What Is Data Replication?

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With data modernization initiatives, a growing number of organizations are moving data from source databases and applications to the cloud. This is true even in the case of a distributed database. A distributed database means that files are located across multiple sites, in the same or different networks. Database replication supports a wide range of sources, goals and platforms. Data replication simplifies read and write operations. It supports all the processing power that network management needs.

Data replication ensures that the appropriate data is ready and available the moment it’s needed. To be data-driven, companies need access to real-time data. With data replication, IT teams and data users can always have access to data in real time. Data replication makes advanced analytics, machine learning (ML) and artificial intelligence (AI) possible.

Better data means better business decision-making. With data replication, dependable data synchronization and input are at your fingertips. Some business improvements include:

Data replication makes it possible to move and manage petabyte-scale data. This can be done with low latency from source to target. Petabytes of data can be transferred from one location to another with little or no delay. Real-time data is always available, so you can gain from reliable data entry and synchronization.

Technologies that support and enable data replication methods in big data include:

Change data capture (CDC) is a data integration pattern that allows users to detect and manage small changes at the data source. With CDC, users can apply data changes downstream. This change management can take place across the entire enterprise. CDC manages changes as they happen. The result? Fewer resources are needed for full data batching. Data consumers can take in changes in real time. There’s also less impact on the data source or the transit mechanism that links the data source and the data user. The data user only receives the updated data. This saves time, money and resources. CDC propagates these changes onto analytical platforms for real-time, actionable insights. There are several CDC methods with their own advantages and disadvantages, including Timestamp CDC, Triggers CDC and Log-based CDC.

Data engineers can extract data from any source with batch replication. With batch replication, only minimal configurations are needed to load data. Batch replication saves time during data preparation. Large amounts of data can be moved into the cloud. The data is analyzed quickly for business insights. But incremental changes to the source database or data warehouse are not captured. Batch replication is ideal for processing large volumes of data with minimal configurations.

Streaming data replication lets you continuously copy streaming data. It works with real-time sources, platforms and hubs including:

Full-table replication lets you work with all the rows in a table. Rows can include new, updated or existing ones. Rows are fully replicated. This happens during every job that is earmarked for replication. Full-table replication is a good fit when incremental replication is not possible, such as when records are deleted from the source. Limits of full-table replication include:

Snapshot replication copies data changes from one database to another. This happens at specific times and on demand. Snapshot replication is helpful when the database is less critical. It is also helpful when the database does not change often.

Asynchronous replication is a type of data storage backup. It is helpful when data is not backed up right after the main storage replication is completed. Instead, the data is backed up over time.

Across industries, all-sized organizations employ data replication and reap its rewards, which include:

Disaster recovery – Data replication supports disaster recovery. It constantly keeps a reliable backup of primary data on a non-production database. This makes data instantly available in cases of data recovery and failure. The cost and complexity of protecting critical workloads are reduced with data replication.

Data availability – Data replication delivers dynamic, near real-time transactional replication. This lets enterprises make accurate business decisions and respond to business events as they happen.

Speed of data access – Data replication makes data access faster, especially in organizations with multiple locations. Users in Asia or Europe may experience latency when reading data in North American data centers. Putting a replica of the data closer to the user can improve access times and balance the network load.

Real-time analytics – Data replication solutions with CDC capabilities can continuously replicate incremental changes. They do this by identifying and copying data updates as they take place in a database or data warehouse. They move the data into a message hub or events streaming platform. This enables the use of real-time data analytics.

Data warehouse modernization – Data replication feeds data from traditional on-premises data warehouses like Teradata, Oracle Exadata and SQL server. The data is fed into cloud data warehouses. These may include:

Next, the data is enriched, curated and cleansed. At this stage, cloud data integration solutions are used to ready the data for analytics and business intelligence use cases.

Cloud data lake ingestion – The cloud data lake has emerged as a critical platform for cost-effectively storing data. Cloud data lakes can process a wide variety of data types. These include both structured and unstructured data. Data replication is critical for ingesting data in real-time or in batch mode. The data is moved into a cloud data lake for driving modern analytics use cases such as:

IT costs – Data replication tools can reduce the IT labor involved in creating and managing data replication transactions across the enterprise. This saves time, money and resources.

Accelerate data integration – Companies are collecting more data than ever. They are struggling to bring together data from various siloed databases and data warehouses. They also struggle to deliver actionable analytics and AI. With data replication and ingestion solutions, organizations can efficiently ingest and replicate data for cleansing, parsing, filtering and transforming the data. This allows them to make their data available to data users for analytics and AI consumption.

Though data replication provides multiple benefits, organizations face many challenges in implementing data replication solutions. Below are some of the key challenges when performing different types of data replication:

Data replication use cases can be found across a variety of industries, including: