Data Integration

The Complete Guide to Real-Time Data Replication

What is Real-Time Data Replication? In this blog we discuss real-time data, real-time data replication and the benefits it confers. We also look at real-time data use cases by different industries, and various real-time replication methods. Finally, we look at what qualities a high-performance data replication tool should possess, and how BryteFlow checks off every box in the checklist.

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What is Real-time Data?

Real-time data refers to information that is ready for use whenever it is generated. The data is transmitted immediately from source to target for consumption, and this is invaluable for deriving real-time business insights, recording financial transactions, stock market trading, fueling predictive analytics for equipment maintenance, or even monitoring health with medical devices. Basically, real-time data is the foundation on which real-time insights are built and helps you to react and adapt to changing situations fast. However real-time data may face a delivery lag sometimes, mostly due to bottlenecks in network bandwidth or inadequate infrastructure. BryteFlow for Real-Time Data Replication

What is Real-time Data Replication?

Now that you know what real-time data is, it’s time to find out about real-time replication, which helps you to access the data almost as soon as it is created. Real-time data replication is the process by which data is duplicated and synchronized over multiple systems to enable data consistency across locations, provide high availability, or as a backup replica for disaster recovery. Real-time data replication involves constant monitoring of source systems for changes in data, and then copying the changes or deltas to the target systems to sync data with source, and keep it updated. Real-time replication is typically enabled by Change Data Capture or CDC, a set of processes that detect and transmit changes at source in real-time but have negligible impact on system performance. Oracle CDC (Change Data Capture): 13 Things to Know

Change Data Capture to drive Real-Time Data Replication

Change Data or Capture or CDC as it is popularly known, is the underlying mechanism that drives real-time replication. It identifies changes at the source system including inserts, updates and deletes with a variety of CDC patterns which could include log-based CDC, trigger-based CDC, timestamp-based CDC or table differencing. Overall CDC processes ensure data integrity and consistency across data warehouses like AWS Redshift, Snowflake, Azure Synapse, and Databricks, and in transactional systems and applications. Change Tracking is a subset of CDC. Change Tracking informs about changes in a specific row since the last query but does not inform about the values that have changed, or the number of times it has changed. SQL Server CDC for Real-Time Data Replication

CDC is low impact because it does not need to query the database directly and delivers only the deltas incrementally to the target. It uses a publisher-subscriber model and caters to a variety of data management use cases. For e.g., getting updated customer information into marketing databases or keeping inventory databases up to date. CDC can be of 2 types- Push or Pull. In the ‘Push’ CDC the source database pushes updates to downstream services and applications. With ‘Pull” CDC, the downstream services and applications query the source system at specified intervals to pull the updates. The Easy Way to CDC from Multi-Tenant Databases

Real-Time Data Replication Stages

The process of real-time data replication includes 3 stages:

Real-Time Data Replication Stage 1: Data Ingestion
This is the first step is data ingestion that involves collecting and transferring data from the source system to the target. It ensures that data has been properly aggregated for duplication. Successful Data Ingestion (What You Need to Know)

Real-Time Data Replication Stage 2: Data Integration
The second step is data integration when you consolidate data from multiple source systems into a single, unified view. This ensures there are no inconsistencies in data and that data is uniform across platforms (vital when you have multi-source data). Postgres CDC and 6 Easy Methods to Capture Data Changes

Real-Time Data Replication Stage 3: Data Synchronization
This is the big focus of real-time data replication. This step is an ongoing process that keeps data constantly synced between source and target. It ensures that changes at source are immediately delivered and reflected on the target, to keep data continuously and accurately updated, using either some form of Change Data Capture or even batch or real-time synchronization. How to Make CDC Automation Easy

Types of Database Replication Tools

Real-time database replication can be achieved using various technologies like Connectors, APIs, and databases that have built-in support for CDC. The real-time replication process is enabled by various tools with varying capabilities that can help to simplify and automate the process. They fall under different categories:

