Apache NiFi VS Amazon EMR

Apache NiFi Reviews

Amazon EMR Reviews

Apache NiFi mentions (16)

• Ask HN: What low code platforms are worth using?

  Apache NIFI (https://nifi.apache.org/). It uses the concept of Flow-based programming. Also its so underacknolged but this tool is very flexible. I have used as an Event Bus all the 3rd-Party Integrations. - Source: Hacker News / 7 months ago

• Help with choosing techstack for a new DE team

  Presently setting up Apache Nifi + Apache MiNiFi for the ETL portion of my work. NiFi was easy enough to figure out; but the docs for MiNiFi have been a pain due to differences between the Java and C++ versions. I then entirely configured it with the Java version so that it was easier to search for answers for the MiNiFi yaml syntax. Source: 10 months ago

• Json splitting and Rerouting (new to nifi)

  NIFI, like most Apache projects does most of its discussion on its mailing lists, but also has a slack. Source: 12 months ago

• Recommend ETL tool in Linux

  You might want to give a tool like nifi a try: Https://nifi.apache.org/. Source: about 1 year ago

• Anki shared decks for Clickhouse and Apache Nifi

  Recently I got a job at a new company and now I need to learn about Clickhouse, Docker, Apache Nifi and Kafka. I've found the decks about Docker and Kafka, but I can't find ones about Clickhouse and Nifi. Https://clickhouse.com/docs/en/intro Https://nifi.apache.org/. Source: about 1 year 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