DreamFactory
Postman
MuleSoft Anypoint Platform
AWS CloudTrail
Sentinet
Paw.cloud
Boomi
Apigee
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
DreamFactory is an API management platform used to generate, secure, document, and extend APIs. The platform is used within a wide variety of sectors, including banking, auto manufacturing, online gaming, consulting, and government.
Perhaps best known for its API generation capabilities, the platform can generate APIs for 20 databases including MySQL, Microsoft SQL Server, Oracle, and MongoDB, among others. Generators are also available for Excel, AWS S3, email delivery providers, and IoT.
Authentication and security is another core feature. APIs can be authenticated using API keys, Active Directory, LDAP, OAuth, OpenID Connect, SAML 2.0, and Okta. A robust yet convenient set of role-based access controls (RBACs) allow administrators to easily create highly tailored API access rules.
DreamFactory's scripting engine supports PHP, Python (version 2 and 3) and NodeJS. Developers can use the engine to create entirely scripted APIs which incorporate third-party libraries and packages. The scripting engine can also be used to extend existing endpoints, allowing developers to implement API composition, apply data masking and hiding, response transformation, and more.
Recently added features include restricted administrators, API scheduling, API auditing, and API generation connectors for Snowflake, Hadoop, and Apache Hive.
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Based on our record, Amazon EMR should be more popular than DreamFactory. 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.
Dreamfactory.com — Open source REST API backend for mobile, web, and IoT applications. Hook up any SQL/NoSQL database, file storage system, or external service and it instantly creates a comprehensive REST API platform with live documentation, user management,... - Source: dev.to / almost 3 years ago
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 1 year 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 2 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 2 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 / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Postman - The Collaboration Platform for API Development
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
MuleSoft Anypoint Platform - Anypoint Platform is a unified, highly productive, hybrid integration platform that creates an application network of apps, data and devices with API-led connectivity.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
AWS CloudTrail - AWS CloudTrail is a web service that records AWS API calls for your account and delivers log files to you.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost