Real-Time Data Streaming: 100B Records MySQL to TiDB

The Challenge: Real-Time Streaming + 100 Billion Historical Records

A leading payment service provider came to Xstreami with a primary goal: set up a real-time data streaming pipeline so every transaction in their MySQL databases would instantly flow into a high-performance TiDB analytics layer. Standard streaming — or so it seemed.

Then came the full picture. Their system held over 100 billion historical payment records spread across 40 MySQL tables — years of transactions, merchant profiles, fraud logs, reconciliation data, settlement records, and more. This historical data could not be left behind. It needed to be transformed with the same complex business logic as the live stream and loaded into TiDB before go-live. And there was one non-negotiable requirement: absolute zero data loss.

The Streaming Requirement

Every new payment transaction — INSERTs, UPDATEs, DELETEs — had to be captured from MySQL in real-time, transformed on-the-fly with complex business logic, and delivered to TiDB in under 100 milliseconds.

The Historical Data Requirement

100 billion+ existing records from 40 fragmented MySQL tables needed to be read, enriched, transformed through multi-layer business logic, and loaded into a single consolidated TiDB table — with guaranteed zero data loss.

The operational challenges driving this transformation were severe:

● Query Performance Collapse: Joining 15–20 MySQL tables for a single analytics query took 5–10 seconds during peak hours — unacceptable for a real-time payment environment
● Massively Complex Transformation Logic: Business rules spanned fee calculations, currency conversions, tiered risk scoring, AML compliance flags, merchant-level settlement adjustments, and dynamic fraud thresholds — all needing to apply consistently to both historical and live data
● Zero Tolerance for Data Loss: In a regulated payments environment, even a single missing record means compliance failure, reconciliation errors, and potential financial liability. Data accuracy had to be 100%
● No Production Disruption: The migration of 100B+ historical records had to run in parallel with live transaction processing — MySQL could not be taken offline, and live streaming could not be delayed
● No Custom Code Acceptable: Engineering resources were already stretched. The team needed a platform that business analysts could configure and update without writing or deploying code

The core requirement in one sentence: Stream live MySQL transactions to TiDB in real-time while simultaneously transforming 100 billion historical records — all with zero data loss and zero custom code. No traditional ETL tool on the market could do both simultaneously at this scale.

The Xstreami Solution: No-Code Data Transformation

Enter Xstreami—a cutting-edge real-time data streaming platform that transforms how organizations handle data consolidation and migration. For this payment service, Xstreami became the bridge between their legacy MySQL infrastructure and a modern, high-performance TiDB database.

High-Level Architecture

Dual-mode: Historical IDS + Live CDC streaming in one unified pipeline

Source

MySQL Database

40 Payment Tables
100B+ Historical Records
Live CDC Binlog

CDC +
IDS

Platform

Xstreami Engine

No-Code Transform
IDS + Live CDC Stream
Zero Data Loss

<100ms
Latency

Destination

TiDB Analytics

1 Unified Table
Sub-100ms Queries
Infinite Scale

What Makes Xstreami Different?

Unlike traditional ETL tools that require extensive programming and maintenance, Xstreami offers a revolutionary approach to data consolidation:

No-Code Transformation

Configure complex data transformations through an intuitive UI. No Python scripts, no Java code, no SQL procedures—just point, click, and transform.

Real-Time Processing

Stream changes from MySQL to TiDB in milliseconds. Every payment transaction is instantly available for analytics and reporting.

Initial Data Sync (IDS)

Migrate historical data at scale before switching to real-time streaming. Handle 100 billion records efficiently with zero data loss.

Single-Click Updates

Modify transformation logic instantly through the UI. No code deployments, no downtime, no risk—just immediate updates.

Universal Connectors

Connect to 50+ data sources and destinations. MySQL, PostgreSQL, MongoDB, TiDB, Snowflake, Kafka, and more—all pre-built and ready to use.

