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Databricks VS Azure Data Factory

Compare Databricks VS Azure Data Factory and see what are their differences

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Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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  3. » Reviews
  4. » Alternatives

Databricks logo Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Azure Data Factory logo Azure Data Factory

Learn more about Azure Data Factory, the easiest cloud-based hybrid data integration solution at an enterprise scale. Build data factories without the need to code.
  • Databricks Landing page
    Landing page //
    2023-09-14
  • Azure Data Factory Landing page
    Landing page //
    2023-01-12

Databricks features and specs

  • Unified Data Analytics Platform
    Databricks integrates various data processing and analytics tools, offering a unified environment for data engineering, machine learning, and business analytics. This integration can streamline workflows and reduce the complexity of data management.
  • Scalability
    Databricks leverages Apache Spark and other scalable technologies to handle large datasets and high computational workloads efficiently. This makes it suitable for enterprises with significant data processing needs.
  • Collaborative Environment
    The platform offers collaborative notebooks that allow data scientists, engineers, and analysts to work together in real-time. This enhances productivity and fosters better communication within teams.
  • Performance Optimization
    Databricks includes various performance optimization features such as caching, indexing, and query optimization, which can significantly speed up data processing tasks.
  • Support for Various Data Formats
    The platform supports a wide range of data formats and sources, including structured, semi-structured, and unstructured data, making it versatile and adaptable to different use cases.
  • Integration with Cloud Providers
    Databricks is designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, allowing users to easily integrate it into their existing cloud infrastructure.

Possible disadvantages of Databricks

  • Cost
    Databricks can be expensive, especially for large-scale deployments or high-frequency usage. It may not be the most cost-effective solution for smaller organizations or projects with limited budgets.
  • Complexity
    While powerful, Databricks can be complex to set up and manage, requiring specialized knowledge in Apache Spark and cloud infrastructure. This might lead to a steeper learning curve for new users.
  • Dependency on Cloud Providers
    Being heavily integrated with cloud providers, Databricks might face issues like vendor lock-in, where switching providers becomes difficult or costly.
  • Limited Offline Capabilities
    Databricks is primarily designed for cloud environments, which means offline or on-premise capabilities are limited, posing challenges for organizations with strict data governance policies.
  • Resource Management
    Efficiently managing and allocating resources can be challenging in Databricks, especially in large multi-user environments. Mismanagement of resources could lead to increased costs and reduced performance.

Azure Data Factory features and specs

  • Scalability
    Azure Data Factory can handle significant data volumes and allows for scaling up or down as needed, making it suitable for both small and complex data integration projects.
  • Integration
    It provides native integration with various Azure services and a wide array of connectors for different data sources, facilitating seamless data flow across platforms.
  • Cost-effective
    The pay-as-you-go pricing model enables cost management by aligning expenses with actual usage patterns, which can be beneficial for budget-conscious projects.
  • Ease of Use
    Offers a user-friendly interface with drag-and-drop features, making it accessible even for users with limited coding experience.
  • Security
    Azure Data Factory includes robust security features like network isolation, access management, and encryption both in-transit and at-rest, ensuring data protection.

Possible disadvantages of Azure Data Factory

  • Complexity
    Managing large and complex data pipelines may require a steep learning curve and expertise in Azure services, which could be a hindrance for non-technical users.
  • Debugging Challenges
    Debugging tasks and identifying error sources in complex ETL processes can be cumbersome, requiring detailed monitoring and analysis.
  • Limited On-Premise Integration
    While ADF offers numerous connectors, integration with certain on-premise data stores might still require additional configuration and setup.
  • Latency Issues
    Data transfer latency can occur when dealing with extremely large datasets or when integrating multiple cloud and on-premise sources.
  • Dependency on Cloud
    As a cloud-based service, performance can be impacted by internet connectivity issues, and consistent access to the cloud is necessary for operations.

Databricks videos

+ Add

Introduction to Databricks

More videos:

  • Tutorial - Azure Databricks Tutorial | Data transformations at scale
  • Review - Databricks - Data Movement and Query

Azure Data Factory videos

+ Add

Azure Data Factory Tutorial | Introduction to ETL in Azure

More videos:

  • Review - Use Azure Data Factory to copy and transform data
  • Review - Pass summit 2019: Head to Head, SSIS Versus Azure Data Factory

Category Popularity

0-100% (relative to Databricks and Azure Data Factory)

Databricks

Azure Data Factory

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Integration
0 0%
Data Integration
100% 100
Big Data Analytics
100 100%
Big Data Analytics
0% 0
ETL
0 0%
ETL
100% 100

User comments

Share your experience with using Databricks and Azure Data Factory. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Databricks and Azure Data Factory

