Introduction
Building a high-performance operational data hub can dramatically improve the flow of data across your enterprise, enabling use cases like Customer 360, real-time analytics, and intelligent automation. In this tutorial, we walk through how to use TapData to implement a real-time data hub—from source ingestion to downstream consumption.
TapData is purpose-built for real-time data integration, with built-in CDC, schema mapping, and support for modern targets like MongoDB, Apache Doris, and real-time APIs.
Step 1: Define Your Data Hub Architecture
Before implementation, define the core data sources and consumers. A typical operational data hub scenario may include:
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Sources:
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MySQL (ERP system)
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SQL Server (CRM system)
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Oracle (billing system)
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Targets:
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MongoDB (Customer 360 document view)
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ClickHouse (real-time analytics)
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API Gateway (mobile apps)
The goal is to enable sub-second latency from source updates to target visibility.
Step 2: Configure Source Connectors with CDC
TapData supports log-based Change Data Capture (CDC) for many mainstream databases. For each source, configure a CDC connector.
Example: Configuring MySQL CDC
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Create a new MySQL connection in TapData.
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Enable binlog on the MySQL instance (binlog_format=ROW
).
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Grant necessary privileges to the TapData user.
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Create a “CDC” type sync task in the TapData console.
TapData will automatically:
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Parse DML changes (INSERT/UPDATE/DELETE)
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Map source schema to downstream target
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Track offsets for fault tolerance and retry
Repeat the same process for other sources like Oracle, PostgreSQL, or SQL Server.
Step 3: Build Real-Time Pipelines to Target Systems
With sources connected, define how the data should be routed and transformed in real-time.
Example 1: MongoDB for Unified Document Views
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Use TapData’s visual flow editor to create a data pipeline
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Configure field mapping and key structure for target MongoDB collections
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Optionally enable deduplication, type transformation, and conflict resolution
MongoDB is ideal for representing complex, nested business entities such as customers, orders, or assets in a unified document format. TapData enables you to merge multi-source records—from CRM, ERP, and service platforms—into a single real-time JSON document per entity, eliminating fragmentation and simplifying API or UI consumption.
Example 2: ClickHouse for Real-Time OLAP
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Select ClickHouse as the sync target in TapData
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Choose the appropriate merge strategy (e.g., insert or deduplicate by primary key) based on your table design
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Use TapData’s built-in type mapping and transformation engine to align source fields with ClickHouse’s column types
ClickHouse is well-suited for high-speed analytical workloads. TapData ensures seamless real-time data delivery by managing schema conversion, change tracking, and efficient batch-flush writing to ClickHouse, even under high write throughput.
Step 4: Enable Materialized Views for Real-Time Consumption
TapData supports auto-refreshed materialized views for downstream applications. These are real-time snapshots of data transformations, ideal for:
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BI dashboards
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External APIs
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AI model input pipelines
You can define transformation logic (joins, filters, enrichments) visually, and TapData keeps the result updated within milliseconds of upstream changes.
Step 5: Monitor, Scale, and Govern
TapData provides a built-in monitoring dashboard to track:
You can also:
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Enable alerting rules for failures
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Configure output frequency and batch size to avoid overloading downstream systems
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Ensure access control is applied at the API or data consumer layer to protect sensitive information
Summary: Build Once, Stream Everywhere
With TapData, building an operational data hub is no longer a months-long infrastructure project. In just a few steps, you can connect heterogeneous systems, stream data in real time, and serve up-to-date data to downstream consumers — without complex code or ETL scripts.
Whether you’re running modern SaaS, legacy ERP, or hybrid architectures, TapData helps you unify your data, instantly.
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