Software Alternatives & Reviews

memcached VS Google Cloud Dataflow

Compare memcached VS Google Cloud Dataflow and see what are their differences

memcached logo memcached

High-performance, distributed memory object caching system

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
  • memcached Landing page
    Landing page //
    2023-07-23
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

memcached videos

Course Preview: Using Memcached and Varnish to Speed Up Your Linux Web App

Google Cloud Dataflow videos

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to memcached and Google Cloud Dataflow)
Databases
100 100%
0% 0
Big Data
0 0%
100% 100
NoSQL Databases
100 100%
0% 0
Data Dashboard
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 memcached and Google Cloud Dataflow

memcached Reviews

Redis vs. KeyDB vs. Dragonfly vs. Skytable | Hacker News
Quick ask: I don’t see “some” of the other offering out there like MemCached… what was the criteria used to select these? I don’t see any source of how the test where run, specs of the systems, how the DB where set up, etc. Would be very valuable to have in order to attempt to re-validate these test on our own platform. I also came back and saw some of your updates...
Memcached vs Redis - More Different Than You Would Expect
So knowing how the difference between Redis and memcached in-memory usage, lets see what this means. Memcached slabs once assigned never change their size. This means it is possible to poison your memcached cluster and really waste memory. If you load your empty memcached cluster with lots of 1 MB items, then all of the slabs will be allocated to that size. Adding a 80 KB...
Redis vs. Memcached: In-Memory Data Storage Systems
Memcached itself does not support distributed mode. You can only achieve the distributed storage of Memcached on the client side through distributed algorithms such as Consistent Hash. The figure below demonstrates the distributed storage implementation schema of Memcached. Before the client side sends data to the Memcached cluster, it first calculates the target node of the...
Source: medium.com
Why Redis beats Memcached for caching
Both Memcached and Redis are mature and hugely popular open source projects. Memcached was originally developed by Brad Fitzpatrick in 2003 for the LiveJournal website. Since then, Memcached has been rewritten in C (the original implementation was in Perl) and put in the public domain, where it has become a cornerstone of modern Web applications. Current development of...

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Based on our record, memcached should be more popular than Google Cloud Dataflow. It has been mentiond 29 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.

memcached mentions (29)

  • Consistent Hashing: An Overview and Implementation in Golang
    Distributed caching Consistent hashing is a popular technique for distributed caching systems like Memcached and Dynamo. In these systems, the caches are distributed across many servers. When a cache miss occurs, consistent hashing is used to determine which server contains the required data. This allows the overall cache to scale to handle more requests. - Source: dev.to / 6 days ago
  • How to choose the right type of database
    Memcached: A simple, open-source, distributed memory object caching system primarily used for caching strings. Best suited for lightweight, non-persistent caching needs. - Source: dev.to / 2 months ago
  • A Developer's Journal: Simplifying the Twelve-Factor App
    Stores session state in a session store like Memcached or Redis. - Source: dev.to / 5 months ago
  • Django Caching 101: Understanding the Basics and Beyond
    Django supports using Memcached as a cache backend. Memcached is a high-performance, distributed memory caching system that can be used to store cached data across multiple servers. - Source: dev.to / 10 months ago
  • Node.js server-side authentication: Tokens vs. JWT
    In server-side authentication, the session state is stored on the server-side, which can be scaled horizontally across multiple servers using tools like Redis or Memcached. - Source: dev.to / 10 months ago
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Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 1 year ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 1 year ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 1 year ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 1 year ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 2 years ago
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What are some alternatives?

When comparing memcached and Google Cloud Dataflow, you can also consider the following products

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Aerospike - Aerospike is a high-performing NoSQL database supporting high transaction volumes with low latency.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?