DEV Community: Joy Akinyi

Change Data Capture (CDC) in Data Engineering: Concepts, Tools, and Real-World Implementation Strategies

Joy Akinyi — Sun, 14 Sep 2025 14:01:37 +0000

Introduction

In today’s fast-paced data landscape, organizations need real-time insights to stay competitive. Change Data Capture (CDC) is a cornerstone of modern data engineering, enabling systems to track and propagate database changes—inserts, updates, and deletes—to downstream applications with minimal latency. Unlike batch processing, which relies on periodic data dumps, CDC streams changes as they occur, supporting use cases like real-time analytics, microservices synchronization, and cloud migrations.

According to Confluent, CDC "tracks all changes in data sources so they can be captured in destination systems, ensuring data integrity and consistency across multiple systems and environments." This is critical for scenarios like replicating operational data to a data warehouse without overloading the source database. Debezium, an open-source CDC platform, defines it as a distributed service that captures row-level changes and streams them as events to consumers, making it ideal for event-driven architectures.

This article dives into CDC concepts, explores tools like Debezium and Kafka, and walks through a real-world implementation for a crypto time-series data pipeline using Docker, PostgreSQL, Kafka, and Cassandra. We’ll also address common challenges—schema evolution, event ordering, late data, and fault tolerance—with practical solutions, drawing from official documentation and real-world configurations. By the end, you’ll have a clear blueprint for building robust CDC pipelines.

Concepts of CDC

CDC transforms database transactions into event streams, allowing applications to react to changes in near real-time. It’s particularly valuable for synchronizing data across heterogeneous systems, such as replicating a transactional database to a scalable NoSQL store for analytics.

Key Methods of CDC

CDC can be implemented through several approaches, each with distinct trade-offs:

Log-Based CDC: The most efficient method, it reads the database’s transaction log (e.g., PostgreSQL’s WAL or MySQL’s binlog) to capture changes. Logs record all operations sequentially, enabling low-latency capture with minimal impact on the source database. Debezium’s documentation highlights its effectiveness for capturing all operations, including deletes, without additional queries.
Trigger-Based CDC: Triggers are set on database tables to log changes into an "outbox" table or notify consumers directly. While simple, this adds overhead, as triggers execute SQL for each change. Confluent notes that triggers can impact write performance, making them less ideal for high-throughput systems.
Query-Based CDC: This involves polling the database for changes using timestamps or version columns. It’s straightforward but risks missing events and struggles with deletes. Redpanda recommends it only when log-based options are unavailable due to its inefficiencies.

CDC Architecture

A typical CDC pipeline includes:

Source Database: Where changes occur (e.g., PostgreSQL).
Capture Mechanism: A tool like Debezium parses logs or triggers to extract events.
Event Stream: Apache Kafka buffers and distributes events reliably.
Consumers: Downstream systems (e.g., Cassandra, data warehouses) process events.

The flow is: Changes are logged → CDC tool captures events → Events are streamed to Kafka → Consumers apply changes. For example, in a crypto pipeline, trade data is inserted into PostgreSQL, captured by Debezium, streamed via Kafka, and stored in Cassandra for scalable analytics.

Here’s a simplified architecture description:

[PostgreSQL] → [Transaction Log (WAL)] → [Debezium] → [Kafka Topics] → [Cassandra]

Tools for CDC

Several tools enable CDC, with Debezium and Kafka Connect standing out for their open-source flexibility and integration.

Debezium

Debezium is an open-source platform for log-based CDC, designed to work with Kafka. It supports connectors for databases like PostgreSQL, MySQL, and MongoDB, capturing row-level changes as events. Debezium performs an initial snapshot of the database and then streams ongoing changes, ensuring consistency and scalability.

Kafka Connect

Kafka Connect is a framework for integrating Kafka with external systems. It uses source connectors (e.g., Debezium) to capture data and sink connectors to write to targets like Cassandra. Confluent’s CDC guide emphasizes its role in simplifying CDC pipelines with managed connectors.

