Building a Real-Time MySQL Data Pipeline for Lakehouse Integration

You can create a real-time MySQL data pipeline for lakehouse integration by leveraging Real-Time MySQL Replication and change data capture (CDC) methods. Real-Time MySQL Replication enables you to quickly transfer updates, inserts, and deletes from MySQL to a lakehouse such as Singdata Lakehouse. Hudi is compatible with these CDC scenarios and simplifies handling small updates. This approach allows you to seamlessly integrate OLTP data into your Singdata Lakehouse, enhancing analytics and providing a unified dataset for business decision-making. Be sure to select the CDC tool that best fits your requirements.

Key Takeaways

• Real-Time MySQL Replication and Change Data Capture (CDC) help send updates fast from MySQL to a lakehouse. This makes analytics better and helps people make choices faster.

• CDC moves only the data that has changed. This keeps your system running well. It also makes sure your analytics use the newest information.

• To set up MySQL for CDC, you need to turn on binary logging. You also need to give special permissions. This helps make sure all changes get recorded.

• Pick the best replication tool for what you need. Tools like AWS DMS, Debezium, and Fivetran have different features. They help move data in good ways.

• It is important to check your pipeline often. Use tools like Grafana and PMM to watch how it works. This helps you find problems early.

Real-Time MySQL Replication

CDC Basics

Change Data Capture (CDC) helps you move data fast. CDC watches every insert, update, and delete in MySQL. You only work with data that changes. This keeps your system quick and saves resources. CDC gives you fresh data for analytics. Your reports and dashboards show the newest information. CDC keeps your target database up to date. It also lowers the work your servers need to do.

Tip: CDC only moves changed data. You do not have to move lots of old data.

Here are some main benefits of CDC:

• You get real-time updates for analytics.

• Your system works faster by using only changed data.

• Your target database stays in sync all the time.

• You get correct insights from new data.

MySQL Setup

To start Real-Time MySQL Replication, you must set up CDC in MySQL. First, check if you have admin rights or make a user with the right permissions. Next, turn on binary logging and set the log format to ROW. You can check these settings with easy SQL commands:

SHOW VARIABLES LIKE 'log_bin';
SHOW VARIABLES LIKE 'binlog_format';

If binary logging is not ON or the format is not ROW, change them in your MySQL settings file. After you change the settings, restart your MySQL server. You should also give these permissions to your CDC user:

1. REPLICATION SLAVE

2. REPLICATION CLIENT

3. SUPER

This setup lets you catch every change and send it to your Singdata Lakehouse.

Replication Tools

You can use many tools for Real-Time MySQL Replication. Some popular tools are AWS DMS, Upsolver, SQLake, Debezium, Fivetran, Airbyte, and BladePipe. These tools support CDC and help you move data from MySQL to your Singdata Lakehouse. Each tool has features like automatic schema updates, error handling, and real-time integration.

Note: CDC gives you almost real-time sync. Lakehouses like Singdata Lakehouse work best with some delay. You get fast updates, but some analytics may use data that is a little behind.

Singdata Lakehouse Integration

Lakehouse Overview

A lakehouse mixes data lakes and data warehouses. You can keep raw and organized data together. Singdata Lakehouse uses this idea to help you handle all your data in one spot. You do not have to split data into different systems. This way, you do not have data silos. It is easier to find and use your data. Singdata Lakehouse also keeps your data safe and follows rules. You can trust that your private data is protected and meets laws. The platform runs in the cloud. You can make it bigger or smaller when you need.

Tip: With Singdata Lakehouse, you can change resources fast and do not have to stay with one cloud company.

Here are some good things about using MySQL data with a lakehouse:

Architecture

Singdata Lakehouse has layers. Each layer does something special. You can see how Real-Time MySQL Replication fits in:

• You can use tools like AWS DMS to move MySQL data into the lakehouse.

• The processing layer gets your data ready for quick searches.

• The metadata layer helps you keep your data neat and safe.

You might hear about Apache Iceberg and MySQL HeatWave Lakehouse. These tools help you handle data in object storage and make queries fast. MySQL HeatWave works with MySQL tools and can grow fast. You can also change the cluster size without stopping your work.

Object Storage

Object storage is very important for a lakehouse. You can keep many types of data in their original form. This makes it simple to add new data from MySQL or other places. Object storage grows as you need more space. You can use it in many cloud places, so you have more options.

When you search data in object storage, you get answers fast if you use good grouping and metadata. Tools like Apache Iceberg and MySQL HeatWave Lakehouse help you use object storage well. They make sure your Real-Time MySQL Replication pipeline gives you new data for analytics.

Pipeline Steps

Initial Load

You begin by setting up the initial load. This step moves all your MySQL data into the lakehouse. You must get MySQL ready for change data capture (CDC) replication. First, make a user with the right permissions. Then, turn on logical replication by changing the MySQL server settings. Set binlog_format to 'ROW'. Set binlog_row_image to 'FULL'. Set binlog_row_metadata to 'FULL'. Make sure binlog retention is at least 24 hours.

Tip: Use primary keys for every table. Do not use large object columns in tables that change a lot. Split big tables into parts for better speed.

Follow these steps for a good initial load:

1. Set up MySQL for CDC replication.

2. Connect to your MySQL database.

3. Put source tables in the data lake.

4. Move changed events from staging to your target table.

You can use tools like AWS DMS, Debezium, or Airbyte to help with these steps. These tools move data fast and keep your lakehouse current.

Note: Test your pipeline in a staging area before using it for real. This helps you find mistakes early.

Incremental Sync

After the initial load, you need to keep your lakehouse updated with MySQL. Incremental sync only moves new or changed data. This makes your pipeline quick and saves resources. You set up source and destination connections. Then, you make and set up your replication job. You can run the sync by hand or set a schedule.

Here is a simple way to do incremental sync:

1. Make source and destination connections.

2. Create and set up your replication job.

3. Run the sync by hand or on a schedule.

4. Watch job progress and performance numbers.

You can pick different ways to do incremental sync. For example, Flink CDC with Hudi gets changes from MySQL and writes them to Hudi tables. OLake reads MySQL's binary logs and writes data to Iceberg tables for analytics.

Tip: Pick tables to sync based on what your business needs. Change how often you sync to match your data flow.

Handling schema changes and errors is important during incremental sync. Mistakes can happen. Plan for problems by talking with development and database teams. Make a checklist before you change things. Use data lineage tools to see how data moves and what changes affect. Make a data contract to set clear rules for data format. Keep incidents separate to stop big failures. Make changes when traffic is low and have a plan to undo them. Test schema changes in a staging area and use versioning to track updates.

Alert: Schema changes can cause problems with data quality. New data may not fit new rules, so inserts can fail or break constraints. Always test changes before you use them.

Monitoring

You need to watch your pipeline to keep it working well. Checking replication lag and binlog growth helps you find issues early. Set alerts for delays in replication. Use monitoring tools to watch performance and catch mistakes.

Here are some popular monitoring tools and what they do:

• PMM helps you see performance numbers.

• ProxySQL balances loads and checks queries.

• Prometheus collects metrics for MySQL.

• Grafana shows data and sets alerts.

• Loki keeps logs together for easy fixes.

Tip: Watch your pipeline with real-time tools. Track data flow and quality to make sure results are right.

Testing and checking your pipeline before using it for real is important. Set rules for data quality like completeness, accuracy, and consistency. Use automated checks to learn about your data. Check data at different steps. Watch how data changes and compare results to key numbers. Use quarantine to keep bad records separate. Set up advanced monitoring and alerts to fix problems fast.

Emoji: 🛡️ Good monitoring keeps your pipeline strong and your data safe.

Tool Comparison

Open Source

You can pick open source tools like Debezium and Airbyte for MySQL replication. These tools give you lots of control. You can change them to fit your needs. Debezium uses Kafka to work on many servers. Airbyte gets bigger by adding more Docker containers or Kubernetes pods. Both tools have strong security features. Debezium can hide data with masking. Airbyte lets you use Single Sign-On, SSL, and role-based access controls.

• Debezium finds changes very fast. You get real-time sync between MySQL and your lakehouse.

• Airbyte has over 600 connectors. You can make ELT pipelines for many sources.

• You can make Airbyte faster by adding more workers.

Tip: Open source tools let you change your pipeline. You must handle updates and fixes yourself.

Managed Services

Managed services like Fivetran and Upsolver make things simple. You do not need to set up or take care of them. These services handle scaling and watching for you.

Fivetran gives you ready-made schemas and costs less to own. You get a managed way to work. Upsolver has live monitoring and strong ways to watch your data. You can see your pipelines and get alerts.

Note: Managed services cost more but save time. You get help and easy ways to connect with business apps.

Best Practices

You should use best practices to keep your pipeline strong and able to grow.

• Watch your pipeline in real time. Look for delays and check how it works.

• Make clear rules for data quality. Clean and check your data before loading.

• Build your pipeline so it can grow with your business. Plan for more data and users.

• Use modular design. Split your pipeline into smaller parts to manage easily.

• Test your pipeline with automatic checks. Find problems before you start using it.

🛡️ Good planning and watching help you stop common problems. You keep your data pipeline strong and ready to grow.

You can make a real-time MySQL pipeline for lakehouse integration by doing these steps: First, install BladePipe with Docker or Binary. Next, add MySQL and Paimon as sources in BladePipe Cloud. Then, create a Sync DataJob, test the connections, and check the setup. After that, use StarRocks to look at data and see results in Paimon.

You can learn about CDC techniques with MySQL. You can look at real-time CDC examples and try hands-on projects. The guide "A Guide to Better Data Pipelines: Tools, Types & Real-Time Use Cases" helps you learn about new tools and best ways to work in data engineering.

FAQ

What is Change Data Capture (CDC) and why do you need it?

CDC tracks every change in your MySQL database. You use CDC to move only new or updated data. This keeps your pipeline fast and your analytics fresh.

How often should you sync data from MySQL to the lakehouse?

You can set your sync to run every few minutes or hours. Real-time sync gives you the latest data. Pick a schedule that matches your business needs.

What happens if your MySQL schema changes?

You must plan for schema changes. Test all changes in a staging area first. Use tools that handle schema updates automatically. This keeps your pipeline running smoothly.

Which tool is best for beginners?

Airbyte is a good choice for beginners. You get a simple setup and many connectors. The interface guides you through each step.

How do you monitor your data pipeline?

Use tools like Grafana or PMM. Set alerts for delays or errors. Watch your pipeline daily to catch problems early.

See Also

The Role of Lakehouse in Modern Data Management Strategies

Enhancing Dataset Freshness by Integrating PowerBI with Singdata Lakehouse

Key Steps and Best Practices for Constructing a Data Pipeline

An Introductory Guide to Understanding Data Pipelines

The Efficiency of Apache Kafka in Streamlined Data Processing