Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows that result in a saving of ~10 hours of engineering time/week and 10x faster reporting, analytics, and decision making.
The platform supports 100+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 500 data-driven companies spread across 35+ countries trust Hevo for their data integration needs.
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Based on our record, Artifactory should be more popular than Hevo Data. It has been mentiond 20 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.
In a previous article, we used open-source Airbyte to create an ELT pipeline between SingleStoreDB and Apache Pulsar. We have also seen in another article several methods to ingest MongoDB JSON data into SingleStoreDB. In this article, we’ll evaluate a commercial ELT tool called Hevo Data to create a pipeline between MongoDB Atlas and SingleStoreDB Cloud. Switching to SingleStoreDB has many benefits, as described... - Source: dev.to / over 1 year ago
One of my customers just purchased Precisely to extract from their iSeries machines into Snowflake. Hevo can also do it. Source: over 1 year ago
I've been looking at Hevo data as well, and they certainly make the setup/maintenance a lot easier, but they have a latency of 5-10 minutes. What's the minimum lowest latency that can be achieved with aws for syncing dynamodb to redshift? Source: over 1 year ago
Don't decide on something without looking at Hevo - I've used this in two organisations now and can't speak more highly of it. Cheap, super simple to use, and super configurable if you want to get into the nitty gritty. Source: about 2 years ago
In that case you should try Hevo Data, you can start with their freemium model and see if it works well for you. Source: about 2 years ago
I kind of hate it, but Artifactory seems popular at companies: https://jfrog.com/artifactory/. Source: 10 months ago
When not providing all dependencies yourself, you might suffer from people deleting the packages you depend on (IMHO a very rare scenario). If it is really that critical (hint: usually it isn't), create a local mirror of Pypi (full or only the packages you need). Devpi, Artifactory, etc. Can do that or you just dump the necessary files into Cloud storage, so you have a backup. Source: about 1 year ago
Operate a pull-through cache registry, like Artifactory or the open source reference Docker registry. This will allow you to pull images from Docker Hub less frequently, improving your chances of staying under the anonymous usage limit. - Source: dev.to / about 1 year ago
Like suppose for a second that . . . Idk . . . a product team wants our ci workflows to start using Artifactory. Okay great, I don't know Artifactory integration but I'm going to tell them "Sure, I'll get right on that.". Source: about 1 year ago
If these "assets" have an independent release schedule I would treat them separately (especially if they are externally provided). If they are not built from source then treat them as artefacts, they don't belong in git. You can store the in an artefact repository (like Artifactory of Nexus) or (as u/nekokattt points out) in something like S3. Source: over 1 year ago
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