Real-World Implementation: Crypto Data Pipeline

To illustrate CDC, we’ll implement a pipeline for crypto time-series data, replicating trades from PostgreSQL to Cassandra via Kafka using Debezium. The setup uses Docker on an Ubuntu server, based on a real-world configuration shared by a data engineering team.

Prerequisites

Ubuntu server (e.g., 22.04 LTS).
Docker and Docker Compose installed.
Firewall allowing ports 5433, 2181, 9092, 8083, and 9042.

Step 1: Set Up the Ubuntu Server

Update the system and install Docker:

sudo apt update && sudo apt upgrade -y
sudo apt install docker.io docker-compose -y
sudo systemctl start docker
sudo systemctl enable docker
sudo usermod -aG docker $USER

Step 2: Configure Docker Services

The following docker-compose.yml orchestrates the pipeline:

version: '3.8'
services:
  postgres:
    image: debezium/postgres:16
    container_name: mydb
    environment:
      POSTGRES_USER: joy
      POSTGRES_PASSWORD: your password
      POSTGRES_DB: mydb
    command:
      - "postgres"
      - "-c"
      - "wal_level=logical"
      - "-c"
      - "max_wal_senders=1"
      - "-c"
      - "max_replication_slots=1"
    ports:
      - "5433:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data
  app:
    image: binance
    container_name: binance_app
    env_file: .env
    depends_on:
      - postgres
  zookeeper:
    image: confluentinc/cp-zookeeper:7.6.1
    container_name: zookeeper
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
    ports:
      - "2181:2181"
  kafka:
    image: confluentinc/cp-kafka:7.6.1
    container_name: kafka
    depends_on:
      - zookeeper
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
    ports:
      - "9092:9092"
  connect:
    image: debezium/connect:2.7.3.Final
    container_name: connect
    depends_on:
      - kafka
      - postgres
      - cassandra
    environment:
      BOOTSTRAP_SERVERS: kafka:9092
      GROUP_ID: 1
      CONFIG_STORAGE_TOPIC: connect-configs
      OFFSET_STORAGE_TOPIC: connect-offsets
      STATUS_STORAGE_TOPIC: connect-status
      CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: 1
      KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      CONNECT_PLUGIN_PATH: /kafka/connect,/kafka/connect/cassandra-sink,/kafka/connect/debezium-connector-postgres
      CONNECT_REPLICATION_FACTOR: 1
      CONNECT_OFFSET_FLUSH_INTERVAL_MS: 10000
    ports:
      - "8083:8083"
    volumes:
      - ./plugins:/kafka/connect
  cassandra:
    image: cassandra:5
    container_name: cassandra
    environment:
      - MAX_HEAP_SIZE=1G
      - HEAP_NEWSIZE=256M
    ports:
      - "9042:9042"
    volumes:
      - cassandra_data:/var/lib/cassandra
volumes:
  postgres_data:
  cassandra_data:

Explanation:

postgres: Uses debezium/postgres:16 with CDC enabled via wal_level=logical. The mydb database is created with user joy and password your password. Port 5433 (host) maps to 5432 (container).
app: A custom binance image (assumed to ingest crypto data into PostgreSQL tables like klines, trades).
zookeeper and kafka: Provide the event streaming backbone, with Kafka advertising on port 9092.
connect: Runs Debezium Connect to manage CDC connectors, exposing port 8083 for REST API.
cassandra: Runs Cassandra 5 for scalable storage, with port 9042 for CQL access.
restart: always: Ensures services run persistently, replacing the need for nohup.

