In real-time applications, data constantly moves. Every click, transaction, and interaction generates data businesses want to capture and use immediately to take relevant actions. But how can you detect and process this constant stream of data? The answer lies in database streaming. In this blog, we will explore database streaming, its benefits, and the popular techniques and tools used to implement it.
What Is Database Streaming?
Database streaming is the process of continuously capturing changes in a database (like new rows, updates, or deletions) and sending those changes to another destination or data consumer in real time (this can be a target Database, Analytics tools, APIs, or applications). Streaming data changes from databases solves the problems of older methods (batch processing) that struggle with the massive amounts of data we generate today. Instead of waiting hours or days to collect and process all the data, streaming makes it efficient to handle updates instantly as they happen.
Why Is Database Streaming Important?
Let's have a look at some benefits of using database streaming to achieve real-time data synchronization, faster decision-making and enhanced AI applications.
1. Real-time insights
Businesses thrive on real-time information. Think about a logistics company tracking deliveries. With database streaming, they can see the location of every package instantly and reroute deliveries if needed. From data in your primary database, you build real-time views that serve internal and customer-facing dashboards.
2. Data synchronization
Streaming keeps all systems, from CRMs to analytics dashboards, up to date with the most current data. For example, if your sales team is using a CRM tool, database streaming makes sure they always see the latest customer updates.
Additionally, database streaming is invaluable for migrating data—whether from an on-premise database to the cloud or from a primary database to an analytics database. By continuously streaming changes during the migration process, you avoid downtime and guarantee that no data is lost.
Example:
- While migrating a database from on-premise servers to a cloud provider like AWS or Google Cloud, streaming keeps the new database in sync with the old one until the migration is complete.
- When syncing a primary database (e.g., PostgreSQL) with an analytics database (e.g., Snowflake or BigQuery), database streaming ensures that new transactions are available for analysis in real-time.
3. Enables AI and machine learning
You can find a high demand for database streaming in AI use cases as well. Because AI applications need fresh, high-quality data to deliver accurate predictions. When you first start building an AI application, you might use a relational database like PostgreSQL or MySQL to store your data. PostgreSQL is a great choice for managing structured data efficiently. For instance, you might use it to store user activity logs, transactions, or product details. However, as your application evolves, you’ll likely need to send data changes frequently to your AI models to keep them updated. Database streaming solves this problem by capturing changes in real time and streaming these updates directly to your AI pipeline.
4. Improves user experience
From personalized recommendations to instant notifications, database streaming makes user experiences smoother and more dynamic.
- Social media platforms like Facebook or LinkedIn use streaming to process likes, comments, and shares in real time.
Techniques and tools for setting up database streaming
So, what other techniques and tools we can leverage to build a database streaming solution? While the exact setup depends on the tools you choose, the general approach is similar:
1. Capture Changes in Your Database (Change Data Capture - CDC)
The first step is to monitor your database for any changes, like updates, inserts, or deletions. This technique is called Change Data Capture(CDC) which tracks and captures changes in your database such as MySQL, Microsoft SQL, Oracle, PostgreSQL, MongoDB, or Cassandra. CDC works by continuously monitoring the database for any changes made to the data. Multiple types of change data capture patterns can be used for data processing from a database. These include log-based CDC, trigger-based CDC, CDC based on timestamps, and difference-based CDC.
Tools to Use:
- Debezium: Tracks changes in databases and streams them to other services.
- AWS Database Migration Service (DMS): Helps detect and replicate database changes.
- Supabase, Google Firestore, Amazon DynamoDB, or Azure CosmosDB: These databases have real-time features to detect changes and send change events to subscribed data consumers via built-in integrations or webhooks.
Example: When a customer updates their shipping address, CDC will capture that change and pass it on to the email service so they get a confirmation email immediately.
2. React to Changes Using Events (Event-Driven Architecture - EDA)
After capturing changes in your database, the next step is to decide what actions to take. In an event-driven architecture (EDA), every change (called an "event") triggers a specific action. Per each event, you usually process data to transform, enrich, or analyze the data before sending it to the next receiver or application. For example, if an update is made to a customer record in the database, the event could trigger actions like recalculating loyalty points, updating the CRM service, and sending a personalized email.
Tools to Use:
- Serverless Data Transformation in Python: GlassFlow with its Python-first approach and serverless architecture, can process and transform billions of events in real time. One of the possible integration with GlassFlow you can achieve is consuming changes via webhook events from Supabase and instantly trigger Python-based transformation functions.
- Message Broker: Apache Kafka is great at handling streams of data. It can collect and transport huge amounts of information in real time. Kafka works well with Debezium. Debezium captures database changes and streams events via Kafka to data consumers. However, you also need combine with another service like Flink to transform events.
- Serverless Functions: Serverless services like AWS Lambda, Google Cloud Functions, or Azure Functions can quickly react to events, performing tasks like transforming or enriching the data. You can combine these functions with databases. For example, AWS Lambda can be triggered by DynamoDB Streams to process changes in a database, Google Cloud Functions can react to Firestore triggers and Azure Functions can be triggered by the change feed from CosmosDB.
- Data Transformation Tools: Use tools like AWS Glue or dbt to clean and process data as part of your event-driven workflows.
- Streaming database: A streaming database keeps downstream systems updated with the latest changes from the source database, allowing them to use simple SQL queries to access current data. It also lets you organize CDC data into a materialized view, where query results are stored in the database’s local cache for faster performance. Materialize, and RisingWave are a few of them.
3. Build an Event Data Pipeline
The next step is to move the captured and processed data to its destination and automate this process for every change in the main database. You build an event-driven pipeline to ingest specific events, transform them, and send them to the intended destination. The Event-driven pipeline manages the data flow from the data producer to the data consumer.
Tools to Use:
- GlassFlow: Simplifies setting up event-driven pipelines reducing infrastructure and DevOps costs.
- Google Dataflow: Ideal for creating real-time and batch processing pipelines on Google Cloud.
- Other serverless setups: Serverless solutions also allow you to run code in response to events, but they often require additional components to handle data pipelines effectively. For example, AWS Lambda, when paired with Amazon Kinesis, provides a highly flexible way to build event-driven pipelines. Lambda functions can process incoming data streams, enabling quick transformations or triggering additional workflows.
Conclusion
By adopting database streaming, you can overcome the limitations of batch updates, improve real-time interactions, and enable your AI application to provide timely and relevant insights. This becomes especially important as your application scales, and the volume of data changes increases. With tools like GlassFlow setting up event-driven has never been easier. By adopting the right tools and techniques, businesses can unlock the full potential of their data, making faster decisions and creating better experiences.
Are you ready to take your data to the next level with database streaming? Let’s get started! 🚀
