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Advice on Azure Data Factory and Azure Pipelines
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 227.9K 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?
Balaramesh Thiyagaraj
We are currently using Azure Pipelines for continous integration. Our applications are developed witn .NET framework. But when we look at the online Jenkins is the most widely used tool for continous integration. Can you please give me the advice which one is best to use for my case Azure pipeline or jenkins.
Replies (1)
shailesh shah
If your source code is on GitHub, also take a look at Github actions. https://github.com/features/actions
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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.
What is Azure Pipelines?
Fast builds with parallel jobs and test execution. Use container jobs to create consistent and reliable builds with the exact tools you need. Create new containers with ease and push them to any registry.
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What are some alternatives to Azure Data Factory and Azure Pipelines?
Azure Databricks
Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
Talend
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.