Amazon RDS, a managed database service, is designed to support several databases. It simplifies the deployment, scaling, and administration of relational databases like PostgreSQL. However, by moving PostgreSQL on Amazon RDS data to BigQuery, a fully-managed data warehouse, you can analyze massive amounts of data effortlessly. Data migration from PostgreSQL on Amazon RDS to Google BigQuery integration empowers businesses to analyze customer journeys, sales growth, popular products, and more.
This article gives you detailed information on the two different ways to connect PostgreSQL on Amazon RDS to BigQuery.
Methods to Connect PostgreSQL on Amazon RDS to BigQuery
- Method 1: Move Data from PostgreSQL on Amazon RDS to BigQuery using CSV Files.
- Method 2: Use a No-Code Tool for PostgreSQL on Amazon RDS to BigQuery Migration.
Method 1: Move Data from PostgreSQL on Amazon RDS to BigQuery Using CSV Files
The PostgreSQL on Amazon RDS to BigQuery integration process using the CSV files includes the following steps:
Step 1: Export Data to CSV Files
To extract data from PostgreSQL on Amazon RDS into CSV file, you can use PostgreSQL COPY or PostgreSQL Dump command. Use the psql to connect to your Amazon RDS PostgreSQL database and run the following command.
- Using PostgreSQL COPY Command
The COPY command in PostgreSQL enables you to export data from a table to a CSV file directly.
COPY table_name TO '/path/to/your_file.csv' DELIMITER ',' CSV HEADER;
Let’s understand the above command.
table_name: Replace this with the table name from which you want to export data.
/path/to/your_file.csv: Mention the desired file path, and name for the CSV file.
DELIMITER ‘,’: This option specifies the delimiter to be used in the CSV file.
CSV: Indicates that the output format will be in CSV.
HEADER: It means that the first row in the CSV file will contain the columns names.
pg_dump is a PostgreSQL utility allowing you to create a backup of a PostgreSQL database. Use this command with the –data-only and –column-inserts options to export data from Amazon RDS PostgreSQL tables to CSV files. Here is the basic syntax of the pg_dump command:
pg_dump -h <RDS_HOST> -p <RDS_PORT> -U <RDS_USERNAME> -d <RDS_DB_NAME> -t <TABLE_NAME> --data-only --column-inserts --file <OUTPUT_FILE_PATH>
The backup file will be in the SQL file format, which you need to convert into a CSV file.
Step 2: Create a Google Cloud Storage Bucket and Upload the CSV File
In the Google Cloud Console, create a new Google Cloud Storage (GCS) bucket to store the CSV files.
- Log in to Google Cloud Console. Navigate to the Storage section, and click on Browser in the left-side menu.
- Click the + CREATE BUCKET button on the Google Cloud Storage page to create a new bucket. Enter a unique name for the bucket. Choose the default storage class and location for your bucket.
- Open the bucket that you’ve just created by clicking on its name in the GCS browser. Click on the UPLOAD FILES button to upload CSV files from your local machine to the GCS bucket.
- In the file upload dialog, select the CSV files exported from Amazon RDS PostgreSQL that you want to upload.
- Once the upload is complete, you’ll see the uploaded files listed in the GCS bucket.
Step 3: Load Data into BigQuery
Once the CSV files are in the GCS bucket, you can load the data into BigQuery. Here’s how you can perform this task using the BigQuery Web UI:
- In the Google Cloud Console, click on the navigation menu (☰) in the top left corner and select BigQuery under the DATA section.
- Navigate to your Dataset where you want to create the new table or create a new dataset if required. To create a new dataset, click on the down arrow next to your project name, then select Create Dataset.
- Within the selected or newly created dataset, click on the Create Table to create a new table. Give you table a name and define a schema. For every column provide a column name, data type, and description. Ensure the column names and data types match the structure of the CSV files.
- Click Create table to create the BigQuery table.
- Navigate to the table that you’ve just created, select Google Cloud Storage as the data source. Enter the path to the CSV files in GCS that you want to load into the table.
- Click Create Table to start the data loading process.
BigQuery will now load the data from the CSV files into the specified table.
Despite involving several manual steps, this method is the most suitable for use cases such as:
- One-Time Data Migration: Using CSV file approach is ideal when you need to do a one-time data transfer from AWS RDS PostgreSQL to BigQuery. It allows you to migrate data without building complex data pipelines.
