Software Alternatives, Accelerators & Startups

Apache Flink VS ParseHub

Compare Apache Flink VS ParseHub and see what are their differences

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

ParseHub logo ParseHub

ParseHub is a free web scraping tool. With our advanced web scraper, extracting data is as easy as clicking the data you need.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • ParseHub Landing page
    Landing page //
    2021-09-12

Apache Flink videos

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

ParseHub videos

ParseHub Tutorial: Scrape Ratings and Reviews from a Website

More videos:

  • Tutorial - ParseHub Tutorial: Scraping Product Details from Amazon

Category Popularity

0-100% (relative to Apache Flink and ParseHub)
Big Data
100 100%
0% 0
Web Scraping
0 0%
100% 100
Stream Processing
100 100%
0% 0
Data Extraction
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 Flink and ParseHub

Apache Flink Reviews

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ParseHub Reviews

Best Data Scraping Tools
Parsehub is a fantastic tool for people who want to extract data from websites without coding. It is used widely by data analysts, journalists, data scientists, and many fields. Parse Hub is easier to use; you can click on the data that you are working on to build a web scraper, which then exports the data in excel format or JSON.

Social recommendations and mentions

Based on our record, Apache Flink should be more popular than ParseHub. It has been mentiond 30 times since March 2021. 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 Flink mentions (30)

  • Show HN: Restate, low-latency durable workflows for JavaScript/Java, in Rust
    Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 22 days ago
  • Array Expansion in Flink SQL
    I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / about 1 month ago
  • Show HN: An SQS Alternative on Postgres
    You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / about 2 months ago
  • Top 10 Common Data Engineers and Scientists Pain Points in 2024
    Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 3 months ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 5 months ago
View more

ParseHub mentions (3)

  • Home Depot price data using IMPORTXML?
    I've heard some folks have success with "parsehub.com", though I once tried it for a project and found it a bit intimidating... Source: over 2 years ago
  • Free for dev - list of software (SaaS, PaaS, IaaS, etc.)
    Parsehub.com — Extract data from dynamic sites, turn dynamic websites into APIs, 5 projects free. - Source: dev.to / almost 3 years ago
  • Turn any website into an API with no code
    Parsehub is a powerful web scraping GUI tool for efficient fetching and manipulating data from any webpage. It helps you create an API output for a given website. You can even sanitize your content by using regex or replace function. So the input is a URL and the output is a structured json file. - Source: dev.to / about 3 years ago

What are some alternatives?

When comparing Apache Flink and ParseHub, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

import.io - Import. io helps its users find the internet data they need, organize and store it, and transform it into a format that provides them with the context they need.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Apify - Apify is a web scraping and automation platform that can turn any website into an API.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.