DEV Community: Lakshmi Narayana Rasalay

Building Real-Time Data Pipelines from PostgreSQL Using Flink CDC

Lakshmi Narayana Rasalay — Sat, 04 Oct 2025 22:56:58 +0000

Overview
In today's data-driven world, businesses require timely insights to make informed decisions. Traditional batch processing methods often fall short in providing real-time analytics. Enter Change Data Capture (CDC) with Apache Flink—a powerful combination that enables the continuous streaming of database changes. This article delves into building real-time data pipelines from PostgreSQL using Flink CDC, highlighting its advantages, challenges, and best practices.
We’ll capture inserts, updates, and deletes from PostgreSQL and stream them to a sink system like Kafka or it could be integrated with other services like OpenSearch or Delta Lakes

What Is Flink CDC?
Apache Flink is a distributed stream processing framework renowned for its scalability and fault tolerance. Flink CDC (Change Data Capture) extends Flink's capabilities by allowing it to capture and stream real-time changes from databases like PostgreSQL. This means any insert, update, or delete operation in the database can be immediately reflected in downstream systems, ensuring up-to-date data across applications.

Why Use Flink CDC with PostgreSQL?
PostgreSQL, a popular relational database, doesn't natively support real-time data streaming. Flink CDC bridges this gap by:
Real-time Data Streaming: Capturing database changes as they happen.

Fault Tolerant: Ensuring data consistency even in the event of failures.

Scalable: Handling large volumes of data efficiently.

Schema Evolution Handling: Adapting to changes in database schema without disrupting the pipeline.

Common Use Cases
Data Warehousing: Continuously syncing transactional data from PostgreSQL to data lakes or warehouses like Apache Iceberg or Hudi.

Microservices Communication: Propagating database changes to other microservices in real-time.

Analytics: Feeding real-time data into analytics platforms for up-to-date reporting.

Here is the high level Architecture flow :

Building a Real-Time Data Pipeline: A Step-by-Step Guide
Prerequisites
Docker / Docker Compose (recommended)

PostgreSQL 10+

Apache Flink 1.16+ or Flink SQL CLI

Optional: Kafka (for sink)

Java 8/11+ and Maven (if using Java API)

Step 1: Enable Logical Replication in PostgreSQL
Flink CDC requires logical replication to read changes from the WAL (Write-Ahead Log).
This is a crucial step, if you are using AWS - update the Parameter groups with the following settings.
1.1 Update postgresql.conf
ini
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wal_level = logical
max_replication_slots = 10
max_wal_senders = 10

1.2 Update pg_hba.conf
ini
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host replication cdc_user 0.0.0.0/0 md5

1.3 Create CDC User
sql
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CREATE ROLE cdc_user WITH REPLICATION LOGIN PASSWORD 'cdc_pass';
GRANT SELECT ON ALL TABLES IN SCHEMA public TO cdc_user;

Note: Restart PostgreSQL after configuration changes.

Step 2: Start Apache Flink and Optional Kafka via Docker
Create a docker-compose.yml with the following services:
PostgreSQL (with CDC enabled)

Apache Flink

Optional: Kafka + Zookeeper (if you want to use Kafka as a sink)

Here is the sample code:

version: '3.8'
services:
db:
image: postgres
restart: always
environment:
POSTGRES_USER: postgres
POSTGRES_PASSWORD: postgres
POSTGRES_DB: jsonforms
ports:
- '5432:5432'
volumes:
- pgdata:/var/lib/postgresql/data

backend:
build: ./backend
ports:
- '3000:3000'
depends_on:
- db
environment:
DB_HOST: db

frontend:
build: ./frontend
ports:
- '4200:80'
depends_on:
- backend

volumes:
pgdata:

Step 3: Create a PostgreSQL Table with Sample Data
sql
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CREATE TABLE customers (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(100),
updated_at TIMESTAMP DEFAULT now()
);

INSERT INTO customers (name, email) VALUES
('Alice', 'alice@example.com'),
('Bob', 'bob@example.com');

Step 4: Add Flink CDC Connector to Your Flink Project
Option 1: Flink SQL
Download the Flink PostgreSQL CDC JAR from Ververica Maven repo and place it in Flink’s /lib directory. (Its already added as a mount to Docker Compose)
Option 2: Java (Maven Dependency)
xml
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com.ververica
flink-connector-postgres-cdc
3.0.1

🔧 Step 5: Define the PostgreSQL Source in Flink SQL
sql
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CREATE TABLE postgres_source (
id INT,
name STRING,
email STRING,
updated_at TIMESTAMP(3),
PRIMARY KEY (id) NOT ENFORCED
) WITH (
'connector' = 'postgres-cdc',
'hostname' = 'postgres',
'port' = '5432',
'username' = 'cdc_user',
'password' = 'cdc_pass',
'database-name' = 'mydb',
'schema-name' = 'public',
'table-name' = 'customers'
);

