Amazon AppFlow vs Pachyderm

Overview

Pachyderm

Stacks24

Followers95

Votes5

Amazon AppFlow

Stacks9

Followers42

Votes0

Pachyderm vs Amazon AppFlow: What are the differences?

Pachyderm: MapReduce without Hadoop. Analyze massive datasets with Docker. Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations; Amazon AppFlow: *Securely integrate apps and automate data flows at any scale, without code *. 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.

Pachyderm and Amazon AppFlow can be primarily classified as "Big Data" tools.

Some of the features offered by Pachyderm are:

Git-like File System
Dockerized MapReduce
Microservice Architecture

On the other hand, Amazon AppFlow provides the following key features:

Point and click user interface
Native SaaS integrations
Enterprise grade data transformations

Pachyderm is an open source tool with 4.37K GitHub stars and 414 GitHub forks. Here's a link to Pachyderm's open source repository on GitHub.

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CLI (Node.js)

Manual

Detailed Comparison

Pachyderm	Amazon AppFlow
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.	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.
Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS	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 24	Stacks 9
Followers 95	Followers 42
Votes 5	Votes 0
Pros & Cons
Pros 3 Containers 1 Can run on GCP or AWS 1 Versioning Cons 1 Recently acquired by HPE, uncertain future.	No community feedback yet
Integrations
Docker Amazon EC2 Google Compute Engine Vagrant	Google Analytics Slack Dynatrace Datadog Zendesk Marketo Snowflake Amplitude Veeva

What are some alternatives to Pachyderm, Amazon AppFlow?

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

Distributed SQL Query Engine for Big Data

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 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

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 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™ 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

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

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.