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Azure Data Factory

Hybrid data integration service that simplifies ETL at scale
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What is Azure Data Factory?

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.
Azure Data Factory is a tool in the Big Data Tools category of a tech stack.
Azure Data Factory is an open source tool with 443 GitHub stars and 561 GitHub forks. Here’s a link to Azure Data Factory's open source repository on GitHub

Who uses Azure Data Factory?

34 companies reportedly use Azure Data Factory in their tech stacks, including ViaVarejo, Mews, and Runtastic.

192 developers on StackShare have stated that they use Azure Data Factory.

Azure Data Factory Integrations

Java, .NET, Azure HDInsight, Octotree, and Hosted Graphite are some of the popular tools that integrate with Azure Data Factory. Here's a list of all 6 tools that integrate with Azure Data Factory.
Decisions about Azure Data Factory

Here are some stack decisions, common use cases and reviews by companies and developers who chose Azure Data Factory in their tech stack.

We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

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Needs advice
Amazon S3Amazon S3DremioDremio

Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

  1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
  2. Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
  3. Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
  4. Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
  5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
  6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
  7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
  8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!
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Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 228.5K views

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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Azure Data Factory's Features

  • Real-Time Integration
  • Parallel Processing
  • Data Chunker
  • Data Masking
  • Proactive Monitoring
  • Big Data Processing

Azure Data Factory Alternatives & Comparisons

What are some alternatives to Azure Data Factory?
Azure Databricks
Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.
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.
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.
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.
See all alternatives

Azure Data Factory's Followers
458 developers follow Azure Data Factory to keep up with related blogs and decisions.