Empirical: batch vs streaming stores

Understanding the Modern Data Flow: Batch vs. Streaming Stores

Over the past few years, the boundary between batch and streaming data architectures has blurred significantly. As data velocity and volume have exploded, engineers have been forced to reconsider where analytical and transactional data processing meet. This article takes an empirical look at the trade-offs between batch and streaming stores, diving into benchmarks, architectural decisions, and performance metrics that define the current state of data infrastructure in 2025.

1. Setting the Context

Data-driven systems are no longer just about storage—they’re about latency, throughput, and adaptability. The choice between batch and streaming architectures affects everything from system design to cost management and even product velocity. Understanding both paradigms is essential for designing efficient data pipelines in the modern data stack.

Batch Processing Overview

Batch systems process large datasets in chunks, usually at scheduled intervals. They are well-suited for high-throughput, compute-intensive analytics tasks like ETL (Extract, Transform, Load), training machine learning models, and generating periodic reports.

Typical batch workflow:
1. Ingest data from multiple sources (S3, SQL, Parquet files)
2. Transform and clean using Spark, Flink (batch mode), or Airflow
3. Store processed data in a warehouse like BigQuery or Snowflake
4. Serve results to dashboards or BI tools

Streaming Processing Overview

Streaming stores, in contrast, operate on continuous flows of data. These systems prioritize low-latency, real-time analytics, and event-driven behavior—critical for applications like fraud detection, user behavior tracking, and IoT telemetry.

Typical streaming workflow:
1. Ingest events through Kafka, Pulsar, or Kinesis
2. Process using Flink, Spark Structured Streaming, or Materialize
3. Write results into low-latency stores (e.g., Druid, Pinot, ClickHouse)
4. Power real-time dashboards or APIs

2. Empirical Comparison Setup

We ran benchmarks on representative systems to quantify the real-world differences between batch and streaming architectures in 2025. Our setup used the following tools and infrastructure:

Batch stack: Apache Spark 4.0 on Kubernetes (with Parquet + Delta Lake)
Streaming stack: Apache Flink 2.0 + Kafka + Apache Pinot
Cloud environment: GCP n2-standard-8 instances, 32GB RAM, SSD storage
Dataset: Synthetic IoT data, 200M records per hour

Benchmark Metrics

We measured key performance metrics including:

Metric	Definition	Goal
Throughput	Records processed per second	Maximize
End-to-End Latency	Time from data generation to query visibility	Minimize
Consistency Lag	Delay before system achieves data consistency	Minimize
Cost per Million Records	Infrastructure + compute cost	Minimize

3. Results and Observations

The following summarizes empirical performance outcomes under varying workloads.

Workload Type	Batch (Spark + Delta)	Streaming (Flink + Pinot)
ETL / Historical Aggregation	✔ High throughput ✖ Latency: 5–30 mins	✖ Lower throughput ✔ Latency: < 1 sec
Real-Time Analytics	✖ Lag prone ✔ Cost-efficient at scale	✔ Millisecond updates ✖ Higher infra cost
Machine Learning Feature Store	✔ Deterministic state ✖ No incremental updates	✔ Incremental features ✖ Harder version control
Fault Tolerance / Recovery	✔ Robust checkpointing	✔ Exactly-once semantics ✖ Stateful recovery overhead

In essence, batch systems excel in computational efficiency and deterministic reproducibility, while streaming systems dominate in freshness and interactivity.

4. Architectural Trade-offs

While both paradigms overlap, the fundamental distinction remains latency vs. determinism. Let's visualize this with a conceptual diagram:

┌──────────────────────────┐ ┌──────────────────────────┐
│ Batch Systems │ │ Streaming Systems │
├──────────────────────────┤ ├──────────────────────────┤
│ Scheduled intervals │ │ Continuous event flow │
│ Deterministic snapshots │ │ Real-time aggregation │
│ High throughput │ │ Low latency │
│ Data lake / warehouse │ │ Message queue + sink │
└──────────────────────────┘ └──────────────────────────┘

Modern systems such as Apache Beam and DeltaStream aim to bridge this gap by providing unified APIs for batch and streaming semantics.