Step 6: Define the Sink
Option 1: Print to Console
sql
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CREATE TABLE print_sink (
id INT,
name STRING,
email STRING,
updated_at TIMESTAMP(3)
) WITH (
'connector' = 'print'
);

Option 2: Kafka Sink (Optional)
sql
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CREATE TABLE kafka_sink (
id INT,
name STRING,
email STRING,
updated_at TIMESTAMP(3)
) WITH (
'connector' = 'kafka',
'topic' = 'customer_changes',
'properties.bootstrap.servers' = 'kafka:9092',
'format' = 'json'
);

Step 7: Start the Flink Job
Run the SQL insert:
sql
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INSERT INTO print_sink
SELECT * FROM postgres_source;

Or to Kafka:
sql
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INSERT INTO kafka_sink
SELECT * FROM postgres_source;

🔁 Step 8: Test Real-Time Streaming
Insert a new row into PostgreSQL:
sql
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INSERT INTO customers (name, email) VALUES ('Charlie', 'charlie@example.com');

You should see the new change printed in Flink logs or appear in Kafka!

Optional: Flink CDC with Java DataStream API
If you want to build your own Java app:
java
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PostgreSQLSource source = PostgreSQLSource.builder()
.hostname("localhost")
.port(5432)
.username("cdc_user")
.password("cdc_pass")
.database("mydb")
.schemaList("public")
.tableList("public.customers")
.decodingPluginName("pgoutput")
.deserializer(new JsonDebeziumDeserializationSchema())
.build();

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

env.fromSource(source, WatermarkStrategy.noWatermarks(), "PostgresSource")
.print();

env.execute();

Challenges and Pain Points
While Flink CDC offers robust features, there are challenges to consider:
Schema Changes: Handling changes in the database schema (like adding or removing columns) can be complex. Flink CDC provides mechanisms to detect and adapt to these changes, but careful planning is required.

Latency: Ensuring low-latency data processing can be challenging, especially when dealing with large volumes of data.

Resource Management: Efficiently managing resources to handle high-throughput data streams without overloading the system.

Alternatives to Flink CDC
While Flink CDC is a powerful tool, it's essential to consider alternatives based on specific requirements:
Debezium: An open-source CDC platform that integrates with Kafka, suitable for microservices architectures.

Apache Kafka Connect: Provides connectors for various databases, including PostgreSQL, to stream data into Kafka topics.

AWS DMS (Database Migration Service): A managed service that supports real-time data replication across databases.

Each of these tools has its strengths and is better suited for different scenarios.
Best Practices
To ensure a successful implementation of Flink CDC with PostgreSQL:
Monitor Lag: Regularly monitor the lag between the source database and the Flink job to detect potential issues.

Handle Failures Gracefully: Implement retry mechanisms and ensure idempotent processing to handle transient failures.

Optimize Performance: Tune Flink's checkpointing and parallelism settings to balance performance and fault tolerance.

Real-World Use Cases
Sync PostgreSQL changes to a Kafka event bus

Feed real-time dashboards from operational data

Create CDC-based data lake ingestion to S3, Iceberg, or Delta Lake

Trigger downstream event-driven microservices

Summary
Building real-time data pipelines from PostgreSQL using Flink CDC enables businesses to process and analyze data as it arrives, leading to timely insights and informed decision-making. While there are challenges to consider, the benefits of real-time data streaming make it a compelling choice for modern data architectures.

Scaling Micro Frontends in Large Teams: A Comprehensive Guide

Lakshmi Narayana Rasalay — Sun, 08 Jun 2025 02:42:58 +0000

Introduction

As enterprises scale their digital ecosystems, the complexity of managing large frontend applications increases exponentially. Traditional monolithic frontends become bottlenecks, making deployments risky, slowing down development velocity, and introducing cross-team dependencies that are hard to manage. Micro Frontends (MFEs) offer a solution to this challenge by enabling teams to develop, test, deploy, and operate parts of a UI independently.

However, adopting MFEs in large teams (50+ developers across multiple domains) introduces its own set of challenges. This article provides a comprehensive guide to scaling micro frontends in large teams effectively.

Define Clear Domain Boundaries

Preventing overlap and confusion among teams starts with clear ownership and separation. It's essential to divide the application based on business capabilities such as Checkout, User Profile, or Dashboard. Where feasible, align frontend boundaries with backend microservices to enable smoother integration and development flow. Additionally, minimizing shared state and cross-MFE communication is crucial to maintaining autonomy between teams and ensuring scalability.

