Simplify your Data Engineering Process with Datastream for BigQuery

Datastream for BigQuery simplifies and automates the tedious aspects of traditional data engineering. This serverless change data capture (CDC) replication service seamlessly replicates your application database to BigQuery, particularly for supported databases with moderate data volumes.

As an analogy, imagine running a library where traditionally, you manually catalog every book, update records for new arrivals, and ensure everything is accurate. This process can be tedious and time-consuming. Now, picture having an automated librarian assistant that takes over these tasks. Datastream for BigQuery acts like this assistant. It automates the cataloging process by replicating your entire library’s catalog to a central database.

I’ve successfully used this service at my company, where we manage a MySQL database with volumes under 100 GB. What I love about Datastream for BigQuery is that:

1. Easy Setup: The initial setup was straightforward.
2. One-Click Replication: You can replicate an entire database with a single click, a significant improvement over the table-by-table approach of most ELT processes.
3. Automatic Schema Updates: New tables and schema changes are automatically managed, allowing immediate reporting on new data without waiting for data engineering interventions.
4. Serverless Operation: Maintenance and scaling are effortless due to its serverless nature.

Here’s a screenshot showing the interface once you establish a connection:

Streamlining Traditional Data Engineering

Datastream for BigQuery eliminates much of the process and overhead associated with traditional data engineering. Below is a simplified diagram of a conventional data engineering process:

In a typical setup, a team of data engineers would manually extract data from the application database, table by table. With hundreds of tables to manage, this process is both time-consuming and prone to errors. Any updates to the table schema can break the pipeline, requiring manual intervention and creating backlogs. While some parts of the process can be automated, many steps remain manual.

Datastream handles new tables and schema changes automatically, simplifying the entire process with a single GCP service.

Why Replicate Data into a Data Warehouse?

Application databases like MySQL and PostgreSQL are excellent for handling application needs but often fall short for analytical workloads. Running queries that summarize historical data for all customers can take minutes or hours, sometimes even timing out. This process consumes valuable shared resources and can slow down your application.

Additionally, your application database is just one data source. It won’t contain data from your CRM or other sources needed for comprehensive analysis. Managing queries and logic with all this data can become cumbersome, and application databases typically lack robust support for BI tool integration.

Benefits of Using a Data Warehouse:

1. Centralized Data: Bring all your data into one place.
2. Enhanced Analytics: Utilize a data warehouse for aggregated and historical analytics.
3. Rich Ecosystem: Take advantage of the wide range of analytical and BI tools compatible with BigQuery.

Key Considerations for CDC Data Replication

As mentioned earlier, this approach works best for manageable data volumes that don’t require extensive transformations. When data is replicated, keep in mind the following:

1. Normalized and Raw Data: Replicated data is in its raw, normalized form. Data requiring significant cleaning or complex joins may face performance issues, as real-time data becomes less useful if queries take too long to run.
2. Partitioning: By default, data is not partitioned, which can lead to expensive queries for large datasets.

Conclusion

Using change data capture (CDC) logs to replicate data from application databases to a data warehouse is becoming more popular. This is because more businesses want real-time data access and easier ways to manage their data.

Datastream for BigQuery is a great tool for this. It’s serverless, automated, and easy to set up. It handles new tables and schema changes automatically, which saves a lot of time and effort.

By moving data to a centralized warehouse like BigQuery, businesses can:

1. Improve Access: Centralized data makes it easier to access and use with different analytical tools, leading to better insights.
2. Boost Performance: Moving analytical workloads to a data warehouse frees up application databases and improves performance for both transactional and analytical queries.
3. Enable Real-Time Analytics: Continuous data replication allows for near real-time analytics, helping businesses make timely and informed decisions.
4. Reduce Overhead: The serverless nature of Datastream reduces the need for manual intervention, letting data engineering teams focus on more strategic tasks.

As more companies see the value of real-time data and efficient data management, tools like Datastream for BigQuery will become even more important. Other companies, like Estuary, offer similar services, showing that this is a growing market. Keeping up with these tools and technologies is key for businesses to stay competitive.

In short, using CDC data replication with Datastream for BigQuery is a strong, scalable solution that can enhance business intelligence and efficiency.

Thanks for reading!