Azure Data Factory vs Pipedream

Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Pipedream

Stacks38

Followers54

Votes0

Azure Data Factory vs Pipedream: What are the differences?

Scalability: Azure Data Factory allows for scalability by seamlessly scaling out to handle larger workloads and data volumes through its cloud-based architecture. Pipedream, on the other hand, also supports scalability but is more focused on real-time data processing and workflows, making it suitable for smaller to medium-sized projects that require rapid data processing.
Integration Capabilities: Azure Data Factory offers a wide range of built-in connectors and integrations with other Microsoft services like Azure SQL Database, Azure Blob Storage, and more. Pipedream, on the contrary, provides integrations with popular third-party services like Slack, GitHub, Google Sheets, allowing for quick and easy data transfer between different platforms.
Pricing Model: Azure Data Factory follows a pay-as-you-go pricing model, where users are charged based on the number of activities executed and data processed. Pipedream offers a free tier with limited features and usage, while also providing paid plans for users with higher data processing needs or advanced features.
Monitoring and Alerting: Azure Data Factory comes with built-in monitoring capabilities, allowing users to track the performance of their data pipelines, set thresholds, and receive alerts when issues arise. Pipedream, on the other hand, offers similar monitoring features with real-time metrics and alerts available to users, ensuring smooth data processing workflows.
Data Transformation: Azure Data Factory includes robust data transformation features like data wrangling, cleansing, and transformation using its data flows functionality. Pipedream, while supporting basic data transformations, is more focused on event-driven data processing and real-time workflows, making it ideal for scenarios where immediate data actions are required.
Security and Compliance: Azure Data Factory provides robust security features like encryption at rest and in transit, role-based access control, and compliance certifications like ISO, GDPR, HIPAA, ensuring data protection and regulatory compliance. Pipedream also offers security measures like encrypted data storage and data isolation, albeit with a lesser focus on compliance certifications.

In Summary, Azure Data Factory and Pipedream differ in terms of scalability, integration capabilities, pricing model, monitoring and alerting, data transformation, and security & compliance features.

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Advice on Azure Data Factory, Pipedream

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Detailed Comparison

Azure Data Factory	Pipedream
It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.	It is an integration platform for developers to build and run workflows that integrate apps, data, and APIs — no servers or infrastructure to manage.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	Run any Node.js code, or use pre-built actions; Create, share, and fork workflows from the community; Send data to S3, Snowflake, email, SSE, and more; Send data to the Pipedream data warehouse, run SQL on it for free
Statistics
GitHub Stars 516	GitHub Stars -
GitHub Forks 610	GitHub Forks -
Stacks 253	Stacks 38
Followers 484	Followers 54
Votes 0	Votes 0
Integrations
Octotree Java .NET	Node.js Discord Snowflake Slack Shopify Amazon S3

What are some alternatives to Azure Data Factory, Pipedream?

Heroku

Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling.

Clever Cloud

Clever Cloud is a polyglot cloud application platform. The service helps developers to build applications with many languages and services, with auto-scaling features and a true pay-as-you-go pricing model.

Google App Engine

Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.

Red Hat OpenShift

OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.

AWS Elastic Beanstalk

Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.

Render

Render is a unified platform to build and run all your apps and websites with free SSL, a global CDN, private networks and auto deploys from Git.

Hasura

An open source GraphQL engine that deploys instant, realtime GraphQL APIs on any Postgres database.

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.

Cloud 66

Cloud 66 gives you everything you need to build, deploy and maintain your applications on any cloud, without the headache of dealing with "server stuff". Frameworks: Ruby on Rails, Node.js, Jamstack, Laravel, GoLang, and more.

Jelastic

Jelastic is a Multi-Cloud DevOps PaaS for ISVs, telcos, service providers and enterprises needing to speed up development, reduce cost of IT infrastructure, improve uptime and security.

Related Comparisons

Azure Data Factory vs Pipedream: What are the differences?

Scalability: Azure Data Factory allows for scalability by seamlessly scaling out to handle larger workloads and data volumes through its cloud-based architecture. Pipedream, on the other hand, also supports scalability but is more focused on real-time data processing and workflows, making it suitable for smaller to medium-sized projects that require rapid data processing.
Integration Capabilities: Azure Data Factory offers a wide range of built-in connectors and integrations with other Microsoft services like Azure SQL Database, Azure Blob Storage, and more. Pipedream, on the contrary, provides integrations with popular third-party services like Slack, GitHub, Google Sheets, allowing for quick and easy data transfer between different platforms.
Pricing Model: Azure Data Factory follows a pay-as-you-go pricing model, where users are charged based on the number of activities executed and data processed. Pipedream offers a free tier with limited features and usage, while also providing paid plans for users with higher data processing needs or advanced features.
Monitoring and Alerting: Azure Data Factory comes with built-in monitoring capabilities, allowing users to track the performance of their data pipelines, set thresholds, and receive alerts when issues arise. Pipedream, on the other hand, offers similar monitoring features with real-time metrics and alerts available to users, ensuring smooth data processing workflows.
Data Transformation: Azure Data Factory includes robust data transformation features like data wrangling, cleansing, and transformation using its data flows functionality. Pipedream, while supporting basic data transformations, is more focused on event-driven data processing and real-time workflows, making it ideal for scenarios where immediate data actions are required.
Security and Compliance: Azure Data Factory provides robust security features like encryption at rest and in transit, role-based access control, and compliance certifications like ISO, GDPR, HIPAA, ensuring data protection and regulatory compliance. Pipedream also offers security measures like encrypted data storage and data isolation, albeit with a lesser focus on compliance certifications.

In Summary, Azure Data Factory and Pipedream differ in terms of scalability, integration capabilities, pricing model, monitoring and alerting, data transformation, and security & compliance features.

Azure Data Factory vs Pipedream

Overview