Save this in ~/crypto-pipeline/docker-compose.yml and run:

cd ~/crypto-pipeline
docker-compose up -d

Step 3: Configure Debezium Connectors

PostgreSQL Source Connector (`postgres-source.json`)

{
  "name": "postgres-source",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "mydb",
    "database.port": "5432",
    "database.user": "joy",
    "database.password": "your password",
    "database.dbname": "mydb",
    "plugin.name": "pgoutput",
    "slot.name": "debezium_slot",
    "publication.name": "debezium_pub",
    "table.include.list": "public.klines,public.order_book,public.prices,public.ticker_24hr,public.trades",
    "topic.prefix": "dbz"
  }
}

Explanation:

Captures changes from PostgreSQL tables (klines, order_book, prices, ticker_24hr, trades) and publishes to Kafka topics (e.g., dbz.klines).
Uses pgoutput for logical decoding, with a dedicated replication slot (debezium_slot) and publication (debezium_pub).
Connects to the mydb database with user joy and password your password.

curl -X POST -H "Content-Type: application/json" --data @postgres-source.json http://localhost:8083/connectors

Cassandra Sink Connector (`cassandra-sink.json`)

{
  "name": "cassandra-sink",
  "config": {
    "connector.class": "com.datastax.oss.kafka.sink.CassandraSinkConnector",
    "tasks.max": "1",
    "topics": "dbz.public.prices,dbz.public.klines,dbz.public.order_book,dbz.public.ticker_24hr,dbz.public.trades",
    "contactPoints": "cassandra",
    "loadBalancing.localDc": "datacenter1",
    "topic.dbz.public.prices.crypto.prices.mapping": "symbol=value.symbol, price=value.price, event_time=value.event_time",
    "topic.dbz.public.klines.crypto.klines.mapping": "symbol=value.symbol, open_time=value.open_time, close_price=value.close_price, close_time=value.close_time, event_time=value.event_time, high_price=value.highPrice, low_price=value.lowPrice, open_price=value.openPrice, volume=value.volume",
    "topic.dbz.public.order_book.crypto.order_book.mapping": "symbol=value.symbol, event_time=value.event_time, side=value.side, price=value.price, qty=value.qty",
    "topic.dbz.public.ticker_24hr.crypto.ticker_24hr.mapping": "symbol=value.symbol, event_time=value.event_time, high_price=value.highPrice, last_price=value.lastPrice, low_price=value.lowPrice, price_change_percent=value.priceChangePercent, volume=value.volume",
    "topic.dbz.public.trades.crypto.trades.mapping": "id=value.id, price=value.price, qty=value.qty, quoteqty=value.quoteQty, time=value.time, isbuyermaker=value.isBuyerMaker, isbestmatch=value.isBestMatch, event_time=value.event_time",
    "transforms": "unwrap",
    "transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState"
  }
}

Explanation:

Writes data from Kafka topics (dbz.klines, etc.) to Cassandra tables in the crypto keyspace.
Connects to the cassandra service, ensuring scalable storage for crypto data.

curl -X POST -H "Content-Type: application/json" --data @cassandra-sink.json http://localhost:8083/connectors

Step 4: Test the Pipeline

Populate PostgreSQL: Connect to the database:

   docker exec -it mydb psql -U joy -d mydb

Create a table and insert data:

   CREATE TABLE public.trades (id SERIAL PRIMARY KEY, symbol VARCHAR(10), price DECIMAL, timestamp TIMESTAMP);
   ALTER TABLE public.trades REPLICA IDENTITY FULL;
   INSERT INTO public.trades (symbol, price, timestamp) VALUES ('BTCUSDT', 50000.00, '2025-09-13 11:35:00');
   SELECT * FROM public.trades;

Exit with \q.

Verify Cassandra: Check replicated data:

   docker exec -it cassandra cqlsh

Query the trades table:

   DESCRIBE KEYSPACE crypto;
   SELECT * FROM crypto.trades;

Exit with exit.

The binance app inserts data into PostgreSQL, Debezium captures changes, Kafka streams them, and the sink connector writes to Cassandra.

Alternatively, you can automate the python script that fetches data from binance at a defined interval say 5mins using the BlockingScheduler library.

Challenges & Solutions

Building robust CDC pipelines involves addressing several challenges.

Schema Evolution

Challenge: Schema changes (e.g., adding/dropping columns) can break consumers. Decodable notes that forward-compatible changes (adding optional columns) allow old consumers to ignore new fields, while backward-compatible changes (dropping optional columns) ensure new consumers handle old data.

Solutions:

Use a schema registry (e.g., Confluent Schema Registry) to enforce compatibility.

Event Ordering

Challenge: Incorrect event order can lead to inconsistencies, especially across distributed systems so maintaining the original transactional order matters.

Solutions:

Kafka guarantees order within partitions. Use key-based partitioning (e.g., by symbol in the crypto pipeline) to ensure related events stay ordered.
Debezium groups events by transaction for consistency. OLake recommends idempotent consumers to handle occasional out-of-order events.

Late Data

Challenge: Late-arriving events can disrupt aggregates or state in real-time analytics.

Solutions:

Use watermarking in stream processors like Apache Flink to define lateness thresholds and design sinks to be idempotent(applying the same event twice has the same effect as applying it once) and able to accept corrections (e.g., update rows with newer timestamps).
Buffer events in Kafka for replay. Confluent advocates event-time processing to handle delays accurately.

Fault Tolerance

Challenge: Failures in connectors or networks can cause data loss or duplicates.

Solutions:

Debezium provides at-least-once delivery; use idempotent sinks (e.g., Cassandra’s upsert) to deduplicate.
Kafka’s replication ensures durability. Configure high availability for PostgreSQL to prevent replication slot buildup. OLake suggests monitoring with Prometheus for proactive fault detection.

Conclusion

CDC is a game-changer for real-time data engineering, enabling seamless synchronization across systems. Using Debezium, Kafka, and connectors, our crypto pipeline demonstrates how to replicate data from PostgreSQL to Cassandra efficiently. By addressing schema evolution, ordering, late data, and fault tolerance, engineers can build reliable pipelines. As data demands grow, CDC will remain a critical tool for agile, data-driven organizations.

References:

Deploying Anaconda with JupyterLab on an Azure VM for Team Collaboration

Joy Akinyi — Tue, 26 Aug 2025 14:24:04 +0000

Introduction

Anaconda is an open-source distribution platform that bundles Python, the conda package manager, and essential libraries like NumPy, pandas, and scikit-learn. It streamlines environment management and ensures consistency across team projects. By deploying Anaconda with JupyterLab on an Azure Virtual Machine (VM) running Ubuntu, teams can create a cloud-based, collaborative workspace.

This guide walks you through setting up an Azure VM with Ubuntu, installing Anaconda, configuring JupyterLab for team access on port 8888, and testing the setup with a sample project.

Prerequisites

An Azure subscription (sign up for a free account at azure.microsoft.com/free) or better still, a student account
Familiarity with Linux terminal commands.
Sudo privileges on the VM.

Step 1: Set Up an Azure VM with Ubuntu

Log in to the Azure portal at portal.azure.com. or use a student account to log in.
Navigate to Virtual machines under Services and select Create > Virtual machine.
In the Basics tab:
- Choose your subscription.
- Create a new resource group (e.g., myResourceGroup).
- Name the VM (e.g., myVM).
- Select a region close to your team for low latency.
- Choose Ubuntu Server 22.04 LTS - Gen2 as the Image.
Under Administrator account:
- Select SSH public key or Password for authentication.
- Set a username (e.g., azureuser).
- Provide an SSH key or password as needed.
In Inbound port rules, allow SSH (22) and Custom TCP (8888) for JupyterLab access.
- To allow port 8888:
  - After VM creation, go to the VM’s Networking tab in the Azure portal.
  - Click Add inbound port rule.
  - Set Service to Custom, Port ranges to 8888, Protocol to TCP, and Action to Allow.
  - For security, restrict Source to your team’s IP ranges.
Review and create the VM, saving the SSH key if generated.
Note the public IP address from the VM’s overview page.

Connect to the VM via SSH: ssh azureuser@<public-ip> (add -i path/to/key.pem for key-based authentication).

Step 2: Install Anaconda on the Ubuntu VM

Update the system:

   sudo apt update && sudo apt upgrade -y

Install required utilities:

   sudo apt install wget curl git -y

Download the latest Anaconda installer:

   wget https://repo.anaconda.com/archive/Anaconda3-2025.06-1-Linux-x86_64.sh

Verify the installer (optional):

   sha256sum Anaconda3-2025.06-1-Linux-x86_64.sh

Compare the checksum with the official value from Anaconda’s website.

Run the installer:

   #Default is ~/anaconda3, but you can change it to /opt/anaconda3 for system-wide use.
   bash Anaconda3-2025.06-1-Linux-x86_64.sh

Accept the license and install in a shared location like /opt/anaconda3 for team access.

Set permissions:

   #ensures the 'users' group has control of Anaconda’s directory.
   sudo chown -R :users /opt/anaconda3
   #allows all users in the 'users' group to install/update packages without sudo
   sudo chmod -R g+w /opt/anaconda3

Initialize conda:

    #Initialize conda for your shell
   /opt/anaconda3/bin/conda init
    # Reload your shell configuration file
   source ~/.bashrc

Verify the installation:

   conda --version
   python --version

Create a shared conda environment:

   conda create --name teamenv python=3.11
   conda activate teamenv

Step 3: Configure a Secure JupyterLab Server

JupyterLab is configured on port 8888 with a password to secure access, ensuring only authorized team members can log in.

Install JupyterLab:

conda activate teamenv
conda install jupyterlab

Generate a configuration file:
```
jupyter lab --generate-config
```
Set a password to secure the server:
```
from jupyter_server.auth import passwd
passwd()
```
Enter a password (e.g., mysecurepassword) and copy the sha1:... hash. This password is critical to prevent unauthorized access to http://<public-ip>:8888.
Edit ~/.jupyter/jupyter_lab_config.py:

   c.ServerApp.ip = '0.0.0.0'  # Allow access from any IP
   c.ServerApp.port = 8888     # Default port
    c.ServerApp.open_browser = False
   c.ServerApp.password = 'sha1:<hashed-password>'  # Paste the hashed password
   c.ServerApp.allow_remote_access = True
   c.ServerApp.root_dir = '/opt/shared_notebooks'  # Shared directory

Create a shared notebook directory:

   #create a directory
   sudo mkdir /opt/shared_notebooks
   # give ownership to the users group
   sudo chown azureuser:users /opt/shared_notebooks
   # give groups write rights in the notebook
   sudo chmod g+w /opt/shared_notebooks

Note: If you want all users who run JupyterLab to access this folder, you need to make sure they’re added to the users group.:

sudo usermod -aG users <username>

Start JupyterLab in the background:

   conda activate teamenv
   nohup jupyter lab

The nohup command ensures JupyterLab continues running after you exit the SSH session, maintaining access at http://:8888.
Access JupyterLab at http://<public-ip>:8888 and log in with the password. Use HTTPS if configured.

Collaboration Notes:

Users share the teamenv environment and /opt/shared_notebooks.
Add team members to the users group (e.g., sudo adduser teamuser1; sudo usermod -aG users teamuser1) and share the password securely.
Avoid conflicts by using Git or subdirectories in /opt/shared_notebooks.

Step 4: Test with a Mini Project

Test with a JupyterLab notebook fetching cryptocurrency data from CoinGecko.

Install dependencies:

   conda activate teamenv
   conda install requests pandas

Create a notebook in /opt/shared_notebooks and add:



   import requests
   import pandas as pd
   from datetime import datetime

   url = "https://api.coingecko.com/api/v3/coins/markets"
   params = {
       "vs_currency": "usd",
       "ids": "bitcoin,ethereum,cardano,solana",
       "order": "market_cap_desc",
       "per_page": 10,
       "page": 1,
       "sparkline": False
   }

   response = requests.get(url, params=params)
   if response.status_code == 200:
       data = response.json()
       df = pd.DataFrame(data, columns=["id", "symbol", "current_price", "market_cap", "total_volume"])
       df["timestamp"] = datetime.now()
       display(df)
   else:
       print("Error fetching data:", response.status_code)

Run and share the notebook via /opt/shared_notebooks or Git.

