Batch Vs Streaming

This reference explains the fundamental differences between batch and streaming data processing patterns, focusing on their operational characteristics and practical implementation choices. Understanding when to apply each approach affects data pipeline reliability, latency requirements, and resource allocation decisions.

When to Choose Batch Processing

Use batch processing when:

• Data arrives in discrete chunks
• You can tolerate delays up to minutes or hours
• Processing involves complex transformations requiring multiple passes
• Data quality checks are important before downstream consumption

Example: Running a Batch Job

# Submit a batch job using a standard scheduler
kubectl create job --from=cronjob/monthly-report-generator my-monthly-job-2024-01-01

# Monitor job status with kubectl
kubectl get jobs
kubectl describe job my-monthly-job-2024-01-01

# View job logs
kubectl logs job/my-monthly-job-2024-01-01

Batch jobs typically process all available data for a given period, then complete. They are idempotent by design and can be replayed from the beginning.

Batch Processing Configuration

When implementing batch processing, use these key considerations:

# Sample job configuration
apiVersion: batch/v1
kind: Job
metadata:
  name: data-processing-job
spec:
  template:
    spec:
      containers:
      - name: processor
        image: data-processor:latest
        env:
        - name: BATCH_START_TIME
          value: "2024-01-01T00:00:00Z"
        - name: BATCH_END_TIME
          value: "2024-01-01T01:00:00Z"
        command: ["/app/process.sh"]
      restartPolicy: Never

The batch window must be explicitly defined in job configuration. If you forget to set time boundaries, the job processes all available data, which can cause issues with incremental processing or data consistency.

When to Choose Streaming Processing

Use streaming when:

• Data arrives continuously and needs near real-time processing
• Latency requirements are measured in seconds or milliseconds
• You need to maintain state across events
• Real-time analytics or alerting is required

Example: Starting a Streaming Pipeline

# Start streaming processor with specific configuration
docker run -d \
  --name streaming-processor \
  -e INPUT_TOPIC="user-events" \
  -e OUTPUT_TOPIC="processed-events" \
  -e PROCESSING_WINDOW="60s" \
  streaming-processor:latest

# Monitor container logs
docker logs -f streaming-processor

Streaming systems maintain continuous processing loops and typically process one event at a time or in small batches, maintaining state between events.

Streaming Configuration Options

Streaming systems require different configuration parameters than batch:

# Example streaming job specification
apiVersion: v1
kind: Pod
metadata:
  name: streaming-pipeline
spec:
  containers:
  - name: stream-processor
    image: stream-processor:latest
    env:
    # Windowing configuration
    - name: STREAM_WINDOW_SIZE
      value: "60"
    - name: STREAM_WINDOW_TYPE
      value: "sliding"
    # State management
    - name: STATE_BACKEND
      value: "memory"
    # Processing delay
    - name: PROCESSING_LATENCY_MS
      value: "1000"

The critical distinction is that streaming jobs do not have a defined completion time, and they must handle backpressure gracefully.

Key Differences in Data Flow

Batch Processing Flow

-- Example batch processing SQL
BEGIN TRANSACTION;

-- Read data for specific period
INSERT INTO transformed_table 
SELECT * FROM raw_table 
WHERE timestamp >= '2024-01-01T00:00:00Z' 
AND timestamp < '2024-01-01T01:00:00Z';

-- Validate data quality
UPDATE transformed_table 
SET quality_score = calculate_quality_score(data_column);

-- Commit entire batch at once
COMMIT;

Batch processing treats data as a complete set. It reads all input for a period, processes it, then commits the results. This approach is reliable but not real-time.

Streaming Processing Flow

-- Example streaming processing SQL
BEGIN TRANSACTION;

-- Process single event
INSERT INTO processed_events 
SELECT *, current_timestamp() as processed_at 
FROM incoming_events 
WHERE event_id = 'event-12345';

-- Update state in memory or database
UPDATE user_state_table 
SET last_seen = current_timestamp(), 
    session_count = session_count + 1 
WHERE user_id = 'user-98765';

COMMIT;

Streaming processes one event at a time, updating state as it goes. It maintains consistency through individual transaction boundaries rather than batch boundaries.

Common Failure Modes

Batch Processing Failures

Batch jobs fail silently when:

• Data quality issues are not detected early
• Configuration parameters are missing
• Input data is unavailable for the specified time range

# Error case: Missing required parameter
kubectl create job --from=cronjob/report-generator my-job
# Job runs but processes no data because BATCH_START_TIME is not set

The batch job completes successfully even though it processed zero records, leading to silent data gaps.

Streaming Processing Failures

Streaming systems fail when:

• State storage becomes unavailable
• Processing latency exceeds window boundaries
• Event ordering is violated

# Error case: State backend failure
docker run -d \
  --name streaming-processor \
  -e STATE_BACKEND="redis" \
  -e REDIS_HOST="redis-cluster" \
  streaming-processor:latest
# If redis fails, processor stops processing events

Streaming failures typically manifest as event loss or processing delays rather than job completion.

