The Challenge of Real-time Data Ingestion into Data Lakes

In today’s data-driven world, organizations increasingly rely on data lakes for comprehensive analytics, machine learning, and business intelligence. However, getting real-time, consistent, and reliable data into these lakes from various operational databases can be a significant challenge. Traditional batch processing often leads to stale data and complex ETL processes. This is where Change Data Capture (CDC) pipelines become indispensable, enabling incremental data synchronization with minimal impact on source systems. At SoftCrafter, we understand the critical need for efficient data strategies in modern applications, from e-commerce platforms to complex web solutions.

Introducing the Power Trio: Kafka Connect, Debezium, and dbt

To address the complexities of real-time data ingestion, we advocate for a powerful combination of technologies: Apache Kafka Connect for scalable data streaming, Debezium for robust CDC, and dbt (data build tool) for data transformation and modeling within the data lake. This trio forms a highly effective, resilient, and maintainable pipeline for keeping your data lake fresh and accurate.

Kafka Connect: The Data Integration Backbone

Kafka Connect is a framework for scalably and reliably streaming data between Apache Kafka and other systems. It’s an integral part of the Kafka ecosystem, offering pre-built connectors for a vast array of data sources and sinks. For CDC, it provides the perfect platform to run Debezium connectors.

A typical Kafka Connect worker configuration might look like this:

bootstrap.servers=kafka:9092
group.id=connect-cluster
config.storage.topic=connect-configs
offset.storage.topic=connect-offsets
status.storage.topic=connect-status
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=false
value.converter.schemas.enable=false
internal.key.converter=org.apache.kafka.connect.json.JsonConverter
internal.value.converter=org.apache.kafka.connect.json.JsonConverter
internal.key.converter.schemas.enable=false
internal.value.converter.schemas.enable=false

Debezium: The CDC Engine

Debezium is an open-source distributed platform for Change Data Capture. It provides a set of Kafka Connect connectors that monitor specific database management systems (like PostgreSQL, MySQL, SQL Server, MongoDB) and stream all row-level changes to Kafka topics. Each change record includes the operation type (insert, update, delete) and the state of the row, both before and after the change. This granular detail is crucial for maintaining data integrity in the data lake.

Here’s an example of a Debezium PostgreSQL connector configuration:

{
  "name": "postgres-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "tasks.max": "1",
    "database.hostname": "postgres",
    "database.port": "5432",
    "database.user": "debezium",
    "database.password": "dbz",
    "database.dbname": "mydb",
    "database.server.name": "my-app-db",
    "schema.include.list": "public",
    "table.include.list": "public.customers,public.orders",
    "topic.prefix": "debezium",
    "heartbeat.interval.ms": "5000",
    "snapshot.mode": "initial",
    "plugin.name": "pgoutput"
  }
}

This configuration tells Debezium to monitor the customers and orders tables in the public schema of the mydb database and stream changes to Kafka topics prefixed with debezium.

Landing Data in the Data Lake

Once data changes are streaming into Kafka, the next step is to land them efficiently into your data lake. Kafka Connect offers various sink connectors for different data lake technologies, such as S3 (for data lakes built on object storage) or HDFS. These connectors can be configured to write data in formats like Parquet or Avro, often partitioning it by time for optimized querying.

For example, using an S3 sink connector:

{
  "name": "s3-sink-connector",
  "config": {
    "connector.class": "io.confluent.connect.s3.S3SinkConnector",
    "tasks.max": "1",
    "topics": "debezium.public.customers,debezium.public.orders",
    "s3.bucket.name": "my-data-lake-bucket",
    "s3.region": "us-east-1",
    "format.class": "io.confluent.connect.s3.format.parquet.ParquetFormat",
    "storage.class": "io.confluent.connect.s3.storage.S3Storage",
    "partitioner.class": "io.confluent.connect.storage.partitioner.TimeBasedPartitioner",
    "path.format": "'year'=YYYY/'month'=MM/'day'=dd/'hour'=HH",
    "locale": "en",
    "timezone": "UTC",
    "timestamp.extractor": "RecordField",
    "timestamp.field": "_ingest_timestamp",
    "flush.size": "10000"
  }
}

dbt: Transforming and Modeling Data for Analytics

With raw CDC data landed in your data lake, dbt enters the scene to transform this raw, operational data into clean, structured, and query-ready analytical models. dbt allows data analysts and engineers to define data transformations using SQL, leveraging software engineering best practices like version control, testing, and documentation. This is where the true value of your data lake is unlocked, turning raw events into actionable insights.

dbt models can handle the complexities of CDC, such as applying updates and deletes to create a unified, current view of your data or building slowly changing dimensions. For instance, an incremental model in dbt can process new CDC records and merge them into an existing table in your data lake’s query engine (like Presto, Athena, or Snowflake).

An example dbt model to handle CDC upserts:

-- models/my_project/customers_snapshot.sql

{{ config(
    materialized='incremental',
    unique_key='id',
    incremental_strategy='merge'
)}}

SELECT
    id,
    first_name,
    last_name,
    email,
    _ingest_timestamp
FROM {{ source('raw_cdc', 'debezium_public_customers') }}

{% if is_incremental() %}
WHERE _ingest_timestamp > (SELECT MAX(_ingest_timestamp) FROM {{ this }})
{% endif %}

This dbt model incrementally processes new customer records, merging them based on the id. SoftCrafter’s expertise in corporate services often involves setting up such robust data pipelines to empower business intelligence.

Benefits of this Architecture

  • Real-time Insights: Data is available in the data lake with minimal latency, supporting timely decision-making.
  • Scalability and Reliability: Kafka Connect and Debezium are built for distributed, fault-tolerant operations.
  • Data Integrity: CDC ensures every change is captured, preventing data loss and maintaining consistency.
  • Flexibility: The decoupled architecture allows for easy integration of new data sources and analytical tools.
  • Maintainability: dbt brings engineering best practices to data transformation, making models easier to develop, test, and maintain.

By implementing this architecture, organizations can build highly efficient and scalable data ingestion pipelines, transforming their data lakes into powerful analytical engines. If you’re looking to optimize your data infrastructure, explore how SoftCrafter can assist with our software development services. Our team is ready to help you craft robust and innovative solutions. Contact us to learn more about our approach to data engineering and how we can support your business goals.

#DataLake #CDC #KafkaConnect #Debezium #dbt #DataEngineering #RealTimeData #SoftCrafter

Categorized in:

Data Engineering,

Last Update: October 25, 2026

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