AWS Data Pipeline vs WalkMe

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AWS Data Pipeline
AWS Data Pipeline

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AWS Data Pipeline vs WalkMe: What are the differences?

AWS Data Pipeline: Process and move data between different AWS compute and storage services. AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email; WalkMe: Add step-by-step walk-thru guidance to your website or application. WalkMe enables website owners and app developers to easily create multiple interactive on-screen Walk-Thru’s that help users to quickly and easily complete even the most complex tasks.

AWS Data Pipeline can be classified as a tool in the "Data Transfer" category, while WalkMe is grouped under "Tour Widget".

Some of the features offered by AWS Data Pipeline are:

  • You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.
  • Hourly analysis of Amazon S3‐based log data
  • Daily replication of AmazonDynamoDB data to Amazon S3

On the other hand, WalkMe provides the following key features:

  • Using WalkMe's advanced Editor, you can create Walk-Thrus within minutes, without any technical knowledge, all by using a simple point-and-click interface.
  • WalkMe supports all major browsers.
  • Welcome Screen- Customize your welcome screen to greet your user to your Walk-Thru
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What is AWS Data Pipeline?

AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.

What is WalkMe?

WalkMe enables website owners and app developers to easily create multiple interactive on-screen Walk-Thru’s that help users to quickly and easily complete even the most complex tasks.
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          What are some alternatives to AWS Data Pipeline and WalkMe?
          AWS Glue
          A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
          Airflow
          Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
          Apache NiFi
          An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
          AWS Step Functions
          AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
          AWS Batch
          It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
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