Enterprise-Grade Reliability

Built-in monitoring, automatic retries, data validation, and exactly-once delivery guarantees ensure your payment data is always accurate.

The Migration Journey: From 40 Tables to 1

The transformation happened in two critical phases: Initial Data Sync (IDS) for historical data migration, followed by continuous real-time streaming for ongoing transactions.

1

Phase 1: Planning

Architecture Design & Configuration

The team used Xstreami's visual interface to map the 40 MySQL source tables to a unified TiDB schema. All transformation logic—data enrichment, field mapping, calculations, and business rules—was configured through the UI without writing a single line of code. The entire configuration took just 2 days.

2

Phase 2: Initial Data Sync

Transforming 100 Billion Historical Records With Zero Data Loss

Xstreami's IDS engine kicked off the massive data migration. Across the migration window, 100 billion payment records were read from MySQL, transformed according to the configured logic, and loaded into TiDB. The parallel processing capabilities ensured minimal impact on production MySQL databases. Progress was monitored in real-time through Xstreami's dashboard.

3

Phase 3: Real-Time Streaming

Switching to Live Data Replication

Once IDS completed, Xstreami automatically transitioned to real-time streaming mode. Every INSERT, UPDATE, and DELETE operation in the MySQL payment tables was captured instantly and streamed to TiDB with the same transformation logic. Latency from source to destination: less than 100 milliseconds.

4

Phase 4: Validation

Data Quality Verification

The team ran comprehensive validation checks comparing MySQL source data with TiDB destination data. Xstreami's built-in data quality tools verified record counts, field values, and business logic correctness. Success rate: 99.999%—well within acceptable thresholds for a payment system.

5

Phase 5: Production

Go-Live & Continuous Operation

The payment service cutover to querying TiDB for analytics and reporting while maintaining MySQL for transactional operations. Xstreami continues to run 24/7, streaming every payment transaction in real-time. The no-code platform allows the team to modify transformation logic instantly whenever business requirements change.

Deep Dive: The Payment Data Transformation

Let's examine exactly how Xstreami transformed the complex 40-table payment structure into a single, queryable table optimized for high-performance data pipelines.

Source: 40 MySQL Payment Tables

The original MySQL schema was highly normalized across multiple categories:

MySQL Source Schema (Simplified)
-- Core Transaction Tables
                                                            transactions (txn_id, merchant_id, customer_id, amount, currency, status, created_at)
                                                            transaction_details (detail_id, txn_id, payment_method_id, gateway_id, reference_id)
                                                            transaction_items (item_id, txn_id, product_id, quantity, unit_price)
                                                            -- Party Tables
                                                            merchants (merchant_id, name, category_id, country_id, status, kyc_status)
                                                            customers (customer_id, name, email, phone, risk_score, tier)
                                                            merchant_accounts (account_id, merchant_id, bank_id, account_number)
                                                            -- Payment Method Tables
                                                            payment_methods (method_id, customer_id, type, provider, card_last4, expiry)
                                                            payment_gateways (gateway_id, name, provider, region, fees)
                                                            payment_processors (processor_id, gateway_id, processor_code, status)
                                                            -- Settlement & Reconciliation
                                                            settlements (settlement_id, txn_id, amount, status, settled_at)
                                                            reconciliation (recon_id, settlement_id, status, discrepancy_amount)
                                                            payouts (payout_id, merchant_id, amount, status, payout_date)
                                                            -- Risk & Compliance
                                                            fraud_checks (check_id, txn_id, risk_score, rules_triggered, status)
                                                            compliance_logs (log_id, txn_id, check_type, result, timestamp)
                                                            aml_records (aml_id, customer_id, risk_level, last_check_date)
                                                            -- Reference Data
                                                            countries (country_id, name, code, currency, tax_rate)
                                                            currencies (currency_id, code, symbol, exchange_rate)
                                                            merchant_categories (category_id, name, mcc_code, risk_level)
                                                            products (product_id, name, category, price)
                                                            -- Plus 20+ more tables for fees, disputes, refunds, chargebacks, etc.
                                                                                    

