AWS Data Wrangler VS Apache Spark

AWS Data Wrangler Reviews

Apache Spark Reviews

AWS Data Wrangler mentions (4)

• Read files from s3 using Pandas/s3fs or AWS Data Wrangler?

  I had no problem with awswrangler (https://github.com/aws/aws-sdk-pandas) and it supports reading and writing partitions which was really helpful and a few other optimizations that made it a great tool. Source: 5 months ago

• Redshift API vs. other ways to connect?

  Awslabs has developed their own package for this and given it's for their product, seem likely to maintain it. https://github.com/awslabs/aws-data-wrangler. Source: over 2 years ago

• Parquet files

  AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler. Source: over 2 years ago

• Go+: Go designed for data science

  Yep, agreed. Go is a great language for AWS Lambda type workflows. Python isn't as great (Python Lambda Layers built on Macs don't always work). AWS Data Wrangler (https://github.com/awslabs/aws-data-wrangler) provides pre-built layers, which is a work around, but something that's as portable as Go would be the best solution. - Source: Hacker News / about 3 years ago

Apache Spark mentions (56)

• Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy

  Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉. - Source: dev.to / about 2 months ago

• 🦿🛴Smarcity garbage reporting automation w/ ollama

  Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system. - Source: dev.to / 3 months ago

• Go concurrency simplified. Part 4: Post office as a data pipeline

  Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago

• Five Apache projects you probably didn't know about

  Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago

• Spark – A micro framework for creating web applications in Kotlin and Java

  A JVM based framework named "Spark", when https://spark.apache.org exists? - Source: Hacker News / 11 months ago