Observable VS Amazon EMR

Observable Reviews

Amazon EMR Reviews

Observable mentions (286)

• I'm writing a new vector search SQLite Extension

  Could this be implemented in Rust? Does that project (sqlite-loadable-rs) support WASM? https://observablehq.com/@asg017/introducing-sqlite-loadable-rs. - Source: Hacker News / 10 days ago

• Show HN: An extension to track your Wikipedia adventures

  Have you tried out a tangled-tree visualization? [1] I've found it to be super useful when visualizing these sorts of relationships in a compact way. [1] https://observablehq.com/@nitaku/tangled-tree-visualization-ii. - Source: Hacker News / 10 days ago

• A feature-rich front-end drag-and-drop component library

  Maybe I'm easy to impress, but I always stop and play around with the nested tree example when I come across Sortable. It works so flawlessly, and feels very tuned to mobile dnd. It even works to arrange (and reflow) inline spans in a paragraph! I have yet to come across this functionality in a text editor.. [0]: https://observablehq.com/@dleeftink/sortable-playground. - Source: Hacker News / 18 days ago

• Parquet-WASM: Rust-based WebAssembly bindings to read and write Parquet data

  Arrow JS is just ArrayBuffers underneath. You do want to amortize some operations to avoid unnecessary conversions. I.e. Arrow JS stores strings as UTF-8, but native JS strings are UTF-16 I believe. Arrow is especially powerful across the WASM <--> JS boundary! In fact, I wrote a library to interpret Arrow from Wasm memory into JS without any copies [0]. (Motivating blog post [1]) [0]:... - Source: Hacker News / 20 days ago

• A flat map with the least error possible: The Gott-Goldberg-Vanderbei projection

  Here’s the D3 implementation (which is just an interrupted azimuthal equidistant projection): https://observablehq.com/@d3/azimuthal-equidistant-hemispheres. - Source: Hacker News / 21 days ago

Amazon EMR mentions (10)

• 5 Best Practices For Data Integration To Boost ROI And Efficiency

  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

• What compute service i should use? Advice for a duck-tape kind of guy

  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

• Processing a large text file containing millions of records.

  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

• How to use Spark and Pandas to prepare big data

  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

• Beginner building a Hadoop cluster

  Check out https://aws.amazon.com/emr/. Source: about 2 years ago