Configuration Differences

Batch Job Configuration

# Batch-specific configuration keys
spec:
  template:
    spec:
      containers:
      - name: batch-processor
        env:
        # Time window for processing
        - name: BATCH_START_TIME
          value: "2024-01-01T00:00:00Z"
        - name: BATCH_END_TIME  
          value: "2024-01-01T01:00:00Z"
        # Retries for failed records
        - name: MAX_RETRIES
          value: "3"
        # Parallel processing
        - name: PARALLEL_THREADS
          value: "8"

Batch jobs are configured with time boundaries and retry strategies. They do not require state persistence across runs.

Streaming Job Configuration

# Streaming-specific configuration keys
spec:
  template:
    spec:
      containers:
      - name: stream-processor
        env:
        # Processing window size
        - name: STREAM_WINDOW_SIZE
          value: "60"
        # Window type (sliding, tumbling)
        - name: STREAM_WINDOW_TYPE
          value: "sliding"
        # State management
        - name: STATE_BACKEND
          value: "memory"
        # Processing timeout
        - name: PROCESSING_TIMEOUT_MS
          value: "5000"

Streaming jobs require windowing configuration and state management settings. They must be designed to handle partial failures gracefully.

Operational Differences

Monitoring Batch Jobs

Batch jobs are monitored by:

• Job completion status
• Data volume processed
• Error counts in logs

# Monitor batch job completion
kubectl get jobs
# Output shows job status
NAME                    COMPLETIONS   AGE
data-processing-job     1/1           2m

# Check specific job details
kubectl describe job data-processing-job

Batch monitoring focuses on whether the job completed successfully with expected data volumes.

Monitoring Streaming Jobs

Streaming jobs are monitored by:

• Event processing rate
• Latency metrics
• State consistency

# Check streaming processor logs for errors
docker logs streaming-processor | grep -i error

# Monitor processing throughput
kubectl top pods streaming-pipeline

Streaming monitoring focuses on continuous health and performance metrics rather than job completion.

Decision Matrix

Choose batch processing when:

• Data arrives in predictable chunks
• You can accept several minutes of delay
• Processing requires complex transformations
• Data quality checks are essential

Choose streaming processing when:

• Real-time data consumption is required
• Latency must be under a few seconds
• Stateful processing is needed
• Continuous data flow is expected

Implementation Patterns

Batch Processing Pattern

# Standard batch processing workflow
# 1. Determine batch window
BATCH_START="2024-01-01T00:00:00Z"
BATCH_END="2024-01-01T01:00:00Z"

# 2. Process all data in window
./process-batch.sh --start-time "$BATCH_START" --end-time "$BATCH_END"

# 3. Validate results
./validate-batch.sh --start-time "$BATCH_START" --end-time "$BATCH_END"

Batch jobs are scheduled and executed as discrete units with clear boundaries.

Streaming Processing Pattern

# Standard streaming processing workflow
# 1. Start processor
./start-streaming-processor.sh

# 2. Monitor continuously
while true; do
  if [ ! -f /tmp/processor-running ]; then
    echo "Processor failed, restarting..."
    ./start-streaming-processor.sh
  fi
  sleep 30
done

Streaming jobs run continuously and require monitoring for liveness and performance.

Sharp Edges

Silent Batch Failures

Batch jobs may complete successfully even when they process no data:

# This job completes but processes zero records
kubectl create job --from=cronjob/report-generator my-job
# Job runs and exits with code 0, but no processing occurs

The job's success does not indicate data was processed.

Streaming Backpressure

Streaming systems may silently drop events when:

• State storage is unavailable
• Processing exceeds window boundaries
• Memory limits are reached

# Example of silent failure
docker run -d \
  --name streaming-processor \
  -e STATE_BACKEND="redis" \
  streaming-processor:latest
# If redis becomes unresponsive, events are dropped without error

Streaming systems must implement explicit error handling for state failures.

Configuration Confusion

Batch and streaming configurations can be confused:

# Common mistake: Using batch configuration for streaming
spec:
  template:
    spec:
      containers:
      - name: stream-processor
        env:
        # Wrong: Batch window settings
        - name: BATCH_START_TIME
          value: "2024-01-01T00:00:00Z"
        - name: BATCH_END_TIME
          value: "2024-01-01T01:00:00Z"
        # Should use streaming settings instead
        # - name: STREAM_WINDOW_SIZE
        #   value: "60"

Using batch configuration in streaming contexts can cause silent failures or unexpected behavior.

The fundamental distinction between batch and streaming lies in how data is processed, monitored, and recovered from failures. Batch jobs are discrete units that process complete datasets, while streaming jobs are continuous processes that handle individual events with state management.

This page was rewritten on 15 October 2026. It replaced a templated version that was not specific to its own title. How these pages are written and checked. If anything here is wrong, tell us at [email protected] and we will correct it.