Real-Time Data Streaming Options on the Cloud

Modern distributed systems increasingly rely on event-driven and stream-based architectures. Instead of batch-oriented ETL pipelines, applications now ingest, process, and react to data in motion. Cloud providers offer managed streaming platforms that abstract infrastructure complexity while delivering low-latency, fault-tolerant data pipelines.

This article provides a technical overview of real-time data streaming options on the cloud, focusing on architecture, guarantees, scalability, and trade-offs.

Core Concepts in Real-Time Streaming

Before comparing services, it's important to understand the underlying primitives:

• Producers – Applications emitting events or records
• Brokers – Durable, distributed systems that store and route events
• Consumers – Applications processing streams
• Partitions / Shards – Units of parallelism and ordering
• Offsets / Checkpoints – Track consumption progress
• Delivery semantics – At-most-once, at-least-once, exactly-once

Most cloud streaming services are built around these abstractions.

1. Apache Kafka (Managed Cloud Deployments) Architecture Overview

Kafka is a distributed commit log. Data is written sequentially to partitions and replicated across brokers.

Key components:

• Topics → Partitioned logs
• Brokers → Store and replicate data
• ZooKeeper / KRaft → Metadata and cluster coordination
• Consumer groups → Parallel stream processing

Cloud Implementations

• Amazon MSK
• Confluent Cloud
• Azure Event Hubs (Kafka protocol)

Guarantees & Performance

• Ordering guaranteed within a partition
• At-least-once by default
• Exactly-once supported via transactions and idempotent producers
• Extremely high throughput (millions of events/sec)

Trade-offs

• Operational complexity (even when managed)
• Requires careful partition planning
• Higher cost at scale

Best For

• Event sourcing
• Microservice communication
• Log-based architectures
• Streaming backbones

2. Amazon Kinesis Data Streams Architecture Overview

Kinesis uses shards as the fundamental scaling unit. Each shard supports a fixed read/write throughput.

Components:

• Producers → PutRecords API
• Shards → Ordered sequence of records
• Consumers → Enhanced fan-out or polling
• Checkpoints → Stored externally (e.g., DynamoDB)

Guarantees & Performance

• Ordering per shard
• At-least-once delivery
• Sub-second latency
• Serverless scaling (with manual shard control or on-demand mode)

Trade-offs

• AWS-only
• Shard-based scaling can require tuning
• Less flexible ecosystem than Kafka

Best For

• Native AWS architectures
• Clickstream analytics
• Log ingestion pipelines

3. Google Cloud Pub/Sub Architecture Overview

Pub/Sub is a globally distributed messaging system with push and pull subscription models.

Components:

• Topics → Message streams
• Subscriptions → Message delivery config
• Ack deadlines → Control redelivery
• Dead-letter topics → Failure handling

Guarantees & Performance

• At-least-once delivery
• Ordering supported with ordering keys
• Horizontal auto-scaling
• Extremely low operational overhead

Trade-offs

• Limited long-term message retention
• Less control over partitioning
• Not ideal for replay-heavy workloads

Best For

• Event-driven microservices
• Serverless pipelines
• Cross-region event distribution

4. Azure Event Hubs + Azure Stream Analytics Architecture Overview

Azure Event Hubs is similar to Kafka/Kinesis, using partitions for scale and ordering.

Components:

• Event producers (AMQP, HTTPS, Kafka API)
• Partitions → Ordered streams
• Consumer groups → Parallel consumption
• Capture → Automatic storage in Blob/Data Lake

Guarantees & Performance

• At-least-once delivery
• Kafka-compatible endpoint
• Low-latency ingestion at high throughput

Trade-offs

• Azure ecosystem dependency
• Less flexible stream processing without additional services

Best For

• Telemetry ingestion
• Azure-native analytics
• Kafka migration to Azure

5. Stream Processing Engines (Flink & Spark Streaming)

Streaming platforms handle transport, but real-time value comes from processing.

Apache Flink

• True event-time processing
• Stateful stream operators
• Exactly-once semantics via checkpoints
• Low-latency windowing

Apache Spark Structured Streaming

• Micro-batch-based processing
• Unified batch + streaming APIs
• Easier learning curve
• Higher latency compared to Flink

Cloud Availability

• Databricks
• Amazon EMR
• Google Dataproc
• Azure HDInsight

Best For

• Fraud detection
• Sessionization
• Real-time aggregations
• ML feature pipelines

Architectural Patterns