How to Ingest Real-Time Data into Snowflake Using CockroachDB and Redpanda Connect

In today’s data-driven world, real-time data ingestion is crucial for businesses to make informed decisions quickly. Snowflake, with its powerful data warehousing capabilities, provides a robust platform for analyzing large volumes of data. However, integrating real-time data into Snowflake can be challenging, especially when dealing with distributed databases like CockroachDB and event streaming platforms like Redpanda. This article will walk you through the process of ingesting real-time data from CockroachDB into Snowflake using Redpanda Connect.

Introduction to the Technologies

Before diving into the implementation, let’s briefly introduce the three main technologies involved:

CockroachDB

CockroachDB is a distributed SQL database that provides strong consistency and is designed to scale horizontally. It is ideal for applications that require high availability and resilience. CockroachDB supports standard SQL and is compatible with PostgreSQL, making it an excellent choice for modern, distributed applications.

Redpanda

Redpanda is a modern streaming platform that is fully compatible with Kafka APIs but is built from the ground up for better performance and lower latency. Redpanda can handle high-throughput data streams and is optimized for real-time data processing, making it an ideal choice for event-driven architectures.

Snowflake

Snowflake is a cloud-based data warehousing solution known for its scalability, performance, and ability to handle both structured and semi-structured data. It enables businesses to perform complex queries and analytics on vast amounts of data without worrying about the underlying infrastructure.

Prerequisites

Before we proceed, ensure you have the following:

CockroachDB: A running instance of CockroachDB.
Redpanda: A running Redpanda cluster with Redpanda Connect enabled.
Snowflake Account: Access to a Snowflake account with the necessary privileges to create databases, schemas, and tables.
Docker: Installed Docker for setting up Redpanda Connect.

Setting Up CockroachDB

To start, you need to have a CockroachDB cluster running. For simplicity, this example assumes a single-node CockroachDB setup. You can start a CockroachDB instance locally using Docker:

bash

docker run -d \

--name=cockroachdb \

--hostname=cockroachdb \

-p 26257:26257 -p 8080:8080 \

cockroachdb/cockroach:v22.2.0 start-single-node \

--insecure

Once the CockroachDB instance is up and running, you can access the SQL shell:

bash

docker exec -it cockroachdb cockroach sql --insecure

Next, create a database and a sample table:

sql

CREATE DATABASE sales_db;

USE sales_db;

CREATE TABLE orders (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
customer_id UUID,
product_id UUID,
quantity INT,
order_date TIMESTAMP DEFAULT current_timestamp()
);

Now, insert some sample data:

sql

INSERT INTO orders (customer_id, product_id, quantity) VALUES

(gen_random_uuid(), gen_random_uuid(), 2),

(gen_random_uuid(), gen_random_uuid(), 3),

(gen_random_uuid(), gen_random_uuid(), 1);

Setting Up Redpanda and Redpanda Connect

Running Redpanda

If you haven’t installed Redpanda yet, you can quickly get it running using Docker:

bash

docker run -d --name=redpanda \

-p 9092:9092 \

vectorized/redpanda:latest \

redpanda start --overprovisioned --smp 1 --memory 1G --reserve-memory 0M --node-id 0 --check=false

Configuring Redpanda Connect

Redpanda Connect is Redpanda’s mechanism for integrating with other systems, similar to Kafka Connect. First, run a Redpanda Connect container:

bash

docker run -d --name=redpanda-connect \

-p 8083:8083 \

vectorized/connect:v23.2.0 \

connect-distributed.sh /etc/kafka/connect-distributed.properties

Setting Up a CockroachDB Source Connector

Now that Redpanda Connect is up and running, you need to configure a source connector to capture data changes from CockroachDB and push them into Redpanda. For this, we can use the Debezium connector, which supports CockroachDB.

First, create a JSON configuration file for the connector:

json

{

"name": "cockroachdb-source-connector",

"config": {

"connector.class": "io.debezium.connector.postgresql.PostgresConnector",

"database.hostname": "cockroachdb",

"database.port": "26257",

"database.user": "root",

"database.password": "",

"database.dbname": "sales_db",

"database.server.name": "cockroachdb",

"plugin.name": "pgoutput",

"table.include.list": "public.orders",

"snapshot.mode": "initial",

"tasks.max": "1"

}

}

Submit the connector configuration to Redpanda Connect:

bash

curl -X POST -H "Content-Type: application/json" \

--data @cockroachdb-source-config.json \

http://localhost:8083/connectors

This configuration will start capturing changes from the orders table in CockroachDB and stream them to a Redpanda topic named cockroachdb.public.orders.

Configuring Snowflake for Data Ingestion

Create a Snowflake Database and Table

Log into your Snowflake account and create a database, schema, and table to store the incoming data:

sql

CREATE DATABASE sales_db;

USE sales_db;

CREATE SCHEMA sales_schema;CREATE TABLE orders (
id STRING,
customer_id STRING,
product_id STRING,
quantity INTEGER,
order_date TIMESTAMP
);

Create a Redpanda Sink Connector for Snowflake

Next, configure a sink connector in Redpanda Connect to consume data from the Redpanda topic and load it into Snowflake.

Create a JSON configuration file for the Snowflake sink connector:

json

{

"name": "snowflake-sink-connector",

"config": {

"connector.class": "com.snowflake.kafka.connector.SnowflakeSinkConnector",

"topics": "cockroachdb.public.orders",

"snowflake.url.name": "https://<your_account>.snowflakecomputing.com",

"snowflake.user.name": "<your_username>",

"snowflake.private.key": "<your_private_key>",

"snowflake.database.name": "SALES_DB",

"snowflake.schema.name": "SALES_SCHEMA",

"snowflake.table.name": "ORDERS",

"snowflake.ingestion.method": "SNOWPIPE",

"tasks.max": "1"

}

}

Submit the Snowflake sink connector configuration:

bash

curl -X POST -H "Content-Type: application/json" \

--data @snowflake-sink-config.json \

http://localhost:8083/connectors

This configuration instructs Redpanda Connect to read data from the cockroachdb.public.orders topic and insert it into the orders table in Snowflake using Snowpipe.

Verifying Data Flow

To verify that the entire pipeline is working, you can insert new data into the orders table in CockroachDB and check if it appears in Snowflake.

Insert a new order in CockroachDB:

sql

INSERT INTO orders (customer_id, product_id, quantity) VALUES

(gen_random_uuid(), gen_random_uuid(), 5);

Now, query the orders table in Snowflake:

sql

SELECT * FROM sales_db.sales_schema.orders;

You should see the new order reflected in Snowflake, confirming that the real-time data ingestion pipeline is working as expected.

Conclusion

Ingesting real-time data from distributed databases like CockroachDB into Snowflake can significantly enhance your data processing and analytics capabilities. By using Redpanda Connect as a streaming intermediary, you can achieve a robust and scalable data pipeline that ensures timely and accurate data delivery. The combination of CockroachDB’s distributed nature, Redpanda’s high-performance streaming, and Snowflake’s powerful data warehousing creates a compelling solution for modern data-driven applications.

This setup can be extended further by adding more connectors for different data sources, scaling the Redpanda cluster for higher throughput, or using advanced Snowflake features like materialized views for real-time analytics. The flexibility and scalability of these technologies ensure that your data infrastructure can grow with your business needs.

With this comprehensive guide, you now have a solid foundation for building a real-time data ingestion pipeline using CockroachDB, Redpanda, and Snowflake. Whether you’re dealing with e-commerce, finance, or IoT data, this approach provides the necessary tools to stay ahead in the competitive data landscape.