Software Alternatives & Reviews

TimescaleDB VS Apache Flink

Compare TimescaleDB VS Apache Flink and see what are their differences

TimescaleDB logo TimescaleDB

TimescaleDB is a time-series SQL database providing fast analytics, scalability, with automated data management on a proven storage engine.

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
  • TimescaleDB Landing page
    Landing page //
    2023-09-23
  • Apache Flink Landing page
    Landing page //
    2023-10-03

TimescaleDB videos

Rearchitecting a SQL Database for Time-Series Data | TimescaleDB

More videos:

  • Review - Visualizing Time-Series Data with TimescaleDB and Grafana

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

Category Popularity

0-100% (relative to TimescaleDB and Apache Flink)
Databases
65 65%
35% 35
Big Data
0 0%
100% 100
Time Series Database
100 100%
0% 0
Stream Processing
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 TimescaleDB and Apache Flink

TimescaleDB Reviews

ClickHouse vs TimescaleDB
Recently, TimescaleDB published a blog comparing ClickHouse & TimescaleDB using timescale/tsbs, a timeseries benchmarking framework. I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB. Some of the claims about TimescaleDB made in their post are very bold, that made me even more curious. I thought it’d be a great...
4 Best Time Series Databases To Watch in 2019
The Guardian did a very nice article explaining on they went from MongoDB to PostgresSQL in the favor of scaling their architecture and encrypting their content at REST. As you can tell, big companies are relying on SQL-constraint systems (with a cloud architecture of course) to ensure system reliability and accessibility. I believe that PostgresSQL will continue to grow, so...
Source: medium.com
20+ MongoDB Alternatives You Should Know About
TimescaleDB If on the other hand you are storing time series data in MongoDB, then TimescaleDB might be a good fit.
Source: www.percona.com

Apache Flink Reviews

We have no reviews of Apache Flink yet.
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Social recommendations and mentions

Based on our record, Apache Flink should be more popular than TimescaleDB. It has been mentiond 27 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.

TimescaleDB mentions (5)

  • Ask HN: Does anyone use InfluxDB? Or should we switch?
    (:alert: I work for Timescale :alert:) It's funny, we hear this more and more "we did some research and landed on Influx and ... Help it's confusing". We actually wrote an article about what we think, you can find it here: https://www.timescale.com/blog/what-influxdb-got-wrong/ As the QuestDB folks mentioned if you want a drop in replacement for Influx then they would be an option, it kinda sounds that's not what... - Source: Hacker News / 6 months ago
  • Best small scale dB for time series data?
    If you like PostgreSQL, I'd recommend starting with that. Additionally, you can try TimescaleDB (it's a PostgreSQL extension for time-series data with full SQL support) it has many features that are useful even on a small-scale, things like:. Source: over 1 year ago
  • Quick n Dirty IoT sensor & event storage (Django backend)
    I have built a Django server which serves up the JSON configuration, and I'd also like the server to store and render sensor graphs & event data for my Thing. In future, I'd probably use something like timescale.com as it is a database suited for this application. However right now I only have a handful of devices, and don't want to spend a lot of time configuring my back end when the Thing is my focus. So I'm... Source: over 2 years ago
  • How fast and scalable is TimescaleDB compare to a NoSQL Database?
    I've seen a lot of benchmark results on timescale on the web but they all come from timescale.com so I just want to ask if those are accurate. Source: over 2 years ago
  • The State of PostgreSQL 2021 Survey is now open!
    Ryan from Timescale here. We (TimescaleDB) just launched the second annual State of PostgreSQL survey, which asks developers across the globe about themselves, how they use PostgreSQL, their experiences with the community, and more. Source: about 3 years ago

Apache Flink mentions (27)

  • 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 / 16 days 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 / 3 months ago
  • Go concurrency simplified. Part 4: Post office as a data pipeline
    Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago
  • Five Apache projects you probably didn't know about
    Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 4 months ago
  • Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
    Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 4 months ago
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What are some alternatives?

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

InfluxData - Scalable datastore for metrics, events, and real-time analytics.

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

Prometheus - An open-source systems monitoring and alerting toolkit.

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

OpenTSDB - OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase.

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