Native Database Replication Tools
Many modern RDBMS like MySQL, PostgreSQL, and SQL Server, provide master-slave replication that is built-in for creating and managing database replicas.‍ These databases have native tools to help in replication. For e.g. SAP with the SAP SLT replication tool, Oracle with Oracle GoldenGate, and SQL Server with SSIS. SQL Server CDC for Real-Time Data Replication

Database Replication with ETL Tools
Third-party tools, both open-source and commercial, are available for extraction, transformation and loading and they perform real-time database replication as well. Our very own BryteFlow is one of them, besides others like Fivetran and Qlik Replicate. Oracle CDC (Change Data Capture): 13 Things to Know

Database Replication with Dedicated Replication Tools
Besides ETL tools you can use specialized CDC replication tools to perform the replication. These tools offer essential replication features like compression of data, real-time replication and automated failover.

Database Replication with Cloud Services
Cloud vendors such as AWS with its AWS DMS (AWS Data Migration Service), Azure with SQL Server Management Studio (SSMS), and Snowflake with Snowflake Streams are some Cloud platforms that provide managed replication services for smooth data replication between on-premise and cloud environments. These services are available to you as subscribers to the platform. AWS DMS CDC and Limitations for Oracle Sources

CDC Replication with Message Brokers
Message brokers like Apache Kafka, RabbitMQ, Redis, Amazon SQS, Active MQ, and others perform real-time streaming replication which also serves as a CDC mechanism. Apache Kafka is a distributed messaging software, and as with other message queues, Kafka reads and writes messages asynchronously. Kafka CDC transforms databases into data streaming sources. Debezium connectors get data from external sources to Kafka in real-time while Kafka connectors stream the data from Kafka to various targets – again in real-time, with Kafka acting as a message broker. Learn about Oracle CDC to Kafka

Real-Time Data Replication Benefits

Generally, data replication, by creating backup replicas in different locations is thought to be part of a disaster recovery strategy. However real-time data replication has many other use cases. It enables continuous data synchronization across systems, thus ensuring consistency for operational data. This results in enhancement of data quality and reduces latency. Your data on-premise and on Cloud platforms remains updated and consistent, so users can access trustworthy data, and depend on it to generate accurate insights. Successful Data Ingestion (What You Need to Know)

Real-time data replication ensures immediate data consistency and uniformity
Real-time replication ensures the data is uniform across distributed systems and platforms. This is important for applications that need instant accuracy like transactional payment systems, stock market trading, CRMs etc. Users who wish to analyze data are also assured of trustworthy data that is the same for all users. Oracle Replication in Real-time, Step by Step

Real-time data replication is needed for Real-Time Online Analytical Processing (OLAP)
Organizations get data in real-time from a variety of sources and replicate it across multiple databases and data warehouses for analytical processing. Organizations often replicate data to Snowflake, Redshift, Databricks, PostgreSQL and Azure Synapse etc. for Analytics or ML purposes. These robust platforms are known for their strong analytical capabilities and faster read performance. It also ensures that analytics workloads do not impact the primary database adversely. Postgres CDC and 6 Easy Methods to Capture Data Changes

Real-time data replication optimizes server performance
Queries on a single data source will severely burden the infrastructure, leading to slowing down of queries and impacting operations of source systems. Data replication distributes the database across different sites in the distributed system, reducing the load on the primary server. Thus, replication optimizes the network load by balancing it across the data infrastructure, effectively distributing traffic to replicas on other servers, improving overall server performance, and meeting real-time data requirements of users. It also promotes concurrency, since multiple users can access and query the same data at the same time. Oracle Replication in Real-time, Step by Step

Real-time data replication ensures data is easily available
If you have data replicated at multiple locations on the network, it can minimize downtime and avert data loss when there are network disruptions, software errors, hardware failures, and system outages. Even if one site is down, users can still access data from another site. Data is always accessible 24×7 from all geographies and is highly resilient. Snowflake CDC With Streams and a Better CDC Method

Real-time data replication is needed for resilient disaster recovery
Should there be a natural calamity or a data breach, real-time replication protects data with immediate failover, helping organizations bounce back from system failures. Data replication stores backups of primary data on a secondary device, which can be immediately accessed for recovery and failover.
Real-time data replication ensures faster and global access to data
Real-time data replication distributes data over different geographies. It may happen that users may experience latency while accessing data stored in another country. Storing replicas of the data on local servers helps users to access and query the data faster, especially useful in the case of multinational or multi-branch organizations.

