The previous two articles — [Big Data Computing：Batch Processing](https://xx/Big Data Computing：Batch Processing) and [Big Data Computing：Real-Time Processing](https://xx/Big Data Computing：Real-Time Processing) — introduced the principles, architectures, frameworks, and application scenarios of batch processing and real-time computing, as well as their respective limitations. We learned that as the two “dual engines” of big data computing, both batch processing and real-time computing have their own irreplaceable roles in different scenarios, despite not being perfect.
This article will compare batch processing and real-time computing from multiple perspectives to gain a deeper understanding of their characteristics, limitations, and suitable use cases.
Concepts
Batch Processing
Batch processing is a big data computing method that collects data in batches, processes them together, and outputs results at once. It is suitable for scenarios with large data volumes, complex computation logic, and high latency tolerance, such as daily or monthly reporting systems.
Real-Time Computing
Real-time computing typically refers to processing continuous data streams as soon as they are generated. The data is fed into a computation framework, processed within a defined time window, and the results are output immediately—unlike batch processing, which waits for an entire batch to accumulate before computing.
Architecture & Workflow
The core characteristics of batch processing are bulk processing, periodicity, and high throughput, making it suitable for latency-insensitive tasks like historical data analysis.
Real-time computing is characterized by continuous operation, low latency, and incremental computation, making it suitable for latency-sensitive tasks like tracking online user counts.
A comparison of architecture and workflow is shown below:
| Dimension | Batch Processing | Real-Time Computing |
|---|---|---|
| Data Source | Batch imports | Continuous streams (message queues, sockets) |
| Data Volume | Large, fixed-period batches | Small, per-second or per-minute windows |
| Computation | Full data computation | Incremental computation |
| Execution Mode | One-off or scheduled jobs | Long-running processes |
| Latency | High (minutes to hours) | Low (milliseconds to seconds) |
| Consistency | Strong consistency | Eventual consistency (requires handling disorder and delay) |
| Fault Tolerance | Job-level retries | Checkpoints, Exactly-once guarantees |
| Engines | Hadoop, Spark, Hive | Flink, Spark Streaming, Storm |
| Output | HDFS, Hive, RDBMS | Redis, NoSQL, ClickHouse |
Technology Ecosystem
The sustainability of a technology often depends not only on its capabilities but also on the maturity of its ecosystem.
- Batch Processing emerged in the early days of big data computing, with a mature ecosystem built around Hadoop. Storage evolved from HDFS to support S3, OSS, and other file systems, while compute engines evolved from MapReduce to Spark, Tez, etc. On top, data warehouse tools expanded from Hive to Impala and Presto. Backed by a stable community, batch processing remains vital for large-scale offline analytics.
- Real-Time Computing grew rapidly thanks to evolving application demands. Stream processing frameworks and messaging middleware advanced significantly, even surpassing batch processing in some scenarios. The ecosystem includes data ingestion tools, message queues, stream processing engines, and storage systems. For example, ingestion evolved from Flume and Canal to Flink CDC; message queues expanded from Kafka to Pulsar; stream engines advanced from Storm to the highly capable Flink.
Advantages & Disadvantages
| Dimension | Batch Processing | Real-Time Computing |
|---|---|---|
| Latency | High | Low |
| Data Volume | Handles massive full datasets | Processes incremental streams (with windowing) |
| Cost | High per-job cost, low scheduling freq | Continuous resource usage, high ops overhead |
| Stability | Mature, highly fault-tolerant | Sensitive to network issues, skew, disorder |
| Consistency | Strong consistency | Requires additional guarantees (e.g., Flink Exactly-once) |
| Dev Complexity | Low (Batch SQL / ETL) | High (must handle disorder, state, fault-tolerance) |
Application Scenarios
Batch Processing
- Offline Data Warehouse Construction: Use Hive to clean, aggregate, and generate wide tables for business analysis.
- Offline Analytics: Perform statistical analysis on various dimensions within a data warehouse.
- Machine Learning Training: Process historical data in bulk to prepare training datasets.
Real-Time Computing
- Real-Time Recommendations: Generate instant recommendations based on user behavior to improve accuracy and relevance.
- Risk Control: Monitor behavior, transactions, or sensitive activities in real-time to detect risks and reduce losses.
- Real-Time Analytics: Calculate real-time metrics (e.g., sales, online users) for instant decision-making.
Unifying Batch and Stream Processing
While batch and real-time computing each have suitable use cases, maintaining two separate stacks increases development and operations costs—especially when business logic overlaps.
This has led to batch-stream unification solutions, which aim to provide a unified API for both processing modes. Developers can write once and run in either mode, reducing cost and complexity. Flink and modern data lake technologies are evolving toward this unified model.
Conclusion
Batch and real-time computing are complementary approaches in big data. Batch excels in massive, one-off computations with a focus on accuracy and stability; real-time excels in low-latency processing for timely insights.
As ecosystems evolve, more big data platforms will adopt unified batch-stream architectures, allowing developers to build once and deploy for both. The next article will explore technologies behind batch-stream unification.