Amazon RDS to BigQuery Migration: 2 Easy Methods

The volume of data that businesses collect has grown exponentially over the past few years. This could be data related to numerous aspects like how people are interacting with their product or service, what people think of their offerings, how well is the performance of their Marketing efforts, etc. Businesses can then use this data to make data-driven decisions and plan their future strategies accordingly.

One of the most widely used databases is Amazon Relational Database Service (RDS) which gives businesses the ability to choose from multiple Relational Databases. This article will help you understand how you can set up Amazon RDS to BigQuery Migration to analyze your Amazon RDS data.

Introduction to Amazon RDS

Amazon Web Services Relational Database Service (AWS RDS) is one of the most popular Cloud-based fully-managed database services that give users the ability to set up, operate and scale Relational Databases seamlessly in the cloud. It provides its users with a cost-efficient alternative to traditional on-premise database systems. With AWS RDS in place, users can focus solely on their applications and business since the time that would have been utilized in performing time-consuming database administration tasks is freed up.

AWS RDS gives users the ability to create instances of the most popular Relational Database Management Systems (RDBMS), including MySQL, MariaDB, SQL Server, Oracle, PostgreSQL, etc. This means that all existing applications of any business that might be using these databases can be seamlessly migrated to AWS RDS without any changes to the application’s codebase. Amazon RDS also carries out all necessary backups of the database on a regular basis and ensures that the version is up-to-date. 

Users can significantly benefit from the ability of AWS RDS to efficiently scale the compute resources or storage capacity associated with their Relational Database, as this allows users to pay only for what they use and not invest a fortune in external hardware resources while scaling up. AWS RDS also makes it easy for businesses to implement Data Replication and ensure availability, improve Data Durability, or scale beyond the capacity constraints of a single database instance for read-heavy database workloads. 

More information on Amazon RDS can be found here.

Understanding the Key Features of Amazon RDS

The key features of AWS RDS are as follows:

• Easy Scalability: AWS RDS allows users to scale their resources up or down as per their requirements without any downtime and pay only for the resources they use at any given point in time.
• High Availability and Durability: Amazon RDS operates on a highly reliable infrastructure offered by Amazon Web Services. Users have the ability to provision a Multi-AZ DB Instance. A Multi-AZ DB Instance ensures that AWS RDS synchronously replicates all user data to a secondary instance in a different Availability Zone (AZ). AWS RDS also houses numerous other features that exceptionally enhance the reliability of critical production databases, including automated backups, automatic host replacement, and database snapshots.
• Advanced Security: AWS RDS houses functionalities that give users the ability to control network access to their database. AWS RDS lets users run their database instances in Amazon Virtual Private Cloud (Amazon VPC), enabling them to isolate their database instances and connect to their existing IT infrastructure through an industry-standard encrypted VPN. Most Amazon RDS engine types offer encryption at rest and in transit.
• High-Speed Processing: AWS RDS allows users to choose between two SSD-backed storage options: one for cost-effective general-purpose use and the other optimized for high-performance OLTP applications. In addition, Amazon Aurora can provide users with a performance on par with commercial databases at almost 1/10th the cost.

Introduction to Google BigQuery

Google BigQuery is a well-known Cloud-based Enterprise Data Warehouse designed for business agility. It gives users the ability to run complex SQL queries and perform an in-depth analysis of massive datasets. Google BigQuery was built on Google’s Dremel technology to process read-only data.

It leverages a columnar storage paradigm that supports immensely fast data scanning, along with a tree architecture that makes querying and aggregating results significantly efficient. Google BigQuery is Serverless and built to be highly scalable. Google utilizes its existing Cloud architecture to successfully manage a serverless design. It also makes use of different data models that gives users the ability to store more dynamic data.

Google BigQuery is fully managed and performs storage optimization on existing data sets by detecting usage patterns and modifying data structures for better results.

More information on Google BigQuery can be found here.

Understanding the Key Features of Google BigQuery

• Fully Managed by Google: All databases or Data Warehouses built on Google BigQuery are deployed, maintained, and managed directly by Google. 
• Integrations: Google BigQuery, being a part of the Google Cloud Platform (GCP) supports seamless integration with all Google products and services. Google also provides a wide variety of integrations with numerous third-party services along with functionality to integrate with the APIs of applications that are not directly supported by Google.
• High Data Processing Speed: Google BigQuery was designed to enable users to perform real-time analysis of their data.
• Serverless: In Google BigQuery’s Serverless model, the processing is automatically distributed over a vast number of machines that are working in parallel. Hence, any business making use of Google BigQuery can focus on gaining insights from data rather than on setting up and maintaining the infrastructure.
• Google BigQuery ML: Google BigQuery houses a functionality called BigQuery Machine Learning (BQML) that gives users the ability to create and execute Machine Learning models using standard SQL queries.