Data Replication Best Practices

Have you defined your objectives for data replication?
Before starting real-time data replication, it’s crucial to define your business objectives. Any replication strategy must align with your business needs to be effective. You must question why you need real-time data replication, identify the data to be replicated, regulatory compliances, what kind of applications will access the data, and what the user profiles look like. Also examine your infrastructure, define high priority data sources, target data warehouses, and the required data flows.

Timeliness requirements of the data will define your replication mode
The timeliness of the data must link to your business objectives. Do you need sub-second latency? This may be true for a credit card transaction or an online subscription system, where you need data to reflect in real-time, and you will need real-time data replication for this. On the flip side, if your business needs once a day reporting like an inventory system, or an attendance record that needs to be updated at the end of day, you can do snapshot replication rather than replicate individual transactions.

Choosing the right replication method
There are various methods available, such as snapshot replication, transactional replication, and log-based replication. Factors to consider include data volume, network bandwidth, transformation requirements, latency requirements, the complexity of data changes, and the level of data consistency needed.

For transactional replication, use log-based data replication
Log-based Change Data Capture ought to be part of your real-time data replication arsenal. Log based CDC monitors database logs that are stored by modern databases external to production systems. As such they have minimal impact on source systems and provide an efficient, accurate way to replicate data.

Proper data validation and cleansing is needed for data consistency
At the configuration and implementation stage before replicating data, implement proper data validation, error management, data cleansing methods and filtering logic to ensure that the replicated data is complete, accurate, and uniform across all target databases or systems -this helps to ensure high quality data.

Attention to implementation and configuration of the replication pipeline
After defining sources and targets, you must examine how you can put together robust data pipelines. You could try and minimize complexity of transformations, aggregate common data flows, enable caching and compression of data, and select the right replication tools to promote efficiency.

Monitoring and testing the data replication
Once the replication is underway, you need to monitor the replication, its latency and consistency of data. You could also perform regular testing to test the accuracy and completeness of data. Doing this helps to fix glitches or errors in the replication early on, leading to more reliable outcomes.

Taking regular backups of replicated data
Besides the process of data replication, you must also have regular data backups as part of a backup strategy. This will provide data protection and recovery of data in case of any adverse circumstances like network failures or data loss due to incidents of data terrorism.

Real-Time Data Use Cases

Here are some popular use cases for real-time data in various industries.

Real-Time Data Use Cases in Manufacturing
Manufacturers use intelligent sensors across production lines and supply chains. Replicating this sensor data in real-time helps identify and correct issues before products leave the production line, improving quality, efficiency, and saving time and money.

Real-Time Data Use Cases in the Retail Industry
Customer data from different sources including social media, ecommerce channels, locations, demographics, transaction history, loyalty programs etc. can be replicated and aggregated to understand spending patterns and how customers behave and buy. This helps to provide a unified view of the customer, enabling companies to provide real-time personalized offers and discounts, benefiting customers and increasing sales. Retail companies also use real-time data to predict inventory. Forecasting and logistics models can be created, using the data to predict demand and optimize the supply chain for better stock management, efficient distribution and to maximize returns. Real-Time Data in the Retail Industry

Real-time Data Use Cases for the Oil and Gas Industry

In industries like Oil and Gas, companies access and use real-time data from IoT enabled devices, sensors, and weather and seismic monitoring. Data from sensors and geological data can be used to predict equipment failure and create predictive maintenance models to optimize performance and life of machinery. Real-time data can also help in enhancing oil recovery for oil & gas companies by analyzing drilling, production and seismic data. This helps production engineers understand when to make changes to the oil reservoir, and to oil lifting and extraction processes. This can add to thousands of dollars in earnings by averting potential losses, increasing oil output, and maintaining the health of equipment. Real-Time Data for Oil & Gas Companies