The Challenge: A typical payment analytics query required joining 15-20 of these tables, resulting in:

Query execution times of 5-10 seconds during peak hours
Heavy load on the production MySQL database
Complex SQL that only senior engineers could write
Delayed insights due to query performance bottlenecks

Transformation: Xstreami's No-Code Logic

1

Source Capture

Monitor all 40 MySQL tables for changes using CDC (Change Data Capture). Capture every INSERT, UPDATE, and DELETE operation in real-time.

2

Event Enrichment

For each transaction event, perform lookups to enrich data from related tables: merchant details, customer info, payment method, gateway, country, currency, etc.

3

Field Mapping

Map source fields to destination schema. Rename fields for clarity, combine fields where needed, and format data according to business requirements.

4

Calculations

Compute derived fields: fees, net amounts, currency conversions, tax calculations, merchant share, risk scores, and business metrics.

5

Data Validation

Apply validation rules to ensure data quality: check for null values, validate formats, verify referential integrity, and flag anomalies.

6

Load to TiDB

Write the enriched, transformed record to the consolidated TiDB table using optimized batch inserts for maximum throughput.

The Power of No-Code: All of this complex transformation logic was configured through Xstreami's visual interface in less than 2 days. No Java, no Python, no SQL scripts. Just point-and-click configuration with instant deployment.

Destination: Single TiDB Payment Analytics Table

The resulting schema in TiDB is a fully denormalized, analytics-optimized table:

TiDB Destination Table
CREATE TABLE payment_analytics (
                                                                -- Transaction Core
                                                                txn_id                  VARCHAR(50) PRIMARY KEY,
                                                                txn_timestamp           TIMESTAMP,
                                                                txn_amount              DECIMAL(15,2),
                                                                txn_currency            VARCHAR(3),
                                                                txn_status              VARCHAR(20),
                                                                
                                                                -- Merchant Information
                                                                merchant_id             VARCHAR(50),
                                                                merchant_name           VARCHAR(200),
                                                                merchant_category       VARCHAR(100),
                                                                merchant_country        VARCHAR(50),
                                                                merchant_risk_level     VARCHAR(20),
                                                                
                                                                -- Customer Information
                                                                customer_id             VARCHAR(50),
                                                                customer_name           VARCHAR(200),
                                                                customer_email          VARCHAR(200),
                                                                customer_tier           VARCHAR(20),
                                                                customer_risk_score     INT,
                                                                
                                                                -- Payment Details
                                                                payment_method_type     VARCHAR(50),
                                                                payment_provider        VARCHAR(100),
                                                                payment_gateway         VARCHAR(100),
                                                                card_last4              VARCHAR(4),
                                                                
                                                                -- Financial Calculations
                                                                gross_amount            DECIMAL(15,2),
                                                                gateway_fee             DECIMAL(10,2),
                                                                platform_fee            DECIMAL(10,2),
                                                                tax_amount              DECIMAL(10,2),
                                                                net_amount              DECIMAL(15,2),
                                                                merchant_share          DECIMAL(15,2),
                                                                
                                                                -- Settlement Info
                                                                settlement_status       VARCHAR(20),
                                                                settlement_date         DATE,
                                                                payout_status           VARCHAR(20),
                                                                
                                                                -- Risk & Compliance
                                                                fraud_check_status      VARCHAR(20),
                                                                fraud_risk_score        INT,
                                                                aml_check_status        VARCHAR(20),
                                                                compliance_flags        JSON,
                                                                
                                                                -- Location Data
                                                                country_code            VARCHAR(3),
                                                                country_name            VARCHAR(100),
                                                                region                  VARCHAR(100),
                                                                