Select the Right Integration Strategy

Choosing the right strategy for integrating micro frontends is foundational. Options include build-time integration using tools like Nx or Lerna, run-time integration using dynamic loading mechanisms such as Webpack Module Federation, Single-SPA, or SystemJS, server-side composition that assembles pages using Edge Side Includes or SSR frameworks, and client-side composition that dynamically loads MFEs via shell apps.

Popular frameworks that support these strategies include Single-SPA, which enables combining multiple frameworks such as React, Angular, and Vue in a single app. Module Federation (Webpack 5) allows dynamic imports of remote modules at runtime, providing flexibility. Angular Elements lets Angular components be exported as native web components. Frameworks like Bit.dev and Piral further support component sharing and orchestration.

For most large teams, Webpack Module Federation offers flexibility and independence. For managing multiple frameworks and lifecycle hooks, Single-SPA is effective. SEO-sensitive or performance-critical applications benefit from SSR or edge composition strategies.

Isolate Runtime and Dependencies

Avoiding version conflicts and ensuring runtime stability is key in micro frontend systems. Best practices include using namespacing techniques for CSS (such as CSS Modules or Shadow DOM), avoiding global variables or shared window state, pinning shared dependencies to specific versions, and loading boundaries asynchronously to maintain clean dependency graphs.

Enable Independent CI/CD Pipelines

Faster iteration and deployment cycles, along with minimizing the impact of failures, are achieved through independent CI/CD pipelines. Each MFE should maintain its own pipeline using tools like GitHub Actions or CircleCI. Automating semantic versioning and artifact publishing ensures traceability. Bundles should be hosted centrally via a CDN or artifact repository, and dynamic routing or feature flags can be used to roll out features progressively.

Standardize Inter-MFE Communication

Maintaining loose coupling is essential for the independence of MFEs. Inter-MFE communication should be standardized using a shared event bus or pub/sub system. Clearly defined interfaces using TypeScript contracts or JSON Schemas facilitate communication while preventing tight binding. Avoiding direct method calls and instead relying on URL routing or custom DOM events further strengthens decoupling.

Shared Libraries and Design Systems

To maintain UI and UX consistency, teams should invest in shared libraries and design systems. These should be versioned and hosted as npm packages or remote modules. Visual consistency can be ensured using tools like Storybook, and a governance process should be in place for managing shared components. Design tokens are helpful in managing theming and branding.

Leveraging Web Components enhances interoperability across frameworks. They provide encapsulation, support custom elements, and allow for reuse across multiple applications regardless of the frontend stack. This framework-agnostic integration enables teams using Angular, React, or Vue to collaborate effectively. Tools such as Stencil, LitElement, and Angular Elements simplify the process of creating and sharing reusable components.

Performance Optimization

To ensure smooth user experiences, MFEs must be optimized for performance. This includes lazy-loading based on routes or user interaction, utilizing chunking and tree-shaking to reduce bundle sizes, and minimizing shared dependencies. Performance metrics like Time-to-Interactive (TTI) and First Input Delay (FID) should be regularly monitored to catch regressions early.

Robust Testing Strategies

Robust testing is necessary to maintain system integrity. A multi-layered approach is ideal: unit testing for individual components, contract testing to validate interfaces between MFEs and host apps, and end-to-end testing to ensure the complete flow works as expected. Tools such as Jest, Cypress, Pact, and Playwright are commonly used in MFE ecosystems.

Error Handling and Observability

In distributed UIs, fault isolation becomes critical. Each MFE should implement error boundaries to prevent failures from cascading. Logs and metrics should be centralized with context-rich metadata to make debugging efficient. Tracking performance and errors per MFE using observability tools like Sentry, Datadog, or New Relic is highly recommended.

Governance and Team Collaboration

Governance ensures that the architectural vision remains consistent as teams scale. Establish a central architecture guild to define and evolve standards. Introducing changes should follow an RFC (Request for Comments) process, and documentation-first development should be enforced. Regular cross-team sync-ups foster collaboration, knowledge sharing, and alignment.

Conclusion

Scaling micro frontends in large teams is not just a technical challenge, but also an organizational one. Success depends on clearly defined boundaries, independent pipelines, standardized communication, and a culture of ownership. With these best practices, MFEs can become a cornerstone for agility and scalability in enterprise frontend architecture.

Building a multi tenancy platform using Camunda BPM

Lakshmi Narayana Rasalay — Tue, 27 May 2025 04:54:33 +0000

Camunda is an open-source workflow and decision automation platform for the modeling, execution, and monitoring of business processes following BPMN (Business Process Model and Notation), DMN (Decision Model and Notation), and CMMN. It is used for the orchestration of complex workflows among microservices, APIs, human tasks, and external systems. Camunda is embedded by developers into their applications to automate business logic, monitor process state, and bring operational transparency.

