Apache Spark vs Hadoop vs Flink vs Superset vs Kafka

RajeshKumar1 · 18 Mar

Apache Spark vs Apache Hadoop vs Apache Flink vs Apache Superset vs Apache Kafka

These tools are not conflicting but complementary—each serves a different purpose in a big data ecosystem. Below, I explain how they work together in a workflow, their best use cases, and when to use each.

How They Work Together: Workflow

Step 1: Data Ingestion & Streaming (Kafka)

Apache Kafka collects, buffers, and transmits data streams in real-time.
Works as a message broker to decouple producers and consumers.
Example: Logs, IoT sensor data, transactions, or user interactions are streamed into Kafka.

Step 2: Data Storage (Hadoop)

Apache Hadoop (HDFS) stores massive volumes of data.
Can handle structured, semi-structured, and unstructured data.
Example: Kafka sends raw data to HDFS for long-term storage.

Step 3: Data Processing & Analytics (Spark/Flink)

Apache Spark processes large-scale batch and real-time data in memory.
Apache Flink processes real-time streaming data with low latency.
Example: Spark performs batch analytics, while Flink handles real-time fraud detection.

Step 4: Data Querying & Visualization (Superset)

Apache Superset provides a BI dashboard to visualize data.
Supports SQL-based queries over processed datasets.
Example: Business teams use Superset to analyze processed data and gain insights.

Primary Use Cases of Each Tool

| Tool | Primary Function | Best for |
|----------|----------------------|--------------|
| Apache Kafka | Real-time data streaming & message queue | Data pipelines, event-driven systems, log aggregation |
| Apache Hadoop | Distributed storage & batch processing | Big data storage, ETL pipelines, archival storage |
| Apache Spark | In-memory batch & real-time data processing | Big data analytics, machine learning, ETL, batch jobs |
| Apache Flink | Real-time stream processing | Event time processing, real-time analytics, fraud detection |
| Apache Superset | Data visualization & BI dashboard | Interactive dashboards, data exploration |

When to Use Each Tool (Detailed Scenarios)

Use Apache Kafka When:

You need real-time streaming and event-driven architecture.
You want to decouple microservices in a distributed system.
You need to ingest data from multiple sources (e.g., logs, IoT, financial transactions).
Example: Uber uses Kafka to handle millions of ride events per second.

Use Apache Hadoop When:

You need cheap and scalable storage for huge datasets.
You have historical big data that needs processing later.
You are running batch ETL pipelines.
Example: Facebook stores user data in Hadoop for analysis.

Use Apache Spark When:

You need fast in-memory batch processing.
You want to run complex machine learning models at scale.
You need SQL-based querying over big data.
Example: Netflix uses Spark for real-time recommendation systems.

Use Apache Flink When:

You need real-time event-driven processing.
You need low-latency stream processing (better than Spark Streaming).
You are working with IoT, financial transactions, or monitoring logs.
Example: Alibaba uses Flink for real-time fraud detection.

Use Apache Superset When:

You want to visualize big data from sources like Hadoop, Kafka, or Spark.
You need an open-source alternative to Tableau/Power BI.
You need an interactive SQL-based BI tool.
Example: Airbnb uses Superset for internal business analytics.

Final Thoughts

These tools complement each other:

Kafka → Hadoop → Spark/Flink → Superset forms a complete data pipeline.
Use Kafka for streaming, Hadoop for storage, Spark/Flink for processing, and Superset for visualization.
Choose Spark for batch jobs, Flink for real-time analytics.