Apache Flink vs Apache Hadoop

Struggling to choose between Apache Flink and Apache Hadoop? Both products offer unique advantages, making it a tough decision.

Apache Flink is a Development solution with tags like opensource, stream-processing, realtime, distributed, scalable.

It boasts features such as Distributed stream data processing, Event time and out-of-order stream processing, Fault tolerance with checkpointing and exactly-once semantics, High throughput and low latency, SQL support, Python, Java, Scala APIs, Integration with Kubernetes and pros including High performance and scalability, Flexible deployment options, Fault tolerance, Exactly-once event processing semantics, Rich APIs for Java, Python, SQL, Can process bounded and unbounded data streams.

On the other hand, Apache Hadoop is a Ai Tools & Services product tagged with distributed-computing, big-data-processing, data-storage.

Its standout features include Distributed storage and processing of large datasets, Fault tolerance, Scalability, Flexibility, Cost effectiveness, and it shines with pros like Handles large amounts of data, Fault tolerant and reliable, Scales linearly, Flexible and schema-free, Commodity hardware can be used, Open source and free.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Apache Flink

Apache Flink

Apache Flink is an open-source stream processing framework that performs stateful computations over unbounded and bounded data streams. It offers high throughput, low latency, accurate results, and fault tolerance.

Categories:
Development Big Data & Analytics
opensource stream-processing realtime distributed scalable

Apache Flink Features

  1. Distributed stream data processing
  2. Event time and out-of-order stream processing
  3. Fault tolerance with checkpointing and exactly-once semantics
  4. High throughput and low latency
  5. SQL support
  6. Python, Java, Scala APIs
  7. Integration with Kubernetes

Pricing

  • Open Source
  • Pay-As-You-Go

Pros

High performance and scalability

Flexible deployment options

Fault tolerance

Exactly-once event processing semantics

Rich APIs for Java, Python, SQL

Can process bounded and unbounded data streams

Cons

Steep learning curve

Less out-of-the-box machine learning capabilities than Spark

Requires more infrastructure management than fully managed services

Apache Hadoop

Apache Hadoop

Apache Hadoop is an open source framework for storing and processing big data in a distributed computing environment. It provides massive storage and high bandwidth data processing across clusters of computers.

Categories:
Ai Tools & Services Big Data
distributed-computing big-data-processing data-storage

Apache Hadoop Features

  1. Distributed storage and processing of large datasets
  2. Fault tolerance
  3. Scalability
  4. Flexibility
  5. Cost effectiveness

Pricing

  • Open Source

Pros

Handles large amounts of data

Fault tolerant and reliable

Scales linearly

Flexible and schema-free

Commodity hardware can be used

Open source and free

Cons

Complex to configure and manage

Requires expertise to tune and optimize

Not ideal for low-latency or real-time data

Not optimized for interactive queries

Does not enforce schemas