Getting Started with Docker and Docker Compose: A Beginner’s Guide

Joy Akinyi — Sun, 24 Aug 2025 08:31:18 +0000

Have you ever heard the phrase “But it works on my machine”?
This is one of the most common problems developers face when moving applications from their local computer to a server or sharing projects with teammates.

That’s where Docker comes in. Docker allows you to package your application with all its dependencies into a container so that it can run consistently on any environment say your laptop, a server, or even the cloud.

In this guide, we’ll cover the basics of Docker and introduce Docker Compose, a tool that helps you run multi-container applications with ease.

What is Docker?
Docker is an open source platform that allows you to run applications in isolated environments through containerization. Unlike Virtual Environments that emulate entire operating systems,docker containers share the host OS but isolate applications. They are also lightweight and fast.

Installing Docker:

You can approach this by either:

Installing Docker Desktop

Docker Desktop provides an easy-to-use application with a GUI and the Docker CLI preinstalled.It also integrates well with WSL 2 on Windows.

👉 Download here

Once installed, you’ll be able to run Docker commands directly from your terminal (PowerShell, WSL, or macOS terminal).

2.Installing the Docker CLI on Linux:
If you’re running Linux, you can install the Docker CLI directly.

For example, on WSL 2 with Ubuntu 22.04,you can follow this steps:
Installing Docker on WSL 2 with Ubuntu 22.04

After installation,you can verify if Docker is working by running:
docker version

If it's working, you should see the following output:

Client: Docker Engine - Community
 Version:           27.0.2
 API version:       1.46
 Go version:        go1.21.5
 Git commit:        3ab9da9
 Built:             Tue Jul 18 17:45:00 2024
 OS/Arch:           linux/amd64
 Context:           default

Server: Docker Engine - Community
 Engine:
  Version:          27.0.2
  API version:      1.46 (minimum version 1.12)
  Go version:       go1.21.5
  Git commit:       3ab9da9
  Built:            Tue Jul 18 17:45:00 2024
  OS/Arch:          linux/amd64
  Experimental:     false

Basic Docker Concepts:

Image → A blueprint for your application.
Container → A running instance of an image.
Dockerfile → A recipe to build an image.
Docker Hub → Public repository of prebuilt images.

Now,let's talk about building and running your first container;
You can run an already existing image by running
docker run hello-worldIn this case,docker pulls the image from Docker Hub, creates a container, runs it, then exits.

For listing and stopping containers,you can run:

docker ps # running containers docker ps -a # all containers (even stopped ones) docker stop <container_id> docker rm <container_id>

Running your first Dockerfile:

A Docker file as mentioned before is like a recipe for building images
An example of one is:

## Use official Python image
FROM python:3.13-slim

## Set working directory
WORKDIR /app

## Copies the .txt file to the set working directory
COPY requirements.txt .

## Runs the requirements file
RUN pip install -r requirements.txt

## Copy local files into the container
COPY . .

## Run main
CMD ["python","main.py"]

Now that we have a docker file,we can build an image say image1 using:
docker build -t image1 .
Next is to run the container by running:
docker run image1This will run a container based on the image you just created

So far, we’ve learned how to run one container at a time. But in real-world projects, applications often rely on multiple containers running together — for example, a web server and a database. Managing these individually can get complicated, and that’s where Docker Compose comes in

Getting Started with Docker Compose

Docker Compose is a tool that lets you define and run multi-container applications using a single configuration file called docker-compose.yml.

Instead of starting containers one by one, you define all your services in YAML and start them together with one command. A sample docker-compose.yml file is:

version: "3.8"

services:
  web:
    build: .
    ports:
      - "5000:5000"
    depends_on:
      - db

  db:
    image: postgres:15
    restart: always
    environment:
      POSTGRES_USER: example
      POSTGRES_PASSWORD: example
      POSTGRES_DB: testdb
    ports:
      - "5432:5432"

Running with compose:
To build and start all services, run
docker-compose up
To stop everything,run
docker-compose down

In conclusion,With Docker Compose, you don’t have to remember long docker run commands as everything is in one file and also it super easy to share projects.

Why we use Apache Airflow for Data Engineering

Joy Akinyi — Mon, 21 Jul 2025 14:19:28 +0000

Goal: To explain the value of Apache Airflow in building, scheduling, and managing workflows in Data Engineering.

Definitions:
Apache Airflow

Open source platform used to schedule and manage batch oriented workflows.

Data Engineering

Data Engineering involves designing and managing data pipelines by extracting, transforming, and loading data (ETL) hence prepares datasets for analysis.

Orchestration tools like Airflow in data engineering are important especially when it comes to automation, optimization and the execution of data workflows that involve multiple dependent tasks across systems.

Key Components of the Airflow Architecture

Directed Acyclic Graphs(DAG's):A DAG is basically code written in python that defines the sequence of tasks needed to execute a workflow.
Scheduler: Triggers scheduled workflows and submitting tasks to executor
Executor: Runs the tasks e.g LocalExecutor
Web server: Provides a user interface (UI) to inspect, trigger and debug DAGs’ behaviours and tasks
Metadata Database: Used by the scheduler, executor and webserver to store state

The following image represents the structure of Apache Airflow

Also,to effectively design and manage workflows, Apache Airflow uses tasks and operators as core components.

A task is the basic unit of execution in Airflow and each task represents an action like running a python function or executing a sql script.
An operator defines the kind of tasks you want to execute e.g
PythonOperator that executes a python function
BashOperator that runs a Bash command or script
PostgresOperator that executes SQL commands on a Postgres database

With the knowledge above,we can give reasons why Data Engineers use Airflow:

Modular & Scalable Workflow Management:

Python‑based DAG definitions let you build reusable and maintainable modules for pipelines.
Scalable means your workflows can handle more tasks or data without breaking or needing major redesign e.g through parallelization where multiple tasks can run at once.

2.Easy Debugging :

Detailed logs per task in the UI plus retry mechanisms and alerting making debugging robust.

3.Supports Dynamic Pipelines:

Instead of hardcoding every task, you can use loops, conditions, and variables to create tasks using python.

4.Integration with External Systems

Extensive integration with various external systems, databases, and cloud platforms like GCP,Azure and AWS hence ideal in organisations with diverse systems.This proves it's versatility.

Also,Workflow Dependencies are Explicit meaning that you declare dependencies clearly (with >>, << or task dependencies) ensuring correct execution order.

DEV Community: Joy Akinyi

Change Data Capture (CDC) in Data Engineering: Concepts, Tools, and Real-World Implementation Strategies

Introduction

Concepts of CDC

Key Methods of CDC

CDC Architecture

Tools for CDC

Debezium

Kafka Connect

Real-World Implementation: Crypto Data Pipeline

Prerequisites

Step 1: Set Up the Ubuntu Server

Step 2: Configure Docker Services

Step 3: Configure Debezium Connectors

PostgreSQL Source Connector (postgres-source.json)

Cassandra Sink Connector (cassandra-sink.json)

Step 4: Test the Pipeline

Challenges & Solutions

Schema Evolution

Event Ordering

Late Data

Fault Tolerance

Conclusion

Deploying Anaconda with JupyterLab on an Azure VM for Team Collaboration

Introduction

Prerequisites

Step 1: Set Up an Azure VM with Ubuntu

Step 2: Install Anaconda on the Ubuntu VM

Step 3: Configure a Secure JupyterLab Server

Step 4: Test with a Mini Project

Getting Started with Docker and Docker Compose: A Beginner’s Guide

Installing Docker:

Basic Docker Concepts:

Running your first Dockerfile:

Getting Started with Docker Compose

Why we use Apache Airflow for Data Engineering

PostgreSQL Source Connector (`postgres-source.json`)

Cassandra Sink Connector (`cassandra-sink.json`)