StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Analytics
  4. Analytics Integrator
  5. Amazon AppFlow vs Segment

Amazon AppFlow vs Segment

OverviewComparisonAlternatives

Overview

Segment
Segment
Stacks3.3K
Followers941
Votes275
Amazon AppFlow
Amazon AppFlow
Stacks9
Followers42
Votes0

Amazon AppFlow vs Segment: What are the differences?

# Introduction

Amazon AppFlow and Segment are both data integration services that help businesses manage and analyze data efficiently. However, there are key differences between the two platforms that businesses should consider before choosing the right one for their specific needs.

1. **Data Sources**: Amazon AppFlow supports data integration from various sources including AWS services, SaaS applications, and databases. On the other hand, Segment primarily focuses on customer data and supports integrations with analytics, marketing, and data warehousing tools.

2. **Flexibility**: Amazon AppFlow provides a more structured approach to data integration, offering pre-built connectors and templates for common use cases. In contrast, Segment allows for more customization and flexibility, enabling businesses to tailor their data pipelines according to their unique requirements.

3. **Data Transformation**: Amazon AppFlow offers basic data transformation capabilities such as format mapping and filtering. Segment, on the other hand, provides more advanced data transformation features like identity resolution, event tracking, and audience segmentation.

4. **Data Privacy and Security**: Amazon AppFlow ensures data privacy and security through AWS's robust security measures and compliance certifications. Segment also prioritizes data security and offers features like data anonymization and encryption to protect sensitive information.

5. **Real-time Data Streaming**: Segment excels in real-time data streaming capabilities, allowing businesses to collect, process, and analyze data in real-time for immediate insights and actions. Amazon AppFlow supports real-time data streaming but is more tailored towards batch processing and data synchronization.

6. **Scalability**: Amazon AppFlow is designed for scalability and can handle large volumes of data efficiently. Segment is also scalable but may require additional configurations for handling massive volumes of data seamlessly.

In Summary, businesses should consider factors such as data sources, flexibility, data transformation, data privacy, real-time data streaming, and scalability when choosing between Amazon AppFlow and Segment for their data integration needs.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Segment
Segment
Amazon AppFlow
Amazon AppFlow

Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.

It is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. With AppFlow, you can run data flows at nearly any scale at the frequency you choose - on a schedule, in response to a business event, or on demand. You can configure data transformation capabilities like filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps. AppFlow automatically encrypts data in motion, and allows users to restrict data from flowing over the public Internet for SaaS applications that are integrated with AWS PrivateLink, reducing exposure to security threats.

A single API to integrate third-party tools; Data replay that backfills new tools with historical data; SQL support to automatically transform and load behavioral data into Amazon Redshift; More than 120 tools on the platform; One-click to install plugins for WordPress, Magento and WooCommerce; Mobile, web and server-side libraries
Point and click user interface; Native SaaS integrations; Enterprise grade data transformations; High scale data transfer; Data privacy defaults through PrivateLink; Custom encryption keys; IAM policy enforcement; Flexible data flow triggers; Easy to use field mapping; Built in reliability
Statistics
Stacks
3.3K
Stacks
9
Followers
941
Followers
42
Votes
275
Votes
0
Pros & Cons
Pros
  • 86
    Easy to scale and maintain 3rd party services
  • 49
    One API
  • 39
    Simple
  • 25
    Multiple integrations
  • 19
    Cleanest API
Cons
  • 2
    Not clear which events/options are integration-specific
  • 1
    Limitations with integration-specific configurations
  • 1
    Client-side events are separated from server-side
No community feedback yet
Integrations
Google Analytics
Google Analytics
Mixpanel
Mixpanel
UserVoice
UserVoice
LiveChat
LiveChat
Olark
Olark
Marketo
Marketo
Intercom
Intercom
Sentry
Sentry
BugHerd
BugHerd
Gauges
Gauges
Google Analytics
Google Analytics
Slack
Slack
Dynatrace
Dynatrace
Datadog
Datadog
Zendesk
Zendesk
Marketo
Marketo
Snowflake
Snowflake
Amplitude
Amplitude
Veeva
Veeva

What are some alternatives to Segment, Amazon AppFlow?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase