Data replication is the process of moving or copying data from one place to another or storing data simultaneously in more than one location. It lets you create one or more redundant copies of a database or other data store for the purpose of fault tolerance. It encompasses duplication of transactions on an ongoing basis, so that the replicate (often called a mirror) is in a consistently updated state and synchronized with the source.
Today's enterprises have enormous volumes of data and a wide variety of data types. How can a data-driven business with big data be sure that they have high quality data and availability? Companies use data replication to make accurate copies of their database or other data stores. With data replication, copies remain the same across data sources. It enhances fault tolerance and minimizes data loss. Data can be transferred into any database, cloud data lake or cloud data warehouse. This data can be on-site or in the cloud. Data replication also enables the ongoing duplication of data transactions and context. Data can be replicated or mirrored in an updated state and synchronized with any data source.
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:
- Resource efficiency
- Business agility
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
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
Streaming data replication lets you continuously copy streaming data. It works with real-time sources, platforms and hubs including:
- Internet of Things (IoT)
- Social media feeds
- Azure event message hub
- Google pub/sub message hub
- Message hubs like Kafka
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:
- Data latency
- Increased row consumption
- Unavailability of some integration patterns
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.
Data Replication Benefits
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:
- Fraud detection
- Real-time customer offers
- Social media monitoring
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:
- Cost – Keeping copies of the same data across multiple locations leads to higher storage and processing costs.
- Time consumption – The internal IT team requires more time to manually maintain multiple data replication solutions
- Network bandwidth – Replicating data across multiple copies requires deploying new processes and adding more traffic to the network.
- Data consistency – Managing multiple updates in a distributed environment may cause data to be out of sync on occasion. Database administrators need to ensure consistency in replication processes.
Data replication use cases can be found across a variety of industries, including:
- Financial services – In the financial services sector, data replication is used to prevent credit card fraud. It helps companies track customer transactions in real-time. Then it replicates the transactions in near real-time into a production database. This helps detect anomalies. From there, SMS alerts can be sent about fraudulent activities.
- Retail – In the retail arena, data duplication fosters increased sales. It combines a customer’s transaction records and spending patterns. This enables a company to generate real-time offer alerts benefitting the customer and boosting sales.
- Healthcare – For healthcare, data replication improves patient care. It collects and processes bedside monitor data. This empowers clinical researchers to understand and detect diseases.
- Manufacturing – Many manufacturers embed intelligent sensors in devices. They do this across their production lines and supply chains. Replicating the data from these sensors in real-time allows a manufacturer to spot problems. This allows them to correct issues before products leave the production line. This improves production and operations efficiency — saving time, resources and money.
Creating a Hurricane-proof Data Management Strategy
Moving 12+ Years of Data to the Cloud in One Weekend
KLA is a leading maker of process controls and yield management systems. Their customers are semiconductor manufacturers. To keep up with their expanding consumer base, they needed improved data analytics. They also needed to perform data replication using CDC methodology in Snowflake. They looked to Informatica and Snowflake to help them with their cloud-first data strategy. They looked to cloud mass ingestion to deliver continuous data replication. Change data was moved into their Snowflake cloud data lake on a continuous basis. Over the course of a single weekend, the company was able to move 1,000 Oracle database tables. This made 12 years of historical enterprise resource planning (ERP) data available for analysis. They also captured and integrated incremental Oracle data changes directly into Snowflake.
Combating Fraud with Real-time Streaming Analytics
A leading global financial company used data replication to better detect and prevent an increasing number of fraudulent transactions. They needed to be able to send real-time alerts to their customers about fraudulent transactions. Through data ingestion, they put a CDC sense-reason-act framework to work:
- They ingested transaction information from their database.
- A fraud detection ML model detected potentially fraudulent transactions.
- The company alerted customers.
Real-time streaming analytics data delivered out-of-the-box connectivity. Their dream of end-to-end data ingestion and streaming became a reality.
Companies with large volumes and varieties of data need high data availability. Data replication for big data enterprises works across data silos. This means that data copies remain the same as their sources. Copies can be transferred into any cloud data lake, data warehouse or database. It’s essential to find an automated and intelligent data replication solution to speed up and simplify the data replication process while keeping data safe.
Data Replication Resources
For today’s complex data centers, data replication is critical for data modernization. With Informatica’s end-to-end, AI-powered Intelligent Data Management Cloud (IDMC) including data replication and ingestion, you can quickly and safely replicate large volumes of data from a variety of sources. These sources include databases, applications, streaming and files onto cloud or on-premises repositories or onto messaging hubs for real-time analytics.