Real-time Data Use Cases for Energy Services
Machine generated data thrown up by smart meters helps energy companies to discern patterns in consumption. This enables insights in real-time to optimize energy distribution and to increase the reliability of the grid and deliver better remote operations support. Data from IoT enabled energy devices and equipment helps to predict power failures so remediation measures can be taken, thereby reducing power outages and the costs linked to routine physical inspections and maintenance. Real-Time Data for Energy Services

Real-time Data Use Cases for the Mining Industry
Mining industry machinery and equipment is largely digitally operated and throws out a lot of real-time time series data through IoT, that produces data patterns. The data can be used to power AI enabled predictive maintenance models that can predict critical asset downtime and reduce operational risks. Attributes that can help AI determine the health of an asset include temperature, pressure, noise and vibration. The data can help users decide the optimal way to use equipment and machinery, when replacements and maintenance would be due, and the frequency of checkups needed.

Transportation data from tracking vehicles can also be replicated for real-time analytics, so organizations can use this data to discover transit time delays, unloading times and other factors that may be causing issues. Based on the data, planning of loading and unloading can be done in a cost-efficient manner, besides instituting maintenance processes for trucks and specialized vehicles, and to reduce labor costs. Real-Time Data for Mining Services

Real-Time Data Use Cases in the Telecom Industry
Telecom companies generate huge amounts of data from phone calls, sensor data from network, browsing data etc. This real-time data can provide valuable, actionable business insights. Analyzing data consumption patterns and network performance helps telecom companies to better allocate network resources and create predictive capacity models to deliver a smooth performance.

They also get a better idea of their customers since customer data from multiple sources can present a holistic view of the customer. This helps analysts create predictive models that can indicate churn, help in customer segmentation, increase wallet share by customizing products, and make for a focused and improved customer experience. Real-Time Data for the Telecom Industry

Real-Time Data Use Cases in Banking & Financial Services
Financial services are one area where real-time data is of paramount importance. Customer interactions and transactions of the customers are replicated in real-time and can be analyzed and used by employees to upsell and cross-sell products, make targeted offers and generally build closer relationships with customers. Real-time data also helps prevent credit card fraud. By tracking customer transactions in real-time, and replicating them almost instantly into a production database, companies can detect anomalies in transaction patterns and send SMS alerts for suspicious activities. Real-Time Data for Banks and Financial Services

Real-Time Data Use Cases in Healthcare Services
Instead of manually monitoring a patient’s vital signs, healthcare professionals can respond to critical situations proactively by having wireless sensors and wearable devices on patients record and transmit data which can be collected in real-time. Over time, the accumulated data can be used for healthcare predictive analytics to form algorithms that can predict and avert a medical crisis.

Very large real-time datasets are also needed for advanced medical research. For e.g. medical trials involve all kinds of data, right from demographic data, patient data, trial controls and protocols, drug interactions and patient responses. This data can be replicated for healthcare analytics and Machine Learning models so researchers and doctors can form an informed and precise view of the trial’s outcomes.

Real-time data also provides a unified view of the patient to healthcare facilities, since the personal and medical data of every patient is logged into the medical facilities system – prescription, treatment details, attending doctors, payments, hospitalization records and more. This makes responding to queries, booking an appointment or making a payment much faster, making for a better customer experience. Real-Time Data for Healthcare Services

Real-Time Data Replication Methods

Real-Time Data Replication with Built-In Replication Mechanisms
Many modern transactional databases such as Oracle, Postgres, Maria DB etc. have support for built-in data replication mechanisms to help data get backed up to a replica database. These mechanisms are easy to set up and reliable. They use ‘log reading’ technology to perform CDC.

Limitations of Built-In Mechanisms for Real-Time Data Replication

  • Built-in replication for replicating data may involve expensive licensing. For e.g. Oracle’s GoldenGate in most cases needs paid licensing.
  • Successful built-in replication needs source and destination databases to be compatible. Replicating data to a database running on another software version could pose technical challenges.
  • Built-in replication mechanisms need source and destination databases to be using the same technology stack and to be from the same vendor. For e.g. if you are replicating data from Oracle to MySQL, technical issues may crop up.

