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.
Try Hevo today and get your fully managed data pipelines up and running in just a few minutes.
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Amazon EMR might be a bit more popular than Hevo Data. We know about 10 links to it since March 2021 and only 8 links to Hevo Data. 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
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
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