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Apache Kafka VS Extractor API

Compare Apache Kafka VS Extractor API and see what are their differences

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Apache Kafka logo Apache Kafka

Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

Extractor API logo Extractor API

Extract clean text from thousands of articles with a simple API request or use our visual web tool - we'll handle IP rotation, retries and everything else. Features include news search, translation, and ML-powered text extraction.
  • Apache Kafka Landing page
    Landing page //
    2022-10-01
  • Extractor API Landing page
    Landing page //
    2023-07-11

Features

IP Rotation & JS Rendering

We automatically apply IP rotation and retries to every request (Free Plan included), and all our paid plans allow you to render JavaScript before extraction.

Search Country News

Free and paid plans can search the world's news with our News Search endpoint. Every request returns up to 100 news items, including metadata. Collect the URLs - then extract clean text with our Extractor endpoint.

Clean Text & Metadata

Extract clean text, HTML, image and video links, authors, title, publication date, html, and raw text. Choose only the fields you need.

API Not Required

You can extract data from up to 1,000 URLs at a time using our online visual extractor - not just the API. The visual extractor is included in all plans.

Store Your Results

Both the API and the visual extractor allow you to store your results in Jobs. Assign your target URLs a job name, then see their progress online or programmatically. Once the job is done, you can retrieve the results any time.

Translate Extracted Text

All paid accounts are able to translate to and from 55 languages. Swahili to English, Vietnamese to French, or anything you want - extract clean text and translate it with a single API call.

Extractor API

$ Details
freemium
Platforms
Windows Browser Web Android iOS Mac OSX Google Chrome Linux Firefox Cross Platform REST API Safari JavaScript iPhone Chrome OS Internet Explorer Windows Phone Python Node JS Ruby Java C PHP .Net Go Swift C++ Docker ReactJS TypeScript
Release Date
2020 March

Apache Kafka features and specs

  • High Throughput
    Kafka is capable of handling thousands of messages per second due to its distributed architecture, making it suitable for applications that require high throughput.
  • Scalability
    Kafka can easily scale horizontally by adding more brokers to a cluster, making it highly scalable to serve increased loads.
  • Fault Tolerance
    Kafka has built-in replication, ensuring that data is replicated across multiple brokers, providing fault tolerance and high availability.
  • Durability
    Kafka ensures data durability by writing data to disk, which can be replicated to other nodes, ensuring data is not lost even if a broker fails.
  • Real-time Processing
    Kafka supports real-time data streaming, enabling applications to process and react to data as it arrives.
  • Decoupling of Systems
    Kafka acts as a buffer and decouples the production and consumption of messages, allowing independent scaling and management of producers and consumers.
  • Wide Ecosystem
    The Kafka ecosystem includes various tools and connectors such as Kafka Streams, Kafka Connect, and KSQL, which enrich the functionality of Kafka.
  • Strong Community Support
    Kafka has strong community support and extensive documentation, making it easier for developers to find help and resources.

Possible disadvantages of Apache Kafka

  • Complex Setup and Management
    Kafka's distributed nature can make initial setup and ongoing management complex, requiring expert knowledge and significant administrative effort.
  • Operational Overhead
    Running Kafka clusters involves additional operational overhead, including hardware provisioning, monitoring, tuning, and scaling.
  • Latency Sensitivity
    Despite its high throughput, Kafka may experience increased latency in certain scenarios, especially when configured for high durability and consistency.
  • Learning Curve
    The concepts and architecture of Kafka can be difficult for new users to grasp, leading to a steep learning curve.
  • Hardware Intensive
    Kafka's performance characteristics often require dedicated and powerful hardware, which can be costly to procure and maintain.
  • Dependency Management
    Managing Kafka's dependencies and ensuring compatibility between versions of Kafka, Zookeeper, and other ecosystem tools can be challenging.
  • Limited Support for Small Messages
    Kafka is optimized for large throughput and can be inefficient for applications that require handling a lot of small messages, where overhead can become significant.
  • Operational Complexity for Small Teams
    Smaller teams might find the operational complexity and maintenance burden of Kafka difficult to manage without a dedicated operations or DevOps team.

Extractor API features and specs

  • Robust API
    We handle IP rotation, retries and JavaScript rendering - you get clean text.
  • News Search
    Search the world's news with a single API call - up to 100 results per request.
  • Extract Everything
    Extract clean text, translate it into 50+ languages and get tons of metadata.
  • Visual Extraction
    Don't want to use the API? Use our visual online tool to paste or upload URLs!
  • Persistent Jobs
    Both our API and online tool allow you to save extracted text to your Jobs page.
  • Quick Start
    Check out the Getting Started guide for a quick overview of the API and the FAQ for more info.

Apache Kafka videos

Apache Kafka Tutorial | What is Apache Kafka? | Kafka Tutorial for Beginners | Edureka

More videos:

  • Review - Apache Kafka - Getting Started - Kafka Multi-node Cluster - Review Properties
  • Review - 4. Apache Kafka Fundamentals | Confluent Fundamentals for Apache Kafkaยฎ
  • Review - Apache Kafka in 6 minutes
  • Review - Apache Kafka Explained (Comprehensive Overview)
  • Review - 2. Motivations and Customer Use Cases | Apache Kafka Fundamentals

Extractor API videos

Extractor API - Visual Extractor Demo

Category Popularity

0-100% (relative to Apache Kafka and Extractor API)
Stream Processing
100 100%
0% 0
Data Extraction
0 0%
100% 100
Data Integration
100 100%
0% 0
Web Scraping API
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Kafka and Extractor API

Apache Kafka Reviews

Best ETL Tools: A Curated List
Debezium is an open-source Change Data Capture (CDC) tool that originated from RedHat. It leverages Apache Kafka and Kafka Connect to enable real-time data replication from databases. Debezium was partly inspired by Martin Kleppmannโ€™s "Turning the Database Inside Out" concept, which emphasized the power of the CDC for modern data pipelines.
Source: estuary.dev
Best message queue for cloud-native apps
If you take the time to sort out the history of message queues, you will find a very interesting phenomenon. Most of the currently popular message queues were born around 2010. For example, Apache Kafka was born at LinkedIn in 2010, Derek Collison developed Nats in 2010, and Apache Pulsar was born at Yahoo in 2012. What is the reason for this?
Source: docs.vanus.ai
Are Free, Open-Source Message Queues Right For You?
Apache Kafka is a highly scalable and robust messaging queue system designed by LinkedIn and donated to the Apache Software Foundation. It's ideal for real-time data streaming and processing, providing high throughput for publishing and subscribing to records or messages. Kafka is typically used in scenarios that require real-time analytics and monitoring, IoT applications,...
Source: blog.iron.io
10 Best Open Source ETL Tools for Data Integration
It is difficult to anticipate the exact demand for open-source tools in 2023 because it depends on various factors and emerging trends. However, open-source solutions such as Kubernetes for container orchestration, TensorFlow for machine learning, Apache Kafka for real-time data streaming, and Prometheus for monitoring and observability are expected to grow in prominence in...
Source: testsigma.com
11 Best FREE Open-Source ETL Tools in 2024
Apache Kafka is an Open-Source Data Streaming Tool written in Scala and Java. It publishes and subscribes to a stream of records in a fault-tolerant manner and provides a unified, high-throughput, and low-latency platform to manage data.
Source: hevodata.com

Extractor API Reviews

Creating an Automated Text Extraction Workflow โ€” Part 1
The 600 lbs gorilla, Diffbot, comes with a swath of solid APIs but starts at $300, which is ridiculous if youโ€™re just extracting text. Scrapinghubโ€™s News API, Extractor API, and plenty more are better priced if you want an affordable alternative; plus, Extractor API includes a visual online tool for extracting hundreds of articles at once, if you want to do things via UI.
Source: medium.com

Social recommendations and mentions

Based on our record, Apache Kafka seems to be a lot more popular than Extractor API. While we know about 155 links to Apache Kafka, we've tracked only 3 mentions of Extractor API. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Kafka mentions (155)

  • Building Kafka Producer-Consumer Using Go and Docker
    Kafka is a distributed streaming platform used to build real-time data pipelines and streaming applications. It allows producers to send messages to topics, which are then consumed by various consumers, making it ideal for event-driven architectures. - Source: dev.to / about 2 months ago
  • 7 Free Tools for Data Pipeline Reconciliation and Cross-Source Validation
    Apache Kafka is the most widely used distributed event streaming platform and the standard transport layer for event-driven reconciliation architectures. - Source: dev.to / 3 months ago
  • How to Build a Dead Letter Queue System for Reliable Data Processing
    For message-queue-based pipelines: RabbitMQ has native DLQ support through dead letter exchanges. Messages that exceed their retry count or their time-to-live are automatically routed to a designated DLQ exchange. Apache Kafka does not have native DLQ semantics, but the standard pattern is to write failed records to a dedicated topic (-dlq by convention) and include the failure metadata in the record headers. - Source: dev.to / 3 months ago
  • Idempotency in Data Pipelines: How to Prevent Duplicate Records
    Upsert with timestamp tracking. Keep the upsert approach but track which time windows have been fully processed. On retry, skip windows that are marked complete and reprocess only windows that failed mid-run. The Kafka documentation covers offset management patterns that implement this for stream-based pipelines. - Source: dev.to / 3 months ago
  • Real-Time Fraud Detection in Java with Kafka Streams and Vector Similarity
    Apache Kafka allows the payment service to publish a transaction event to a topic, without knowing who will consume it. The fraud service, the notification service, and any other interested component can subscribe to that topic independently:. - Source: dev.to / 3 months ago
View more

Extractor API mentions (3)

  • webscraping for sentiment analysis
    Take a look at our webscraping API - should be able to do what you need it to do. https://extractorapi.com/. Source: about 3 years ago
  • Using ChatGPT to build a database from web scraping?
    If you want to make it easier, we built a text extraction tool that can fit a number of use cases https://extractorapi.com/ people are using it instead of GPT for the scraping and then in certain cases feeding the data that comes from here to some broader app/use case. Just another route! Source: about 3 years ago
  • Text Extraction Tool for Training your ChatGPT app
    I'm looking for input on our tool as a pipeline for text data into your own ChatGPT use case. We know you can use ChatGPT API to do the same task, but we've found that to be costly and time-consuming for the text extraction/scraping portion. We've built a cost-effective and quick tool, Extractor API, for that use case. Would love to see what others are using outside of just relying on ChatGPT for text extraction. Source: about 3 years ago

What are some alternatives?

When comparing Apache Kafka and Extractor API, you can also consider the following products

StatCounter - StatCounter is a simple but powerful real-time web analytics service that helps you track, analyse and understand your visitors so you can make good decisions to become more successful online.

Microlink - Extract structured data from any website

Histats - Start tracking your visitors in 1 minute!

Schema API - Extract structured content from the semantic web

AFSAnalytics - AFSAnalytics.

CRX Extractor - Get any Chrome Extension source code. Learn and hack!