                                                                -- Metadata
                                                                created_at              TIMESTAMP,
                                                                updated_at              TIMESTAMP,
                                                                data_version            INT,
                                                                
                                                                -- Indexes
                                                                INDEX idx_merchant (merchant_id),
                                                                INDEX idx_customer (customer_id),
                                                                INDEX idx_timestamp (txn_timestamp),
                                                                INDEX idx_status (txn_status)
                                                            );
                                                                                    

The Result: Simplified Queries

What once required complex 15-20 table joins now becomes:

Optimized TiDB Query
-- Get all high-value transactions for a merchant in the last 30 days
                                                            SELECT 
                                                                txn_id,
                                                                txn_timestamp,
                                                                customer_name,
                                                                txn_amount,
                                                                payment_method_type,
                                                                fraud_risk_score,
                                                                settlement_status
                                                            FROM payment_analytics
                                                            WHERE merchant_id = 'MERCH_12345'
                                                              AND txn_timestamp >= NOW() - INTERVAL 30 DAY
                                                              AND txn_amount > 10000
                                                            ORDER BY txn_timestamp DESC;
                                                            -- Query time: 0.2 seconds ⚡
                                                            -- Previously: 8.5 seconds with 15+ table joins
                                                                                    

Performance & Business Impact

The migration to Xstreami delivered transformative results across all dimensions of the payment service's data infrastructure:

Metric	Before Xstreami	After Xstreami	Improvement
Query Response Time	5-10 seconds	0.2-0.5 seconds	95% faster
Data Freshness	15-30 minute delay	<100 milliseconds	Real-time
Tables to Query	15-20 joins	Single table	Zero joins
Developer Time per Report	2-3 days	2-3 hours	90% reduction
Infrastructure Load	High MySQL CPU	Minimal impact	Isolated workloads
Schema Change Time	2-3 weeks	1-2 hours	Single-click updates
Code Maintenance	Complex ETL scripts	Zero code	100% reduction
Data Quality Issues	Frequent discrepancies	99.999% accuracy	Automated validation

Real Business Benefits

Revenue Impact

Faster payment processing and real-time fraud detection prevented an estimated $2M in potential fraud losses in the first quarter after deployment.

Time-to-Market

New payment features and reports now launch 10x faster. What took weeks of development now takes hours of configuration.

Business Intelligence

Real-time dashboards enabled instant decision-making. Executives now see payment trends as they happen, not hours later.

Team Productivity

Data analysts can now create reports independently without waiting for engineering resources. SQL complexity reduced by 80%.

Risk Management

Real-time fraud detection and compliance monitoring became possible, dramatically reducing exposure to fraudulent transactions.

Scalability

System now handles 3x transaction volume with no performance degradation. Ready to scale to 10x with TiDB's distributed architecture.

The Bottom Line: The payment service achieved a 10x improvement in data infrastructure efficiency while eliminating the need for complex custom code. Xstreami's no-code platform transformed their ability to deliver real-time payment insights at massive scale.

How Xstreami Achieved Zero Data Loss at 100 Billion Record Scale

Transforming 100 billion historical payment records while simultaneously streaming live transactions is not a problem that scales with brute force. It requires an architecture purpose-built for exactness — where every record is accounted for, every transformation is verifiable, and no interruption can cause data loss. Xstreami's Initial Data Sync (IDS) engine was designed for exactly this.

Why IDS Guarantees Zero Data Loss

Xstreami's IDS engine eliminates data loss through layered, redundant guarantees — not just one safeguard, but many:

IDS Execution: 4 Stages, Zero Data Loss

Every stage has built-in checkpoints, automatic retry, and validation — data loss is architecturally impossible

Parallel Extract

Multi-threaded reads from all 40 MySQL tables simultaneously. Peak throughput: 15,000+ records/sec

Complex Transform

Same no-code business logic applied: fee calc, risk scoring, currency conversion, AML flags, enrichment

Checkpoint & Retry

Continuous progress tracking. Any interruption resumes from exact last checkpoint — no re-processing, no data loss

Validate & Certify

Checksum verification, field-level validation, and row-count reconciliation before marking each batch complete

Zero Data Loss Guarantee: 100 billion records processed → 100 billion records validated in TiDB. 0% discrepancy. Zero tolerance, achieved.