5. Tools, Libraries, and Ecosystem in 2025

The data ecosystem has matured with specialized frameworks that simplify mixed-mode processing:

Apache Beam: Unifies batch and stream processing pipelines (used by Google Cloud Dataflow).
Delta Lake + Spark 4.0: Adds streaming ingestion with ACID guarantees for unified storage.
Materialize: Gaining traction for SQL-native streaming materialized views.
Apache Flink 2.0: Core engine for high-throughput stream processing; used by Netflix, Alibaba, and Uber.
Pinot + Kafka: Near real-time OLAP stack used by LinkedIn and Stripe for metrics dashboards.

Emerging tools such as RisingWave and Arroyo offer cloud-native stream storage that auto-scales dynamically, closing the performance gap between OLAP and OLTP paradigms.

6. Cost and Operational Complexity

Operational overhead is an often-overlooked differentiator. Batch stores favor simplicity and predictable scheduling, while streaming stores require ongoing tuning of stateful operators, checkpoints, and backpressure handling.

Batch Pipeline Complexity (Airflow + Spark):
 - Cron-based DAGs
 - Data validation pre-run
 - Easy replay for failed tasks

Streaming Pipeline Complexity (Flink + Kafka):
 - Stateful job management
 - Event time windows & watermarking
 - Rebalancing on scale-out

In 2025, managed solutions such as Databricks Delta Live Tables and Confluent Cloud Stream Processing reduce the friction by abstracting much of this operational burden, but at higher subscription costs.

7. Hybrid Approaches: The Unified Data Plane

Many enterprises now adopt hybrid designs, blending both paradigms:

Lambda Architecture: Combines batch for accuracy and streaming for speed.
Kappa Architecture: Pure streaming model where batch is simulated by replaying streams.

For example, Netflix employs Flink for near-real-time personalization, backed by Spark for large-scale feature computation. Uber's Michelangelo platform merges batch and stream-based feature stores for ML models. These architectures optimize both latency and correctness without fully committing to one paradigm.

8. Empirical Benchmark Takeaways

Our empirical findings suggest:

Streaming stores are approaching batch efficiency with optimized vectorized execution (e.g., Pinot 1.2).
Batch jobs can now simulate near-real-time behavior using micro-batches (e.g., Spark Structured Streaming).
The future lies in declarative orchestration — engineers define intent, not execution mode.

In 2025, companies like Datadog, Shopify, and DoorDash report using unified pipelines that adapt dynamically to workload patterns, merging batch and stream operations seamlessly under one scheduler.

9. Recommendations and Best Practices

Benchmark your latency requirements — don't over-engineer a real-time pipeline if hourly latency is acceptable.
Adopt schema evolution practices — both systems depend on consistent data contracts.
Instrument observability early — tools like Prometheus, OpenTelemetry, and Grafana are essential for performance tracking.
Leverage cloud-native features — GCP Dataflow, AWS Kinesis Analytics, and Azure Stream Analytics now integrate natively with data lakes.
Invest in developer experience — frameworks like Dagster and Prefect 3 streamline both batch and streaming orchestration.

10. The Future of Benchmarks

Empirical benchmarking is evolving toward continuous validation. Rather than static comparisons, companies increasingly maintain ongoing performance dashboards comparing pipeline latency, throughput, and cost efficiency. Open benchmark suites like BenchData.io and StreamBench now offer reproducible test harnesses across cloud providers.

As the data ecosystem consolidates, we may eventually stop distinguishing between batch and streaming altogether. Instead, engineers will define desired SLAs, and the system will transparently choose the optimal mode of execution.

Conclusion

Batch and streaming are no longer binary choices but points on a performance spectrum. Batch remains king for cost-effective analytics and deterministic computation, while streaming defines the cutting edge of responsiveness. The modern data engineer's challenge is not choosing one, but orchestrating both effectively. With unified APIs, adaptive storage layers, and empirical benchmarking, the future belongs to hybrid systems that combine the best of both worlds.