Real-Time Data Replication with Transaction Logs
Transaction logs in a database monitor and record operations happening in the database. Database logs contain detailed records about tasks like inserts, updates, deletes, data definition commands etc. and keep track of the timeline points where these events occur. The records are then scanned to identify changes and applied to the target database. Implementing real-time replication involves scanning these transactional logs, queuing changes, and custom scripting to ensure updates are reflected accurately in the destination database. These databases enable users to access transaction logs asynchronously to get the changes and these use a messaging queue software like Apache Kafka to replicate changes to the target database. Kafka CDC and Oracle to Kafka CDC Methods

Limitations of Transaction Logs for Real time Data Replication

  • Database Replication with Transaction logs may need special connectors to access the database. For e.g. accessing the transaction logs of your Oracle DB will need either an open-source connector or a commercial licensed one.
  • There may be substantial coding effort involved which is time-consuming and expensive, if you cannot find a connector to parse the DB’s transactional logs. In such a case scripting your own parser is the only solution.

Real-Time Data Replication with Cloud-Based Mechanisms
If you have a cloud-based database to store and manage operational data, your Cloud platform would have replication mechanisms in place. For e.g. the AWS platform enables you to integrate data from events and replicate them via streaming services like AWS Kinesis and Lambda with minimal coding.

Limitations of Cloud-based Mechanisms for Real time Data Replication

  • If your source and target belong to different Cloud platforms, or a third-party database service, you may face issues.
  • For adding and supporting transformation-based functionality, you many need to implement code snippets which will be an additional effort and expense.

Real-Time Data Replication with Trigger-Based Custom Solutions

Modern databases, like Oracle and MariaDB, have the built-in functionality to create triggers on tables and columns. Triggers are a form of custom solutions that can enable real-time data replication when pre-defined changes happen in the source database. When the changes align with the terms specified, triggers record them into a ‘Change Table’ much like an audit log. All the updates that should be applied to the destination database are stored in the ‘Change Table’. A timestamp column is not needed in this case but rather a queueing tool or platform is needed (e.g., Kafka, RabbitMQ). to apply the changes to the target tables. The triggers act as callback functions, facilitating the transfer of data changes to a destination database via a network transport layer.

Limitations of Triggers for Real time Data Replication

  • Triggers are restricted to a small set of database operations like inserts, updates or calling a stored procedure. For instance, triggers cannot capture DDL changes. To comprehensively capture all changes for CDC, trigger-based solutions may need to be used in conjunction with other methods like polling.
  • Triggers can overload the source database and can delay transactions, since transactions are put on hold until the trigger executes. This affects overall system performance, since the database gets locked in trigger operations and a standby is created for future changes.

Real-Time Data Replication with Continuous Polling Methods
Continuous polling mechanisms are used to create custom code snippets to copy data from the source database to the target database. After that the polling mechanisms continuously monitor the data for changes at source. Custom code snippets are designed to detect data changes, format them and update the target database. Continuous polling mechanisms use queueing mechanisms to decouple the process where the source and target database cannot connect or need isolation.

Limitations of Polling for Real time Data Replication

  • With continuous polling mechanisms, you need a specific field in the source database that can be used by the custom code snippets to monitor and capture changes. These are usually timestamp-based columns that update when there are changes in the database. However, one drawback is that deletes cannot be captured by this mechanism.
  • Polling scripts can put extra burden on the source database affecting the database performance and response speed, especially with larger volumes of data. Potential impact on database performance must be considered before adopting polling methods, and they need to be meticulously planned and implemented.

Real-Time Data Replication with Third-Party Tools
There are a wide range of real-time data replication and ETL tools available in the market which can automate almost all of the replication processes. Some notable tools include Qlik Replicate, Fivetran and our very own BryteFlow. If you are confused what a real-time data replication tool must deliver, here’s a small checklist. How BryteFlow Works

What should you look for in a data replication tool?

A data replication tool is key to an efficient real-time replication process. What attributes should an ideal replication tool have? Here we present a few and discuss how our replication tools BryteFlow Ingest and BryteFlow XL Ingest match up against them.

Does the data replication tool provide high performance?
Your replication tool needs to deliver high scalability, low latency and high throughput. It should be very scalable to handle the ever-increasing amounts of data being created every day. The replication tool ought to be able to handle many times the average data volumes, and be able to deliver the data fast with low latency (near real-time) and high throughput.

Note: BryteFlow can handle petabytes of data with ease. It uses parallel, multi-thread loading, smart configurable partitioning and compression mechanisms to replicate data in real-time with very high throughput (approx. 1,000,000 rows per 30 seconds). It is at least 6x faster than Oracle GoldenGate and much faster than most other replication tools in the market, including Qlik Replicate and Fivetran.

Does the data replication tool automate processes?
A good data replication tool must automate replication processes for the user so that he or she is free of the drudgery of coding and maintenance. It also future proofs your data since you don’t need to write new code for connecting to fresh data sources. You should be able to integrate new sources easily and automatically.

Note: BryteFlow automates every replication process including data extraction, data ingestion, Change Data Capture, DDL, data mapping, data prep, masking, merging and SCD Type 2 history. It creates schema and tables automatically on target. Most importantly, it frees up the time of your DBA and data team to focus on high-priority tasks.

Does the data replication tool use log-based CDC to deliver data?
For real-time data replication you need some kind of CDC or streaming mechanism to be in place. CDC can be performed using different methods, but log-based CDC is considered the gold standard here. Log-based CDC is enterprise grade and does not impact the source. Data can be extracted in real-time or at a scheduled frequency. Unlike other CDC mechanisms, it captures all inserts, updates and deletes, making the data trustworthy at the destination.

Note: After the initial full ingest of data, BryteFlow uses log-based Change Data Capture to capture all subsequent changes in the source database continuously or at a scheduled frequency specified by you. It provides Automated Upserts, automatically merging change data with existing data, instead of creating new rows. This is very low-impact and does not affect source systems. No third-party tool or coding is required. Why automate CDC?

Can the data replication tool integrate data from multiple sources?
In the future you may need to bring in data from new and different sources to your production database. You must consider whether the replication tool is capable of data integration – connecting to different database technologies to aggregate the data? After all data teams would prefer one tool to perform replication, ETL and data integration, rather than have a complex implementation starring two or three tools.

Note: BryteFlow can connect to a wide range of transactional databases and applications to replicate and integrate data on the destination database or warehouse, both on-premise and in the Cloud. Data from transactional sources like SAP, Oracle, SQL Server, MySQL and PostgreSQL is delivered to popular platforms like AWS, Azure, SQL Server, BigQuery, PostgreSQL, Snowflake, SingleStore, Teradata, Databricks and Kafka in real-time.

Does the data replication tool provide data versioning?
Data versioning is a must when you are replicating real-time data. It allows you to perform point-in-time analytics and predict trends. Ensure your data replication tool offers this.

Note: With BryteFlow you have the option to save data with SCD Type2 history. BryteFlow Ingest provides out-of-the-box options to maintain the full history of every transaction, with options for automated data archiving. You can go back and retrieve data from any point on the timeline.

Can the data replication tool extract data from hard to replicate sources like SAP systems?
Siloed legacy data like data from SAP systems with multiple customizations and different versions can be incredibly complex and tough to extract. Most replication tools cannot handle SAP data, make sure your data replication tool can, especially if you are looking at SAP sources in your implementation. SAP S/4 HANA and 5 Ways to Extract S4 ERP Data

Note: BryteFlow has a special ETL tool for SAP data called the BryteFlow SAP Data Lake Builder. While BryteFlow Ingest easily extracts and loads data from SAP databases (if access is available), the BryteFlow SAP Data Lake Builder extracts data from SAP systems with business logic intact, creating schema and tables automatically on destination. It delivers data from SAP applications to on-premise and Cloud destinations and has flexible connections for SAP including: Database logs, ECC, HANA, S/4HANA , SAP SLT, and SAP Data Services. BryteFlow replicates SAP data to on-premise and Cloud destinations like Amazon S3, Redshift, Snowflake, SingleStore, Azure Synapse, ADLS Gen2, SQL Server, Databricks, PostgreSQL, Google BigQuery, and Kafka. Check out BryteFlow for SAP

Does the data replication tool provide reliable, trustworthy data?
There are replication tools and there are replication tools. Not all of them provide data that is reliable and accurate. You need a database replication tool that can handle network interruptions well so that no part of the data goes missing. Ideally it should have a mechanism to alert you to incomplete data.