Methods to Set up Amazon RDS to BigQuery Migration

Users can set up Amazon RDS to BigQuery Migration by implementing one of the following methods:

• Method 1: Manual Amazon RDS to BigQuery Migration
• Method 2: Amazon RDS to BigQuery Migration Using Hevo Data

Method 1: Manual Amazon RDS to BigQuery Migration

Users can set up manual Amazon RDS to BigQuery Migration by implementing the following steps:

• Step 1: Exporting Data from Amazon RDS
• Step 2: Importing CSV Data into Google BigQuery
• Step 3: Validating the Data Load

Step 1: Exporting Data from Amazon RDS

This blog shows how this step can be implemented for Amazon RDS MySQL. You have to execute corresponding queries for the database in your use case if not using MySQL. You can export the required data from Amazon RDS MySQL using mysqldump Unix command. Execute the following query to export the required data:

mysql --password=XXX --user=XXX --host=XXX.amazonaws.com --port=XXX --database=DataBaseName -e  "select * from TableName" | sed 's/t/","/g;s/^/"/;s/$/"/;s/n//g' > /usr/home/TableName.csv

The required data will now be downloaded as a CSV.

Step 2: Importing CSV Data into Google BigQuery

Once the data has been exported from Amazon RDS as CSV, it has to be imported into Google BigQuery. Users can easily perform a batch-load job in Python by running the following code:

from google.cloud import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

# TODO(developer): Set table_id to the ID of the table to create.
# table_id = "your-project.your_dataset.your_table_name"

job_config = bigquery.LoadJobConfig(
    source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1, autodetect=True,
)

with open(file_path, "rb") as source_file:
    job = client.load_table_from_file(source_file, table_id, job_config=job_config)

job.result()  # Waits for the job to complete.

table = client.get_table(table_id)  # Make an API request.
print(
    "Loaded {} rows and {} columns to {}".format(
        table.num_rows, len(table.schema), table_id
    )
)

Step 3: Validating the Data Load

You can now use Query editor to query & fetch the test data after the manual Amazon RDS to BigQuery migration so that you can validate the process. Additionally, you can validate the count of records in the Amazon RDS tables and imported Google BigQuery tables to cross-check if you have imported all the required records or not.

Best Practices for Manual Amazon RDS to BigQuery Migration

The best practices that all users should follow while implementing manual Amazon RDS to BigQuery Migration are as follows:

• While using the mysqldump, you can use command filters parameters such as –compress, –compact. COMPACT is a format supported by Antilope. It stores the first 768 bytes of BLOB in case its value doesn’t fit on the page. COMPRESSED is used for compressed tables.
• If you select Auto-Detect schema while creating a table on BigQuery UI, you need to validate it to be on the safe side. If you need to modify or add or remove columns from the table, we can also do that with the EDIT schema option available in Google BigQuery.
• If you don’t choose Auto-Detect schema, you will need to create it manually one by one with the help of the Add Field option.

Limitations of Manual Amazon RDS to BigQuery Migration

Limitations of Manual Amazon RDS to BigQuery Migration

• Brittle Processes: The entire mechanism to move data from Amazon RDS to BigQuery is set up manually. This setup is brittle and prone to error as it requires multiple coherent steps to be executed one after the other. Failure of any single part of the process can cause the entire data pipeline to fail. This can eventually result in data loss. 
• Inability to Transform Data: If your use case demands you to clean, transform and enrich data when moving it from Amazon RDS to BigQuery, then you will need to write additional code to accomplish that. This makes the overall process very cumbersome. 
• Constant Monitoring and Maintenance: More often than not, schema changes, scripts break and ultimately result in data inconsistencies. Since you are moving critical transactional data from Amazon RDS to BigQuery, you will need to station dedicated engineering resources to constantly monitor both the infrastructure and flow of data. This adds to the maintenance overhead.

Method 2: Amazon RDS to BigQuery Migration Using Hevo Data

Hevo helps you directly transfer data from Amazon RDS and various other sources to Google BigQuery, Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Sign up here for a 14-Day Free Trial!

Hevo takes care of all your data preprocessing needs required to set up Amazon RDS to BigQuery Migration and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. 

Let’s look at Some Salient Features of Hevo:

• Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
• Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
• Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
• Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
• Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
• Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
• Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Conclusion

This article provided you with a step-by-step guide on how you can set up Amazon RDS to BigQuery Migration manually or using Hevo. However, there are certain limitations associated with the manual method. If those limitations are not a concern to your operations, then using it is the best option but if it is, then you should consider using automated Data Integration platforms like Hevo.

Visit our Website to Explore Hevo

Hevo helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence, or desired destination in a fully automated and secure manner without having to write the code. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.

Want to try Hevo? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Have a look at our unbeatable pricing here, which will help you choose the right plan for you.

What are your thoughts on moving data from Amazon RDS to Google BigQuery? Let us know in the comments.