IDS Performance Highlights:

→ Peak throughput sustained at 15,000+ records per second without impacting live MySQL workloads
→ Automatic recovery from network interruptions — resumes at exact checkpoint, zero duplicate records
→ Parallel processing across all 40 source tables with intelligent rate limiting
→ End-to-end data validation success rate: 100% — every record accounted for

Seamless Transition to Real-Time

Once IDS completed, Xstreami automatically switched to real-time streaming mode. This transition was completely transparent—no manual intervention required, no service disruption, no downtime.

From that moment forward, every payment transaction in MySQL was:

Captured via Change Data Capture (CDC) within milliseconds
Transformed according to the same no-code logic configured in the UI
Streamed to TiDB with sub-100ms latency
Available immediately for real-time analytics and reporting

The No-Code Advantage

Perhaps the most revolutionary aspect of this project wasn't the scale or speed—it was achieving all of this without writing a single line of code.

Traditional ETL vs. Xstreami

Aspect	Traditional ETL	Xstreami Platform
Development	Months of coding in Python/Java	Days of visual configuration
Skills Required	Senior data engineers	Analysts with business knowledge
Logic Changes	Code deployment (hours/days)	Single-click update (seconds)
Testing	Manual test scripts, staging env	Built-in validation, preview mode
Monitoring	Custom logging, external tools	Real-time dashboard included
Error Handling	Custom retry logic, alerting	Automatic retries, dead letter queue
Maintenance	Ongoing code updates, debugging	Minimal—platform handles it
Documentation	Must be written separately	Self-documenting configuration

Single-Click Logic Updates

One of the most powerful features of Xstreami is the ability to modify transformation logic instantly through the UI. Here's a real example from the payment project:

Scenario: The business team decided to change how merchant fees are calculated—from a flat percentage to a tiered structure based on transaction volume.

Traditional Approach:

Data engineer updates Python ETL script
Code review and testing (2-3 days)
Deployment to staging environment
UAT and validation (1-2 days)
Production deployment window (off-hours)
Total time: 1 week minimum

Xstreami Approach:

Business analyst updates fee calculation rule in UI
Preview transformation with sample data
Click "Deploy" button
Logic goes live instantly, applies to all new transactions
Total time: 15 minutes

Why No-Code Matters for Payment Systems

In the fast-paced world of fintech data streaming, agility is everything:

Regulatory Changes: When compliance requirements change, payment systems must adapt immediately. Xstreami enables same-day compliance updates.
Product Launches: New payment methods, currencies, or merchant types can be added to the data model without code deployments.
Business Experimentation: A/B testing different risk models or pricing structures requires rapid data pipeline changes—impossible with traditional ETL.
Reduced Risk: No code means no code bugs, no deployment failures, no production incidents from typos in SQL scripts.
Team Empowerment: Business analysts who understand payment logic can make changes directly without waiting for engineering resources.

Beyond Payment Systems: Universal Connectors

While this case study focuses on MySQL to TiDB migration, Xstreami's platform offers universal connectivity across 50+ data sources and destinations.

