PackageX Receive
DelivApp
Envoy Deliveries
AfterShip
PAQATO
Threadbeast
Notifii
Orderwerks
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
Our package management software uses machine vision and AI to automate your mailroom and front desk operations. PackageX Receive is easy to use, highly scalable, and works across industries, including:
Simply snap a photo of any package or delivery label (even handwritten! ✍️), and our package management software will: 🌟 Automatically extract all relevant information, 🌟 Match deliveries to the correct recipients, 🌟 Manage notifications, 🌟 Send alerts and reminders to all recipients, 🌟 Collect proof of pickups, and 🌟 Keep track of every item that enters and leaves your mailroom
👉 Trusted by smart teams at WeWork, DelVal, and more in 210+ cities worldwide 👉 100% powered by the cloud. No specialized hardware needed! 👉 Scan packages and notify recipients with the click of a single button 👉 Painless inbound and outbound package tracking 👉 Quick and powerful search: search by carrier, retailer, sender, sender address, or recipient 👉 Users can assign a designated pickup person to collect all of their business's packages 👉 Real-time data and insightful analytics for smarter mailroom operation management 👉 Custom branding features to personalize email communication 👉 Access to our "Virtual Mailroom" features. Convenient call to action buttons within your notification emails.
PackageX Receive Amazon EMRBased on our record, Amazon EMR seems to be more popular. 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: about 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: almost 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: almost 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
DelivApp - Software for managing online ordering, delivery and loyalty for multi-unit restaurants
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Envoy Deliveries - Manage office deliveries simply by snapping a photo.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
AfterShip - AfterShip is the shipment tracking API for ecommerce businesses and marketplaces.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost