3 Common Approaches to Real-time Data Processing

With its powerful capabilities and flexible architecture, Decodable plays a crucial role in modern data stack architectures, serving as the central hub for real-time data ingestion, processing, and integration. By unlocking the power of Apache Flink and Debezium, Decodable enables organizations to tackle their most pressing real-time data challenges with ease. Let's explore some common approaches to data processing and how Decodable excels in implementing them.

Real-time ELT: Continuous Data Movement for Analytics

Real-time ELT focuses on continuously moving data from source systems into target systems like data warehouses, data lakes, or OLAP databases. Unlike ETL, which transforms data before loading, ELT loads raw data as-is and performs any necessary transformations later within the target system. Decodable simplifies real-time ELT by providing fully managed CDC (Change Data Capture) connectors, powered by Debezium, that enable continuous data loading without the need for complex batch processes. Decodable also supports the “EtLT” pattern, in which light transformation, such as filtering out PII, is done before loading the data into the destination system, where more extensive transformations can then be applied.

Decodable seamlessly integrates with a wide range of external data systems, enabling organizations to deliver real-time data to the tools and platforms that drive analytics and decision-making. For data lakehouses like Databricks and data lakes like Amazon S3, Decodable can stream real-time data, combining the benefits of data lakes, lakehouses, and warehouses for both real-time and batch processing. Decodable also feeds real-time data into analytics systems such as Apache Druid, Apache Pinot, Clickhouse, and Elasticsearch, empowering organizations to build real-time dashboards, monitor key metrics, and detect anomalies as they occur.

A common real-time ELT use case is database replication, with many organizations needing to replicate tables from OLTP (Online Transaction Processing) database systems to make the data available for analytics in data warehouses, data lakes, or OLAP (Online Analytical Processing) databases. Decodable's CDC connectors allow you to continuously load data changes rather than relying on batch processes. This approach reduces cost and improves latency, ensuring that your analytics systems always have access to the most up-to-date information. For example, a retail company can use Decodable to replicate sales transactions from their e-commerce database into a data warehouse in real time, enabling up-to-the-minute reporting and analysis.

Real-time ETL: Continuous Data Ingestion, Processing, and Delivery

Real-time ETL involves continuously extracting data from source systems, applying transformations in-flight, and loading the processed data into target systems. Decodable simplifies real-time ETL by providing a unified platform that sits at the heart of a modern data stack, acting as the integral engine for data movement between disparate systems. It typically resides between data sources, such as databases, event streaming platforms, and SaaS applications, and data sinks, including data warehouses, data lakehouses, and real-time analytics systems.

Decodable offers several key benefits over alternative real-time ETL solutions. Its flexibility and extensibility make it easy to integrate with a wide range of data sources and sinks. The platform seamlessly delivers real-time data to downstream systems like data warehouses, enabling up-to-date reporting and analysis. Because the Decodable platform abstracts away the complexities of infrastructure management, data engineers can focus on building real-time ETL pipelines using SQL or custom processing logic.

Ingesting clickstream data, order information, or other application events into data warehouses, data lakes, or OLAP databases is a common requirement for many businesses. With Decodable, you can easily create connections for each source system, transform and enrich the data using SQL or custom processing, and then ingest the processed data into your preferred analytics platform.

By enabling real-time ETL, Decodable empowers organizations to make data-driven decisions based on the freshest information. It eliminates the latency and complexity of traditional batch processes, unlocking opportunities for real-time analytics and actionable insights. With Decodable's intuitive platform and powerful capabilities, real-time ETL becomes accessible and achievable for data teams.

Stream Processing: Flexible Data Transformation, Filtering, and Routing

In addition to real-time ETL and ELT, Decodable enables arbitrary stream processing, focusing on the transformation and routing of data between sources and sinks. With Decodable's powerful processing engine built on Apache Flink, organizations can perform complex operations on streaming data, such as filtering, enrichment, aggregation, and custom business logic.

Decodable acts as the central hub for stream processing in the modern data stack, facilitating the continuous flow of data and ensuring real-time processing and delivery. It seamlessly integrates with various upstream and downstream data systems, consuming data from event streaming platforms, databases, and APIs, applying processing logic, and routing the transformed data to destinations like data warehouses, data lakes, or real-time applications.

Decodable offers key benefits over alternative stream processing solutions. It abstracts away the complexities of managing Apache Flink infrastructure, providing an intuitive interface for defining data flows, while preserving the APIs developers already know. This allows data engineers and developers to focus on business logic rather than technicalities. Decodable leverages Flink's stream processing capabilities for true real-time data transformation without the latency of batch processing. It also provides flexibility and extensibility, supporting SQL-based processing and custom Flink jobs written in Java or Python.

In microservices architectures, it's common to have multiple services producing or consuming data from Apache Kafka topics. However, these services often require different data formats, schemas, or subsets of the same data. Decodable allows you to use standard SQL to filter, route, enrich, aggregate, or perform any other necessary transformations on your Kafka data streams. This simplifies the process of ensuring that each microservice receives the data it needs in the format it expects. For example, a retail company can use Decodable to consume order events from a Kafka topic, enrich the data with customer information, apply business rules to calculate discounts, and then route the processed data to separate topics for the inventory, shipping, and analytics microservices.

By enabling arbitrary stream processing, Decodable empowers organizations to build real-time data pipelines that adapt to their unique data flow requirements. It provides a flexible and intuitive platform for transforming and routing data between systems, ensuring that the right data reaches the right destination at the right time.