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. Application & Data
  3. Databases
  4. Big Data Tools
  5. Amazon AppFlow vs CDAP

Amazon AppFlow vs CDAP

OverviewComparisonAlternatives

Overview

CDAP
CDAP
Stacks41
Followers108
Votes0
Amazon AppFlow
Amazon AppFlow
Stacks9
Followers42
Votes0

Amazon AppFlow vs CDAP: What are the differences?

Key Differences between Amazon AppFlow and CDAP

  1. Data Integration Approach: Amazon AppFlow focuses on bi-directional data flow between various AWS services and SaaS applications through pre-built connectors, while CDAP offers a unified data integration platform that enables data pipelines, ingestion, transformation, and visualization across all data sources.

  2. Integration Ecosystem: Amazon AppFlow primarily integrates with AWS services and a selected number of third-party applications, whereas CDAP provides broader support for integrating with diverse data sources, including cloud services, relational databases, and big data frameworks.

  3. Data Processing Capabilities: CDAP offers advanced data processing capabilities with powerful ETL (Extract, Transform, Load) and data transformation functionalities, supporting complex data workflows and data governance requirements, whereas Amazon AppFlow focuses more on simplifying data movement between different services with minimal transformation options.

  4. Workflow Orchestration: CDAP provides comprehensive workflow orchestration features for managing complex data pipelines, scheduling jobs, and handling dependencies effectively, while Amazon AppFlow lacks detailed workflow orchestration capabilities and focuses more on automating data movement.

  5. Data Governance and Security: CDAP offers robust data governance features, including metadata management, lineage tracking, and fine-grained access controls for ensuring data security and compliance, whereas Amazon AppFlow provides basic security controls but may lack the depth of governance features available in CDAP.

  6. Scalability and Flexibility: CDAP offers greater scalability and flexibility to build custom data pipelines and applications tailored to specific business needs, whereas Amazon AppFlow provides a more streamlined and user-friendly approach for quickly setting up data flows with predefined connectors.

In Summary, Amazon AppFlow is designed for simplified bi-directional data flow between AWS services and SaaS applications, while CDAP offers a more comprehensive data integration and processing platform with advanced capabilities for building complex data pipelines and ensuring data governance and security.

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

CDAP
CDAP
Amazon AppFlow
Amazon AppFlow

Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.

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.

Streams for data ingestion;Reusable libraries for common Big Data access patterns;Data available to multiple applications and different paradigms;Framework level guarantees;Full development lifecycle and production deployment;Standardization of applications across programming paradigms
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
41
Stacks
9
Followers
108
Followers
42
Votes
0
Votes
0
Integrations
Hadoop
Hadoop
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 CDAP, 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