What is Data Replication?

Data replication is the process of moving data from one place to another, copying it, or storing data in more than one place at the same time. Allows you to create one or more copies of a database or data store in order to provide tolerance for error. It involves the continuous copying of processes. Thus, the copy (often called mirror) is constantly in the current state and synchronized with the source.

Today's businesses have huge volumes of data and a wide variety of data types. How can a data-driven business with big data be sure its data is high quality and available? Companies use data replication to obtain accurate copies of databases and other data stores. Thanks to data replication, copies remain the same across data sources. Increases fault tolerance and minimizes data loss. Data can be transferred to any database, cloud data lake or cloud data warehouse. This data can be located on-premises or in the cloud. Data replication also enables continuous replication of data processes and context. The data can be duplicated in an updated state and synchronously with any data source or created the same as a mirror.

Why is Data Replication Important?

With data modernization initiatives, an increasing number of organizations are moving data from source databases and applications to the cloud. This is true even in the case of distributed databases. Distributed database means that files are placed on multiple sites, on the same or different networks. Database replication supports a wide range of resources, goals, and platforms. Data replication simplifies read and write operations. Supports all the processing power needed by network management.

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

Better data means making better business decisions. Reliable data synchronization and input with data replication are at your fingertips. Some of the improvements it provides in terms of business are:

· Resource efficiency

· Cost reduction

· Business agility

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

How Data Replication Works

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 in the data source. With CDC, users can apply the changes downwards. This change management can take place across the entire enterprise. CDC manages changes as they occur. Conclusion? Fewer resources are needed for complete data blending. Data consumers can see changes in real time. The data user receives only updated data. This saves time, cost and resources. CDC disseminates these changes to analytics platforms for real-time, actionable insights. There are several CDC methods with their advantages and disadvantages, for example: Timestamp CDC, Trigger CDC, and Diary-based CDC.

Batch Replication

Data engineers can extract data from any source by batch replication. Only minimal configurations are required to load data with batch replication. Batch replication saves time during data preparation. Large amounts of data can be moved to the cloud. Data is analyzed quickly in terms of business insights. However, incremental values in the source database or data warehouse are not captured. Batch replication is ideal for processing large volumes of data with minimal configurations.

Static Data Replication

Static data replication allows you to continuously copy flowing data. Works with real-time resources, platforms and distributors. For example:

· Internet of Things (IoT)

· Sensors

· Social media feeds

· Azure event message dispatcher

· Google publish/subscribe message dispenser

· Message distributors such as Kafka

Full Table Replication

Full table replication allows you to work with all rows in a table. Rows can contain new, current or existing ones. The rows are completely copied. This happens during each job that is set aside for replication. Full table replication is a good choice when incremental replication is not possible, for example when records are deleted from the source. The limits of full table replication are:

· Data delay

· Increased line consumption

· Unable to use some integration patterns

Snapshot Replication

Snapshot replication copies data changes from one database to another. It takes place at certain times and on demand. Snapshot replication is useful when the database is less important. It is also useful when the database does not change too often.

Asynchronous Replication

Asynchronous replication is a type of data storage backup. It is useful when the data is not backed up immediately after the main storage replication is complete. Instead, the data is backed up over time.

What Are the Benefits of Data Replication?

In industries, companies of all scales use data replication and see the benefits of it. These benefits;

Emergency rescue — Data replication supports emergency recovery. Maintains a reliable backup of primary data continuously in a non-production database. It makes data instantly available in case of data recovery and failure. Data replication reduces the cost and complexity of maintaining significant workloads.

Data availability — Data replication offers dynamic, near-real-time operational replication. This allows businesses to make the right business decisions and respond to them when business events occur.

Data access speed — Data replication accelerates data access, especially in organizations with multiple locations. Users in Asia or Europe may experience delays reading data from North American data centers. Placing a copy of the data near 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. It does this by identifying and copying those updates as data updates occur in a database or data warehouse. They move the data to a message dispatcher or event streaming platform. This allows the use of real-time data analysis.

Data warehouse modernization — Data replication feeds data from traditional on-site data warehouses such as Teradata, Oracle Exadata, and SQL server. The data is fed into cloud data warehouses. These are:

· Snowflake Data Cloud

· Amazon Web Services (AWS) Redshift

· Microsoft Azure Synapse

· Google BigQuery

Then the data is enriched, edited and cleaned. In this phase, cloud data integration solutions are used to analyze data and prepare them for business intelligence use cases.

Cloud data lake retrieval Cloud data lake has emerged as a critical platform for storing data in a cost-effective way. Cloud data lakes can handle a wide variety of data types. These cover both structured and unstructured data. Data replication is important for retrieval of data in real-time or batch mode. The data is moved to a cloud data lake to activate modern analytics use cases such as:

· Fraud detection

· Real-time customer offers

· Social media monitoring

IT costs — Data replication can reduce IT jobs related to building and managing data replication processes across the enterprise. This saves time, cost and resources.

Accelerate data integration Companies are collecting more data than ever before. They try to bring together data from various silo databases and data warehouses. They are also working to provide actionable analytics and AI. With data replication and retrieval solutions, businesses can efficiently import and copy data to clean, parse, filter and transform data. This allows them to make their data available to data users for analysis and AI consumption.

What Are the Challenges of Data Replication?

Although there are numerous benefits of data replication, enterprises may face some challenges in implementing data replication solutions. Below are some of the key challenges that can be encountered when performing different types of data replication:

· Cost Keeping copies of the same data in multiple locations leads to higher storage space and processing costs.

· Time consumption — It takes more time for the on-premises IT team to manually maintain a large number of data replication solutions.

· Network bandwidth Copying data in multiple copies requires the use of new processes and adding more traffic to the network.

· Data consistency Managing multiple updates in a distributed environment can cause data to go out of sync at times. Database administrators need to ensure consistency in replication processes.

What Are Data Replication Uses?

Data replication use cases can be seen in a variety of industries. For example:

· Financial services — In the financial services industry, data replication is used to prevent credit card fraud. It helps companies monitor customer transactions in real time. It then copies almost real-time processes to a production database. This helps to detect abnormalities. Thanks to this, SMS alerts about fraudulent activities can be sent.

· Retail — Copying data in the retail sector helps increase sales. Combines customer transaction records and spending patterns. This allows a company to make real-time bid alerts in the customer's interest and increase sales.

· Health services — Data replication for healthcare improves patient care. The bed collects and processes monitor data. This helps clinical researchers understand and detect diseases.

· Production Many manufacturers install smart sensors inside devices. They do this in their production lines and supply chains. Copying real-time data from these sensors allows the manufacturer to detect problems. In this way, they can fix problems before the products leave the production line. This saves time, resource and cost, improving production and operating efficiency.