Software Alternatives, Accelerators & Startups

Apache Flink VS V7

Compare Apache Flink VS V7 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.

V7 logo V7

Pixel perfect image labeling for industrial, medical, and large scale dataset creation. Create ground truth 10 times faster.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • V7 Landing page
    Landing page //
    2023-08-06

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

V7 videos

Automated Image Labelling with Auto-Annotate - V7 Darwin

More videos:

  • Review - Annotation Basics (OLD) - V7 Darwin AI Academy
  • Review - Video Annotation - V7 Darwin

Category Popularity

0-100% (relative to Apache Flink and V7)
Big Data
100 100%
0% 0
Data Labeling
0 0%
100% 100
Stream Processing
100 100%
0% 0
Data Science And Machine Learning

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 V7

Apache Flink Reviews

We have no reviews of Apache Flink yet.
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V7 Reviews

Top Video Annotation Tools Compared 2022
V7 allows for collaboration and automated workflows, so you can reach human accuracy faster with 10x more training data. V7 offers features similar to Innotescus like
Source: innotescus.io

Social recommendations and mentions

Based on our record, Apache Flink seems to be a lot more popular than V7. While we know about 30 links to Apache Flink, we've tracked only 1 mention of V7. 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 / 14 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

V7 mentions (1)

  • Ask HN: Who is hiring? (December 2022)
    Https://v7labs.com We're automating humanity’s most important visual tasks from early cancer screening, to alzheimer's research, to giving sight to autonomous robots. Dealroom's most promising breakout company of 2022, Forbes top 20 ML startup of 2021. Just raise a $33m Series A and backed by AI heavyweights, including the creators of Keras, Elixir and leaders at DeepMindaand OpenAI. This month we're hiring for: -... - Source: Hacker News / over 1 year ago

What are some alternatives?

When comparing Apache Flink and V7, 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.

Labelbox - Build computer vision products for the real world

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

CloudFactory - Human-powered Data Processing for AI and Automation

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

SuperAnnotate - Empowering Enterprises with Custom LLM/GenAI/CV Models.