Note: Replication with BryteFlow delivers trustworthy data. This is because BryteFlow Ingest integrates seamlessly with BryteFlow TruData, our data reconciliation tool that compares datasets at source and target using row counts and columns checksum. As a timesaving feature you can specify which key columns you need reconciled (rather than all of them) for faster reconciliation. BryteFlow also has an automated catchup mode that enables the replication process to resume from the point it had stopped, possibly due to network outage or server failure.

Does the data replication tool provide consumption-ready data?
Some replication tools deliver data that may need further transformation efforts to render it ready for use on target. This can take up time and effort.

Note: BryteFlow provides out-of-the box data type conversions (Parquet-snappy, ORC) so that the data delivered on target can be consumed for analytics or machine learning models right away, providing a high ROI on your data.

Does the data replication tool ensure security?
Data security is of paramount importance for businesses that manage sensitive data. Check whether the data replication software enables strong security for your sensitive data such as data encryption, and masking. Also how the data is stored and used, is it subject to role-based permissions, does the tool comply with relevant data security regulations?

Note: BryteFlow has an option to mask your sensitive data. As far as data security is concerned, the way BryteFlow works is that your data never leaves your private network, so your data is always subject to whatever data security measures and regulations you have put in place.

Does the data replication tool have a user-friendly interface and is easy to configure and use?
The data replication software needs to be easy to use, with a user-friendly interface and intuitive workflows that allow ordinary users without technical expertise to set up and manage data replication processes. It should not involve a lot of manual coding for configuration.

Note: BryteFlow has a simple user-friendly, point-and-click UI that any business user can use comfortably. It has a dashboard to monitor the replication instances and their status so that you know exactly what is happening. Also, it can be configured in just a day, is easy to deploy, and you can start receiving data in just a couple of weeks!

Does the data replication tool allow data filtering?
It is possible that you may have huge datasets from which you need just some columns of data, or data from a certain period or a particular type of data. Rather than replicating the entire dataset, a good replication tool should allow for data to be filtered, using specific criteria like date range, data type or source system. This reduces load on the system, enhances performance and cuts down on the volume of data that needs to be copied, saving time overall.

Note: BryteFlow allows you to select the tables to be replicated and the date range you need. It allows for smart, configurable partitioning so you can select data based on parameters you put in. It allows for selection of columns using Primary Key and has a masking option for sensitive data.

Does the data replication tool provide some transformation?
Not every data replication tool supports data transformation. However, this is a much-needed function to enable replication of data in the correct format and structure across different platforms. Data formats and configurations may vary on different databases and applications, and the data replication tool should enable some data transformation to ensure consistency across platforms.

Note: BryteFlow Blend is our data transformation tool that delivers transformed and prepared data using simple SQL. BryteFlow Blend transforms, remodels, schedules, and merges data from multiple sources like SAP, Oracle, SQL Server, Postgres, MySQL etc., in real-time on platforms like Amazon Redshift, Amazon S3, PostgreSQL and Snowflake. It integrates seamlessly with BryteFlow Ingest and has an intuitive drag-and-drop interface for complete end-to-end workflows. The tool provides ready-to-use data in real-time for Analytics, AI, and ML use cases.

If you would like a demo of BryteFlow, do get in touch.

Conclusion
We have seen how real-time data replication works, replication mechanisms to achieve it, and how it can benefit organizations to deliver better business performance. We have also seen what an ideal real-time replication tool should look like, and how BryteFlow fits the bill.