data.world
Denodo
IBM Cloud Pak for Data
Teradata QueryGrid
Trustgrid Data Mesh Platform
Cinchy
Informatica Intelligent Data Platform
Zetaris Platform
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
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Based on our record, data.world should be more popular than Amazon EMR. It has been mentiond 24 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.
I'll be sure to check out data.world propose to use it if it makes sense, thanks. Source: 11 months ago
Just google qgis datasets. There are so so many interesting sets you will find. Check out qgis.org, or data.world for starters. Source: about 1 year ago
But, I'm also aware that there are dedicated platforms to catalog and share data (e.g. https://www.dolthub.com/, https://data.world/), and that uploading data on Github, in general, doesn't seem best practise. Source: about 1 year ago
The client is considering the 3 I mentioned, plus data.world. I need to research that one next. Microsoft Purview has already been considered. Source: over 1 year ago
Im looking for Christmas cost dataset by year and country, Im looking in the data.world and other web pages and I cant found anything. Source: over 1 year 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
Denodo - Denodo delivers on-demand real-time data access to many sources as integrated data services with high performance using intelligent real-time query optimization, caching, in-memory and hybrid strategies.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
IBM Cloud Pak for Data - Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Teradata QueryGrid - Data Fabric
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost