Countly Reviews

Amazon EMR Reviews

Countly mentions (6)

• Want your dedicated (and managed) product analytics server?

  Hello HN, founder of Countly (https://count.ly) here. As you might know, we are the creators of one of the first open-source product analytics platforms that has 10+ SDKs for mobile, desktop and web applications. We've been working on a new SaaS, myCountly, to help you launch your own Countly servers in any location, so your user data stays close to home. We are going to do an alpha launch soon, and looking for... - Source: Hacker News / over 1 year ago

• Which crash reporting platform do you use for your Vue apps?

  Is countly still operational? Can't connect to their website https://count.ly/. Source: over 1 year ago

• Ask HN: Best alternatives to Google Analytics in 2021?

  Always surprised more people don’t use countly. Runs nice in docker or digital ocean. https://count.ly. Been self hosting it for years with few issues. - Source: Hacker News / over 2 years ago

• Open Source Analytics Stack: Bringing Control, Flexibility, and Data-Privacy to Your Analytics

  Countly (website, GitHub) is also an open-source product analytics platform that is designed primarily for marketing organizations. It helps marketers track website information (website transactions, campaigns, and sources that led visitors to the website, etc.). Countly also collects real-time mobile analytics metrics like active users, time spent in-app, customer location, etc., in a unified view on your dashboard. - Source: dev.to / over 2 years ago

• Google Analytics deleted my entire account because I didn't log in for 60 days

  Self-hosted alternatives to Google Analytics include: Matomo, open core with a broad feature set: https://matomo.org Countly, open core with desktop and mobile tracking: https://count.ly/ Plausible, open source with a simple feature set: https://plausible.io. - Source: Hacker News / about 3 years 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: about 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