- Low Volume Data: When the amount of data to be migrated is limited, opting for CSV files as a data migration solution is the best-suited approach. There are almost no complexities in moving small csv files from one platform to another, making them an efficient choice for transferring smaller datasets.
- Data Security: With manual approach, you do not move your confidential data on third-party servers. This is highly beneficial when you are handling sensitive data.
Limitations of Using CSV Files to Connect PostgreSQL on Amazon RDS to BigQuery
Data Volume: This may not be the best method for huge datasets, as handling and transferring massive CSV files can be time-consuming and effort-intensive.
No-Real Data Synchronizing: The CSV file method does not support automated real-time data synchronization due to manual intervention. Consequently, businesses can only migrate historical data.
Method 2: Use a No-Code Tool to Automate the PostgreSQL on Amazon RDS to BigQuery Migration
With a no-code ETL tool, you can overcome the first method’s limitations and enjoy several compelling advantages:
- Simplified Data Transfer: No-code tools streamline the data integration and migration process with pre-built connectors. This eliminates the need to extract or load the data manually. As a result, it saves time, reduces errors, and ensures a quick and smooth data migration between source and target systems.
- Real-time Data Integration: No-code tools provide real-time or incremental data syncing capabilities, enabling continuous updates from AWS RDS PostgreSQL to BigQuery. This ensures that the data in BigQuery remains current and up-to-date for businesses to obtain timely insights.
- Scalability: No-code platforms are designed to handle large-scale data migrations efficiently, making them ideal for organizations with growing datasets and performance-critical applications.
With Hevo Data, setting up a PostgreSQL on Amazon RDS to BigQuery ETL pipeline is incredibly fast. It consists of 150+ data connectors to seamlessly extract data from your chosen source and load it into the desired destination.
Here are the steps you can follow to load data from PostgreSQL on Amazon RDS to BigQuery using Hevo platform:
Step 1: Configure Amazon RDS PostgreSQL as Source
Step 2: Configure BigQuery as Destination
After completing these two steps, you can load the data from PostgreSQL on Amazon RDS to BigQuery. Hevo Data enables you to set up the data integration pipelines along with numerous advantages:
- Extensive Data Connectors: Hevo supports multiple connectors to effortlessly extract data from various sources, including PostgreSQL on Amazon RDS, and load it to your BigQuery tables.
- Quick Setup: Using Hevo, you can instantly configure ETL pipelines with pre-built connectors, saving time and effort. Simply select the Amazon RDS PostgreSQL for data extraction and then configure BigQuery for fully managed and automated data migration.
- Drag-and-Drop Data Transformation: Hevo offers this user-friendly feature, making data transformations effortless. With the drag-and-drop transformation capabilities, users can easily carry out lightweight transformations from PostgreSQL on Amazon RDS to BigQuery. However, you can use Python and SQL for custom or advanced transformations.
- Reduced Maintenance: Hevo’s fully managed data migration process minimizes the ETL pipeline maintenance for data replication from PostgreSQL on Amazon RDS to BigQuery. It handles all the continuous maintenance duties, like schema drift management, freeing up your time and resources.
What can you Achieve by Integrating Data from PostgreSQL on Amazon RDS to BigQuery
Here are some of the analyses you can perform after PostgreSQL on Amazon RDS to Bigquery replication:
- Examine sales funnel stages for deeper understanding of customer journeys.
- Identify top revenue contributors to prioritize personalized incentives and strengthen relationships.
- Use data from multiple sources (project management, HR, communication) to establish key performance indicators for team performance.
- Integrate transactional data from Sales, Marketing, Product, and HR to answer essential questions, such as:
- Analyze and identify the best-selling products and understand customer buying behavior.
- Pinpoint customers at risk of churn to implement targeted retention tactics.
- Assess operations for inefficiencies and opportunities to improve overall performance.
Migrating data from PostgreSQL on Amazon RDS to BigQuery helps you centralize massive datasets for in-depth data analytics. The CSV file export method provides a manual and time-consuming approach with limitations in performance, scalability, and real-time data integration. On the other hand, Hevo offers a streamlined and fully automated data integration process using pre-built data connectors, automated workflows, data transformation flexibility, and reduced maintenance efforts.
Businesses can quickly set up PostgreSQL on Amazon RDS to BigQuery ETL pipelines using Hevo to analyze and act on data as it arrives. Such real-time analytics empower organizations to make data-driven decisions promptly and the ability to develop innovative solutions and boost productivity to drive success.
Want to take Hevo for a spin? SIGN UP for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.