Databricks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Source: lakefs.io
7 best Colab alternatives in 2023
Databricks is a platform built around Apache Spark, an open-source, distributed computing system. The Databricks Community Edition offers a collaborative workspace where users can create Jupyter notebooks. Although it doesn't offer free GPU resources, it's an excellent tool for distributed data processing and big data analytics.
Source: deepnote.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Source: www.shipyardapp.com
Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Databricks is a simple, fast, and collaborative analytics platform based on Apache Spark with ETL capabilities. It accelerates innovation by bringing together data science and data science businesses. It is a fully managed open-source version of Apache Spark analytics with optimized connectors to storage platforms for the fastest data access.
Source: visual-flow.com
Top Big Data Tools For 2021
Now Azure Databricks achieves 50 times better performance thanks to a highly optimized version of Spark. Databricks also enables real-time co-authoring and automates versioning. Besides, it features runtimes optimized for machine learning that include many popular libraries, such as PyTorch, TensorFlow, Keras, etc.
Source: blog.bismart.com

Azure Data Factory Reviews

Best ETL Tools: A Curated List
Azure Data Factory uses a pay-as-you-go pricing model based on several factors, including the number of activities performed, the duration of integration runtime hours, and data movement volumes. This flexible pricing allows for scaling based on workload but can lead to complex cost structures for larger or more complex data integration projects.
Source: estuary.dev
15+ Best Cloud ETL Tools
Azure Data Factory is a fully managed, serverless data integration service by Azure Cloud. You can easily connect to more than 90 built-in data sources without any added cost, allowing for efficient data integration at an enterprise level. Azure's visual platform lets you create ETL and ELT processes without having to write any code.
Source: estuary.dev
Top 8 Apache Airflow Alternatives in 2024
While Apache Airflow focuses on creating tasks and building dependencies between them for workflow automation, Azure Data Factory is suitable for integration tasks. It would be a perfect fit for the construction of the ETL and ELT pipelines for data migration and integration across platforms.
Source: blog.skyvia.com
A List of The 16 Best ETL Tools And Why To Choose Them
Azure Data Factory is a cloud-based ETL service offered by Microsoft used to create workflows that move and transform data at scale.
Source: www.datacamp.com
Top Big Data Tools For 2021
Azure Data Factory is a cloud solution that enables you to integrate data between multiple relational and non-relational sources, transforming it according to your objectives and requirements.
Source: blog.bismart.com

Social recommendations and mentions

Based on our record, Databricks should be more popular than Azure Data Factory. It has been mentiond 18 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Databricks mentions (18)

  • Platform Engineering Abstraction: How to Scale IaC for Enterprise
    Vendors like Confluent, Snowflake, Databricks, and dbt are improving the developer experience with more automation and integrations, but they often operate independently. This fragmentation makes standardizing multi-directional integrations across identity and access management, data governance, security, and cost control even more challenging. Developing a standardized, secure, and scalable solution for... - Source: dev.to / 8 months ago
  • dolly-v2-12b
    Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: about 2 years ago
  • Clickstream data analysis with Databricks and Redpanda
    Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / almost 3 years ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / almost 3 years ago
  • A Quick Start to Databricks on AWS
    Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 3 years ago
View more

Azure Data Factory mentions (4)

  • Choosing the right, real-time, Postgres CDC platform
    The major infrastructure providers offer CDC products that work within their ecosystem. Tools like AWS DMS, GCP Datastream, and Azure Data Factory can be configured to stream changes from Postgres to other infrastructure. - Source: dev.to / 6 months ago
  • (Recommend) Fun Open Source Tool for Pushing Data Around
    You might want to look at Azure Data Factory https://azure.microsoft.com/en-us/services/data-factory/ to extend SSIS EDIT: Yes, I missed the "open source" part :). Source: about 3 years ago
  • Deploying Azure Data Factory using Bicep
    I'm also planning to do more content with Azure Data Factory, so I'd thought it be good to make a video combining the two. - Source: dev.to / almost 4 years ago
  • Class construction help
    Or, if oyu are using azure then azure data factory https://azure.microsoft.com/en-us/services/data-factory/. Source: almost 4 years ago

What are some alternatives?

When comparing Databricks and Azure Data Factory, you can also consider the following products

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Workato - Experts agree - we're the leader. Forrester Research names Workato a Leader in iPaaS for Dynamic Integration. Get the report. Gartner recognizes Workato as a “Cool Vendor in Social Software and Collaboration”.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

DataTap - Adverity is the best data intelligence software for data-driven decision making. Connect to all your sources and harmonize the data across all channels.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Xplenty - Xplenty is the #1 SecurETL - allowing you to build low-code data pipelines on the most secure and flexible data transformation platform. No longer worry about manual data transformations. Start your free 14-day trial now.

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