERT" to capture changes based on events. Triggers fire for all INSERT, UPDATE or DELETE commands (that indicate a change) and are part of the transaction, used to create a changelog stored in a shadow table. The shadow tables may hold records of every column change or only the primary key and its operation type (insert, update or delete). Certain users prefer this approach because it operates at the SQL level and some databases support trigger-based functions natively.
However, because the triggers fire as part of the transaction and perform extra work, trigger-based capture always slows down transactions. Also, when pulling changes recorded by trigger-based capture, separate queries hit the database causing extra load. These queries may require table joins and do not follow the consistency order the originating transactions followed.
Trigger-based CDC can be a good fit if:
- The source database supports trigger functions natively, such as Oracle, PostgreSQL, SQL Server and MySQL.
- You require support for tracking custom database operations and information.
- Performance of individual transactions is not critical and the system has adequate resources to absorb the extra load.
Snapshot replication is the process of taking a snapshot of the source database at static points in time and replicating the data in the target systems. Snapshot replication may require high processing power if the source has a very large dataset.
Snapshot data replication can be a good fit if:
- The volume of data is limited.
- There is no requirement to replicate incremental changes close to real-time.
- There is a convenient time window for the snapshot refresh; it is appropriate for overnight data backups, batch reporting and analytical use cases that run when the load on systems is low.
Guarantee data availability and reliability with data replication
Investing in a data replication strategy can be costly and time-consuming. Still, it is vital for companies that want to utilize data for various analytical and business use cases to gain a competitive advantage and safeguard their data against data loss and downtime.
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