Camunda enables companies to orchestrate processes across people, systems, and devices to tame complexity continuously and drive efficiency. A common visual language enables business and IT teams to collaborate seamlessly in designing, automating, and optimizing end-to-end processes with the speed, scale, and resilience required to compete.

When companies scale their businesses, it becomes a requirement to support multiple business units, customers, or partners on the same infrastructure. This is where multi-tenancy is used. A multi-tenant architecture allows you to efficiently host multiple isolated tenants (e.g., customers, departments, or business domains) in one deployment. In workflow automation and process orchestration, Camunda BPM offers a powerful and flexible platform to build such platforms.
This article explains how to establish a multi-tenant workflow platform using Camunda BPM, including architectural strategies, isolation levels, implementation options, and best practices.

Multi-Tenancy Approaches in Camunda
Camunda BPM (particularly in version 7 and Camunda 8/Zeebe) offers flexibility to implement different levels of isolation. Here is the one of the main approach:
Shared Engine, Logical Isolation (Soft Multi-Tenancy)

Within a Shared Engine with Logical Isolation deployment, a single Camunda process engine instance is shared by several tenants, which are logically separated depending on identifiers like tenantId, custom variables, or HTTP headers. Even if the engine is shared, processes, data, and access of users are isolated at the application level in order to preserve tenant-specific visibility and control. This form is cheaper and simpler to manage than having a separate engine per tenant but must be well-designed to prevent cross-tenant data access or privilege escalation.

With the implementation of cloud technologies, Camunda can be deployed on AWS via a number of cloud-native patterns that support scalability, cost-effectiveness, and ease of use. The most common approach is to execute the Camunda engine as a containerized application on AWS Fargate or ECS in order to gain smooth scalability and minimal infrastructure management. Here, asynchronous or long-running tasks can be coded as AWS Lambda functions, which provide an on-demand scaling serverless execution layer. Amazon API Gateway is used for API requests to initiate or check workflows, and multi-tenant authentication and scoping are handled by AWS Cognito or an identity provider. For persistence, Amazon Aurora Serverless or Amazon DynamoDB could be utilized to save the execution data and tenant-specific metadata with flexibility and high availability.

To achieve resiliency on a worldwide basis, a multi-region deployment approach can be adopted. In this case, Camunda can be deployed across two or more AWS regions (such as us-east-1 and eu-west-1), with active-active or active-passive strategies:

Active-active: The Camunda engine runs in parallel in various regions, and requests are routed through Amazon Route 53 and global load balancers. Stateful elements (e.g., databases) use cross-region replication with Aurora Global Database or DynamoDB Global Tables and near real-time data synchronization.

Active-passive: All traffic is processed by the master region and the slave region is in standby mode. During regional failure, infrastructure gets automatically upgraded using Route 53 failover policies and infrastructure-as-code tools like Terraform or AWS CloudFormation to be fully functional again.

Camunda supports effective state management from DB which makes the resiliency work in modern cloud technologies.

Under this platform model, the Shared Responsibility Model becomes crucial:

Camunda (the platform team) is responsible for the shared process engine runtime consistency and reliability. Also creation of tenants and ensuring right granular access are granted to the underlying resources is the platform's responsibility.

The application team also has the responsibility to support tenant-conscious logic, enforce access restrictions, data segregation, and ensure workflows, APIs, and user interactions stay scoping to the correct tenant securely.

Tenants require strict boundaries and strict validation in code, APIs, and external task handlers to make certain that they keep tenant isolation and meet compliance or business integrity demands.
Challenges and how to overcome them in this model:
Versioning and upgrading the shared Camunda engine or process models can pose risks to all tenants if not managed via blue/green or canary deployment strategies.
Make sure blue/green deployments are enabled for Camunda version upgrades and corresponding DB scripts are executed based on camunda’s release notes
Ensure the models are version controlled and built for backward compatible ways to enable old process instances to continue to work without any breaking changes.
Observability across the entire system is another concern, as monitoring needs to cover distributed AWS services, Camunda logs, and workflow traces, often requiring unified dashboards and tracing tools like Otel and Application logs. Enable Tenant’s dashboards for easy troubleshooting and monitoring
Enable self servicing capabilities to eliminate the burden on platform teams and enable smooth onboarding tenant experience. Have good documentation on enabling tenants to leverage the platform effectively

This approach unlocks the possibility of reusing platforms and delivering business process solutions at enterprise-level commitments—without compromising performance, cost savings, or dependability.