Azure Data Factory vs Databricks

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Azure Data Factory

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Advice on Azure Data Factory and Databricks
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 169.9K 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?

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Pros of Azure Data Factory
Pros of Databricks
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      Best Performances on large datasets
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      True lakehouse architecture
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      Databricks doesn't get access to your data
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      Usage Based Billing
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      Data stays in your cloud account
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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 Databricks?

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.

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    What tools integrate with Azure Data Factory?
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    What are some alternatives to Azure Data Factory and Databricks?
    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