Supported Connectors

Relational Databases

MySQL, PostgreSQL, SQL Server, Oracle, MariaDB, TiDB, CockroachDB

NoSQL & Cloud

MongoDB, Cassandra, DynamoDB, Redis, Elasticsearch

Data Warehouses

Snowflake, BigQuery, Redshift, Azure Synapse, Databricks

Messaging Systems

Kafka, RabbitMQ, AWS Kinesis, Azure Event Hubs, Pulsar

File Systems

S3, GCS, Azure Blob, HDFS, FTP, SFTP

APIs & SaaS

REST APIs, Salesforce, HubSpot, Zendesk, custom integrations

Multi-Source Scenarios

Xstreami excels at consolidating data from multiple sources into unified destinations:

Customer 360: Combine data from CRM (Salesforce), support tickets (Zendesk), orders (MySQL), and product usage (MongoDB) into a single customer view
Real-Time Analytics: Stream from operational databases (PostgreSQL), clickstream data (Kafka), and mobile events (Kinesis) into BigQuery
Hybrid Cloud: Synchronize on-premise Oracle databases with cloud-native services like Snowflake and Redshift
Event-Driven Architecture: Capture changes from any database and publish to Kafka topics for downstream microservices

The power of universal connectors: Whether you're migrating MySQL to TiDB (like this case study), syncing PostgreSQL to Snowflake, or streaming MongoDB to Elasticsearch—Xstreami provides the same no-code experience with enterprise-grade reliability.

Key Takeaways for Payment & Fintech Companies

This real-world payment system transformation demonstrates the transformative potential of modern real-time data streaming platforms. Here are the critical lessons:

Real-Time is Non-Negotiable

In payment systems, delayed data means delayed fraud detection, delayed insights, and delayed decisions. Real-time streaming with sub-100ms latency should be the baseline, not the aspiration.

Consolidation Beats Complexity

40 normalized tables might be ideal for OLTP, but they're terrible for analytics. A well-designed consolidated table optimized for queries delivers 10-100x better performance than complex joins.

No-Code Enables Agility

When business logic changes (and in payments, it changes constantly), you need the ability to update transformations in minutes, not weeks. No-code platforms eliminate the development bottleneck.

IDS + Streaming = Complete Solution

You need both historical migration (IDS) and ongoing replication (streaming). Platforms that only do one force you to cobble together multiple tools with fragile integration points.

Data Quality is Built-In

Payment data must be 100% accurate. Choose platforms with automatic validation, exactly-once delivery guarantees, and comprehensive monitoring—not tools that require you to build these capabilities yourself.

Scalability from Day One

Payment volumes can spike 10x during peak seasons. Your data pipeline must scale horizontally without architectural changes. TiDB + Xstreami delivered this by design.

The Future of Payment Data Infrastructure: This project proves that modern real-time data platforms can handle the most demanding fintech workloads—100 billion records, 40-table consolidations, sub-100ms latency—all without custom code. This is the new standard for payment data pipelines.

Ready to Transform Your Payment Data Pipeline?

Discover how Xstreami can consolidate your complex database architecture into a real-time, high-performance data streaming platform—with zero programming required.

Get Started with Xstreami

Conclusion: The Real-Time Data Revolution

This payment service's journey from 40 fragmented MySQL tables to a single, real-time TiDB analytics powerhouse represents more than just a technical migration—it's a fundamental shift in how modern financial services should approach data infrastructure.

The results speak for themselves:

100 billion records migrated without disrupting live production operations using Xstreami's IDS engine
95% query performance improvement by eliminating complex multi-table joins
Real-time payment insights with sub-100ms latency from MySQL to TiDB
Zero lines of code written—all transformation logic configured through the UI
Single-click logic updates enabling rapid business adaptation
10x faster time-to-market for new payment features and reports

What makes this transformation truly remarkable is not just the scale or speed, but the accessibility. By providing a no-code data transformation platform, Xstreami democratized the ability to build and maintain enterprise-grade data pipelines. Business analysts who understand payment logic can now make changes directly, without waiting for engineering resources.

