Apache Hadoop vs Amazon Kinesis

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

Apache Hadoop is a Ai Tools & Services solution with tags like distributed-computing, big-data-processing, data-storage.

It boasts features such as Distributed storage and processing of large datasets, Fault tolerance, Scalability, Flexibility, Cost effectiveness and pros including Handles large amounts of data, Fault tolerant and reliable, Scales linearly, Flexible and schema-free, Commodity hardware can be used, Open source and free.

On the other hand, Amazon Kinesis is a Ai Tools & Services product tagged with realtime, ingestion, processing.

Its standout features include Real-time data streaming, Scalable data ingestion, Data processing through Kinesis Data Analytics, Integration with other AWS services, Serverless management, Data replay capability, and it shines with pros like Handles massive streams of data in real-time, Fully managed service, no servers to provision, Automatic scaling to match data flow, Integrates nicely with other AWS services, Replay capability enables reprocessing of data.

Feature	Apache Hadoop	Amazon Kinesis
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

Apache Hadoop

Description: 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.

Amazon Kinesis

Description: Amazon Kinesis is a managed service that allows for real-time streaming data ingestion and processing. It can ingest data streams from multiple sources, process the data, and route the results to various endpoints.

Key Features Comparison

Apache Hadoop Features

• Distributed storage and processing of large datasets
• Fault tolerance
• Scalability
• Flexibility
• Cost effectiveness

Amazon Kinesis Features

• Real-time data streaming
• Scalable data ingestion
• Data processing through Kinesis Data Analytics
• Integration with other AWS services
• Serverless management
• Data replay capability

Pros & Cons Analysis

Apache Hadoop

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

Amazon Kinesis

Pros

• Handles massive streams of data in real-time
• Fully managed service, no servers to provision
• Automatic scaling to match data flow
• Integrates nicely with other AWS services
• Replay capability enables reprocessing of data

Cons

• Can get expensive with high data volumes
• Complex to set up and manage
• Limits on maximum stream size and shard throughput
• No automatic data retention policies

Pricing Comparison

Apache Hadoop

• Open Source

Amazon Kinesis

• Pay-As-You-Go