MariaDB 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 things aspects such as 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 Relational Database Management Systems (RDBMS) is MariaDB. This article will introduce you to MariaDB and Google BigQuery. It will also help you understand how you can set up MariaDB to BigQuery Migration to analyze your MariaDB data.

Introduction to MariaDB

MariaDB is a popular Open-source Relational Database Management System (RDBMS). It was developed as a software fork of another popular Open-source database, MySQL, by the developers who played key roles in developing the original database. MariaDB was devised in 2009 when MySQL was acquired by Oracle. It was designed to ensure ease of use, speed, and reliability for all its users. 

Like all other Relational Database Management Systems (RDBMS), MariaDB houses support for ACID-Compliant Data Processing. Along with that, it also supports parallel Data Replication, JSON APIs, and multiple storage engines, including InnoDB, Spider, MyRocks, Aria, Cassandra, TokuDB, and MariaDB ColumnStore.

More information on MariaDB can be found here.

Understanding the Key Features of MariaDB

The key features of MariaDB are as follows:

• Robust Transactional Support: Implementation of ACID (Atomicity, Consistency, Isolation, Durability) properties ensures no data loss or inconsistency.
• Ease of Use: Considering that it makes use of SQL for querying data, anyone with basic knowledge of SQL can perform the required tasks with ease.
• Security: It implements a complex data security layer that ensures that only authorized users can access sensitive data.
• Scalable: MariaDB is considered to be highly scalable due to its support for multi-threading. 
• Roll-back Support: MariaDB houses support for roll-backs, commits, and crash recovery for all transactions.
• High Performance: MariaDB houses various fast load utilities along with Table Index Partitioning and Distinct Memory Caches that can help ensure high performance.

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.

Understanding the Need for MariaDB to BigQuery Migration

 Businesses feel the need to set up MariaDB to BigQuery Migration because of the following reasons:

• Powerful Analytics: There are numerous analytics workloads that businesses can run on a Data Warehouse. Since Google BigQuery houses an SQL engine, businesses can use Business Intelligence tools like Google Data Studio, Looker, and Tableau to create descriptive data visualizations and reports.
• Machine Learning Capabilities: Google BigQuery goes beyond conventional Data Warehousing. It can be used to create robust Machine Learning models to carry out batch predictions without having to export data out of Google BigQuery.
• Simplified Workflows: Google BigQuery by design is meant to encourage customers to focus on gathering insights as opposed to managing infrastructure. This approach allows teams to innovate faster with fewer resources. DBAs are not needed to provision and maintain servers, and this enables them to work with a lean team of Data Analysts.
• Scale-out Architecture: From an architectural point of view, the only limit on speed and scale in Google BigQuery is the amount of hardware in the Availability Zone (AZ). Queries are automatically scaled to thousands of machines and executed in parallel. This is the same infrastructure used on other Google products like AdWords, YouTube, Gmail, G-Suite, Google Search, etc.
• Rich Product Ecosystem: Google BigQuery is part of the GCP ecosystem, and it integrates tightly with Cloud AI, Cloud DLP, AutoML, Data Studio, Cloud Scheduler, Cloud Functions, Cloud Composer, etc.

Methods to Set up MariaDB to BigQuery Migration

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

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

Method 1: Manual MariaDB to BigQuery Migration

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

• Step 1: Export your MariaDB data into CSV format by running the following query:

mysql --host=[INSTANCE_IP] --user=[USER_NAME] --password [DATABASE] 
-e " SELECT * FROM mydataset.mytable INTO OUTFILE 'myfile.csv' CHARACTER SET 'utf8mb4'
    FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' ESCAPED BY '"' "

• Step 2: Once the data has been exported from MariaDB 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
    )
)

Limitations of Manual MariaDB to BigQuery Migration

The limitations associated with manual MariaDB to BigQuery Migration are as follows:

• During this process, your MariaDB database might get updated. This means your destination database might miss some records and need to be updated in real-time. The above approach would not work for a use case like this. If you’re looking for real-time database transfer, then additional functionality needs to be introduced to ensure that the data in MariaDB is synced at regular intervals with BigQuery.
• Unfortunately, Google BigQuery is not really designed for working with changes on its tables. Therefore, if the use case requires Data Streaming, you will have to implement Change Data Capture (CPC), which might not be very efficient with Google BigQuery.
• This method is only suitable in cases where you only want to load small datasets. As data size increases, the incidence of error also increases.
• There is a maximum row and cell size limit of 100MB.
• The maximum CSV file size that can be loaded into BigQuery is 5 TB.
• The maximum size per load job is 15 TB across all input files for CSV.
• Google BigQuery does not guarantee Data Consistency when using this method to load data into Google BigQuery. If the underlying data is updated while a query is running, it will result in unpredictable behavior.
•  If you need to clean, transform, and enrich the data in MariaDB before loading it to Google BigQuery, you would need to write additional code to accomplish this. 

Method 2: MariaDB to BigQuery Migration Using Hevo Data

Hevo helps you directly transfer data from MariaDB 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.

Hevo takes care of all your data preprocessing needs required to set up MariaDB 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.

Get Started with Hevo for free

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.

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Conclusion

This article provided you with a step-by-step guide on how you can set up MariaDB 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.

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.

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Share your experience of setting up MariaDB to BigQuery Integration in the comments section below!