The Broader Impact

This case study has implications far beyond one payment service:

For fintech companies: Real-time payment data streaming is no longer a luxury requiring massive engineering investment—it's an accessible capability that can be deployed in weeks
For data teams: The no-code approach frees engineers from writing and maintaining repetitive ETL scripts, allowing them to focus on higher-value work
For business leaders: Faster data pipeline development means faster product launches, quicker regulatory compliance, and more agile responses to market changes
For the industry: As payment volumes continue to explode and real-time expectations become universal, platforms like Xstreami set the new standard for what's possible

The bottom line: Traditional batch ETL is dead. Custom-coded streaming pipelines are overkill. The future belongs to no-code, real-time data platforms that combine the power of enterprise-grade streaming with the accessibility of visual configuration. Xstreami is leading that future.

What's Next?

If you're operating a payment system, e-commerce platform, fintech application, or any data-intensive operation that requires real-time insights, the lessons from this case study are clear:

Assess your data architecture: How many tables are you joining? How long do queries take? How stale is your data?
Consider consolidation: Would a denormalized, analytics-optimized table structure serve your needs better?
Evaluate no-code platforms: Can you achieve your goals without writing custom code? (Hint: with Xstreami, probably yes)
Plan for scale: Will your architecture handle 10x growth? Xstreami + TiDB proved it can
Prioritize real-time: In 2024 and beyond, batch processing is a competitive disadvantage

The payment service in this case study took the leap—and achieved results that exceeded their most optimistic projections. Your organization can too.

Streaming 100B Records in Real Time: MySQL to TiDB Case Study

Subscribe for email updates

The Challenge: Real-Time Streaming + 100 Billion Historical Records

The Xstreami Solution: No-Code Data Transformation

What Makes Xstreami Different?

The Migration Journey: From 40 Tables to 1

Deep Dive: The Payment Data Transformation

Source: 40 MySQL Payment Tables

Transformation: Xstreami's No-Code Logic

Destination: Single TiDB Payment Analytics Table

The Result: Simplified Queries

Performance & Business Impact

Real Business Benefits

How Xstreami Achieved Zero Data Loss at 100 Billion Record Scale

Why IDS Guarantees Zero Data Loss

Seamless Transition to Real-Time

The No-Code Advantage

Traditional ETL vs. Xstreami

Single-Click Logic Updates

Why No-Code Matters for Payment Systems

Beyond Payment Systems: Universal Connectors

Supported Connectors

Multi-Source Scenarios

Key Takeaways for Payment & Fintech Companies

Ready to Transform Your Payment Data Pipeline?

Conclusion: The Real-Time Data Revolution

The Broader Impact

What's Next?

FAQ

Leave a Comment

Subscribe for email updates

Highlights

Database Services

Quick Links

Contacts

Nagercoil Office

Bangalore Office

Streaming 100B Records in Real Time: MySQL to TiDB Case Study

Subscribe for email updates

The Challenge: Real-Time Streaming + 100 Billion Historical Records

The Xstreami Solution: No-Code Data Transformation

What Makes Xstreami Different?

The Migration Journey: From 40 Tables to 1

Deep Dive: The Payment Data Transformation

Source: 40 MySQL Payment Tables

Transformation: Xstreami's No-Code Logic

Destination: Single TiDB Payment Analytics Table

The Result: Simplified Queries

Performance & Business Impact

Real Business Benefits

How Xstreami Achieved Zero Data Loss at 100 Billion Record Scale

Why IDS Guarantees Zero Data Loss

Seamless Transition to Real-Time

The No-Code Advantage

Traditional ETL vs. Xstreami

Single-Click Logic Updates

Why No-Code Matters for Payment Systems

Beyond Payment Systems: Universal Connectors

Supported Connectors

Multi-Source Scenarios

Key Takeaways for Payment & Fintech Companies

Ready to Transform Your Payment Data Pipeline?

Conclusion: The Real-Time Data Revolution

The Broader Impact

What's Next?

FAQ

Leave a Comment

Related Blogs

Subscribe for email updates

Highlights

Database Services

Quick Links

Contacts

Nagercoil Office

Bangalore Office