
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Qubole
Snowflake
HortonWorks Data Platform
Databricks
Authenticator
Aegis Authenticator
andOTP
OTP Auth
Authenticator Plus
TOTP Authenticator
Tofu Authenticator
OTPClient
Amazon EMR
AuthenticatorAmazon EMR is recommended for data engineers, data scientists, and IT professionals who need to manage and process large datasets in a scalable, efficient, and cost-effective manner. It is especially suitable for businesses that are already using AWS services and want to leverage a tightly integrated ecosystem. Additionally, it is a good choice for organizations that require rapid and flexible data analysis capabilities provided by frameworks such as Hadoop, Spark, HBase, and Presto.
Authenticator (mattrubin.me) is recommended for individuals and professionals who need a reliable and easy-to-use solution for managing two-factor authentication codes across multiple accounts. It's ideal for users who value open-source software and prioritize security without the need for additional features commonly found in more complex applications.
Based on our record, Amazon EMR should be more popular than Authenticator. It has been mentiond 10 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.
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: over 3 years 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: about 4 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: about 4 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 / almost 5 years ago
Check out https://aws.amazon.com/emr/. Source: over 4 years ago
PS. At the moment Iโm using this one. Itโs really good but itโs missing an export feature. Source: over 3 years ago
For iOS? Authenticator - it's open-source and about as simple as it gets. Source: over 3 years ago
For iOS offline, you could go with Tofu or Authenticator. These are open-source alternatives. Source: almost 4 years ago
2FA: This is a necessity for all online accounts nowadays, but it is important to store your TOTP codes somewhere trusted and secure. Services like Authy are cloud-based and proprietary, meaning that no one can verify their privacy and security claims. Cloud-based applications are risky as it is, and it is best to keep your codes somewhere local on your devices, such as Aegis for Android, or Authenticator for iOS.... Source: over 4 years ago
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Aegis Authenticator - Aegis Authenticator is a free, secure and open source app to manage your 2-step verification tokens...
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
andOTP - andOTP is a two-factor authentication App for Android 4.4+
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
OTP Auth - The app for calculating one-time-passwords on iPhone and iPad.