Time and effort are the two most important assets for every data team. A report or analysis delivered late where a lot of effort went into it ruins the whole point of working on it. To avoid this, you must optimize your data stack, including source and destination, for efficiency, reliability, and flexibility. That’s where Amazon RDS comes into play– a fully managed service that takes the burden of database administration off your shoulders.

It supports various database engines, from MySQL and PostgreSQL to Oracle and Microsoft SQL Server. This versatility empowers you to choose the engine that best fits your application without compromising performance or functionality. But the real magic happens when we pair Amazon RDS with Databricks – a dynamic duo that unlocks a whole new level of data management and analytics.

In this blog, we’ll take you on a journey through the seamless integration of these powerful tools, revealing how they create a unified ecosystem to effortlessly handle your data deployments and interact with other AWS services.

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Method 1: Using Delta Lake on Databricks

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, CSV, or JSON to delta. Once you have a Delta table, you can write data into it using Apache Spark’s Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

The steps involved in this method are:

  1. Create a Databricks cluster and connect it to your Amazon RDS instance:

To create a Databricks cluster, you can use the Databricks UI or the Databricks CLI. Once you have created a cluster, you need to connect it to your Amazon RDS instance. To do this, you need to provide the cluster with the connection information for your Amazon RDS instance, including the database name, username, and password.

2. Create a Delta table in Databricks that will store the replicated data:

A Delta table is a special type of table that is stored in Databricks Delta. Delta tables provide a number of advantages over traditional tables, including:

To create a Delta table in Databricks, you can use the Databricks UI or the Databricks CLI.

3. Write a Python script that uses the Databricks Delta API to replicate the data from Amazon RDS to the Delta table in Databricks.

The Databricks Delta API provides a number of functions that can be used to replicate data from Amazon RDS to Databricks. These functions include:

delta_load_table(): This function loads data from a source into a Delta table.
delta_save_table(): This function saves a Delta table to a destination.
delta_history(): This function retrieves the history of a Delta table.

To write a Python script that uses the Databricks Delta API to replicate the data from Amazon RDS to the Delta table in Databricks, you can use the following code:

import databricks.delta.tables as delta

# Connect to the Databricks cluster
cluster = dbutils.active_cluster()

# Create a Delta table
table = delta.create_table(cluster, "my_table")

# Replicate the data from Amazon RDS to the Delta table
delta_load_table(cluster, "my_table", "my_rds_table")

4. Schedule the Python script to run on a regular basis.

Once you have written the Python script, you can schedule it to run on a regular basis using the Databricks scheduler. This will ensure that the data is replicated from Amazon RDS to Databricks on a regular basis.

The advantage of this method is that simple to use as it’s a managed service from Amazon. But, it may not be suitable for all use cases. For example, Data lakes can be slow to query, especially as the size of the data increases. This can be a problem if you need to access the data quickly for analysis or machine learning.

Method 2: Using Custom Script Method

Here are the detailed steps:

  • Create a Kafka cluster: You can create a Kafka cluster using a number of different tools, such as Confluent Platform.
  • Create a topic in the Kafka cluster: A topic is a logical grouping of messages in Kafka. To create a topic, you need to provide the name of the topic and the number of partitions.
  • Configure a Kafka producer to read data from Amazon RDS and write it to the Kafka topic: The Kafka producer is a Java application that reads data from Amazon RDS and writes it to the Kafka topic. To configure the Kafka producer, you need to provide the connection information for your Amazon RDS instance, the name of the topic, and the format of the data.
  • Configure a Kafka consumer to read data from the Kafka topic and write it to Databricks.
  • The Kafka consumer is a Java application that reads data from the Kafka topic and writes it to Databricks. To configure the Kafka consumer, you need to provide the connection information for your Databricks cluster, the name of the topic, and the format of the data.
  • Start the Kafka producer and consumer.
  • Once you have configured the Kafka producer and consumer, you can start them. The Kafka producer will start reading data from Amazon RDS and writing it to the Kafka topic. The Kafka consumer will start reading data from the Kafka topic and writing it to Databricks.

Method 3: Using a Third-party No-code Tool

A third-party ETL tool like Hevo Data will help you move data from Amazon RDS to Databricks in a few minutes. Hevo Data comes in handy with the following benefits:-

  • Ready-to-use tool: You can set up the data pipelines in minutes and replicate data with native integrations to 150+ data sources.
  • Security: Hevo promises secure data integration through its compliance with SOC II, GDPR, and HIPAA.
  • Completely automated platform: It automatically handles schema changes in the incoming data with its automapping feature.
  • No infrastructure to manage: Since it’s fully managed, autoscaling is easy with an increase in the volume of data.
  • Personalized customer support: 24/7 support via chat/mail for any issues you may face while building and maintaining pipelines.

Next, let’s see the steps involved in this.

Step 1: Configure Amazon RDS as a source

Configure Amazon RDS as a source

Step 2: Configure Databricks as the destination

Configure Databricks as the Destination

That’s it about the methods of replicating data from Amazon RDS to Databricks. Now, let’s wrap it up!

Conclusion

The versatility of Amazon RDS empowers you to choose the engine that best fits your application without compromising performance or functionality. But, you would need a good data warehouse like Databricks to untap the full potential of this database. And to carry out this replication, there are three main methods.

The first method is to use Delta Lake on Databricks. But, Data lakes can be slow to query, especially as the size of the data increases. Another way to carry out the replication is by building in-house pipelines using a streaming platform like Kafka. The disadvantage of this method is that it consumes your engineering team’s bandwidth, and the cost of operation is high.

The third method is to use an automated third-party tool like Hevo Data which will release you from the burden of building and monitoring pipelines. Now, it’s your call to decide which one is apt for your use case.

Offering 150+ plug-and-play integrations and saving countless hours of manual data cleaning & standardizing, Hevo Data also offers in-built pre-load data transformations that get it done in minutes via a simple drag-and-drop interface or your custom Python scripts. 

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Anaswara Ramachandran
Content Marketing Specialist, Hevo Data

Anaswara is an engineer-turned writer having experience writing about ML, AI, and Data Science. She is also an active Guest Author in various communities of Analytics and Data Science professionals including Analytics Vidhya.

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