I love working with buffer its feature of scheduling makes me free for whole month. Best and easy tool to use.
Based on our record, Buffer seems to be a lot more popular than Google Cloud Dataproc. While we know about 56 links to Buffer, we've tracked only 3 mentions of Google Cloud Dataproc. 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.
For example look at buffer.com. Create simple web app where user will write a post, select target social networks to publish and time of publishing (like 8 hours from now). Source: 7 months ago
I use buffer to post to IG / Tiktok without visiting them. It works fairly well, although not perfect, but they seem to be working on it pretty consistently. Source: 12 months ago
Socialjobnow.com has published a comparison between Buffer and Later, two popular social media management tools used by businesses to schedule and automate their social media posts. The article provides an in-depth analysis of each tool's features, pricing, and benefits, offering valuable insights for businesses looking to optimize their social media strategy. Source: about 1 year ago
Have you looked at Buffer as a Hootsuite alternative - https://buffer.com/, or Agorapulse? - https://www.agorapulse.com/. Source: about 1 year ago
Used tools like Buffer and Planable to plan and schedule all your social media content in advance. Source: about 1 year ago
I have also a spark cluster created with google cloud dataproc. Source: about 1 year ago
Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / about 2 years ago
With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: over 2 years ago
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