Need advice about which tool to choose?Ask the StackShare community!
Azure Data Factory vs Dataddo: What are the differences?
Developers describe Azure Data Factory as "Hybrid data integration service that simplifies ETL at scale". 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. On the other hand, Dataddo is detailed as "Cloud-based data extraction, integration & analytics platform". It connects all your data with your BI, dashboards and data warehouse automatically, securely and without a single line of code.
Azure Data Factory can be classified as a tool in the "Big Data Tools" category, while Dataddo is grouped under "Integration Tools".
Some of the features offered by Azure Data Factory are:
- Real-Time Integration
- Parallel Processing
- Data Chunker
On the other hand, Dataddo provides the following key features:
- Universal data integration platform
- Re-define your data management strategy
- Break the data silos
Azure Data Factory is an open source tool with 161 GitHub stars and 268 GitHub forks. Here's a link to Azure Data Factory's open source repository on GitHub.
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?
