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Azure Data Factory vs Dremio: What are the differences?
Introduction
Azure Data Factory and Dremio are both powerful data platforms that offer data integration and transformation capabilities. However, there are key differences between the two in terms of functionality and architecture.
- Data Integration Approach:
Azure Data Factory is a cloud-based integration service that allows you to create, schedule, and orchestrate data pipelines. It provides a visual interface for designing and managing data workflows, and supports seamless integration with various data sources and destination systems. On the other hand, Dremio is a data virtualization platform that enables users to access, query, and analyze data from multiple sources in real-time. It leverages a "self-service" approach, allowing users to directly access and query data without the need for data movement or ETL processes.
- Data Transformation Capabilities:
Azure Data Factory provides built-in data transformation activities that allow you to transform and manipulate data during the pipeline execution. It supports various data transformation operations like data cleansing, aggregation, filtering, and more. In contrast, Dremio offers advanced data transformation capabilities through its Data Reflections feature. It automatically optimizes and accelerates data transformations by creating aggregated and indexed views of the underlying data, resulting in faster query performance.
- Data Storage and Processing:
Azure Data Factory integrates seamlessly with Azure's storage and processing services like Azure Blob Storage, Azure Data Lake Storage, and Azure Databricks. This allows you to leverage Azure's scalable and cost-effective storage and processing capabilities for performing data integration tasks. Dremio, on the other hand, can connect to various data sources, including both cloud-based and on-premises storage systems. It uses its own distributed storage layer called "Dremio Reflections" to optimize query performance and data access.
- Data Governance and Security:
Azure Data Factory provides robust data governance and security features, including data encryption, role-based access control (RBAC), and Azure Active Directory integration. It also supports data masking and data classification to protect sensitive data. Dremio offers similar data governance capabilities, providing granular access controls and encryption of data in transit and at rest. It also integrates with existing identity management systems for secure authentication and authorization.
- Scalability and Performance:
Azure Data Factory is designed to scale seamlessly to handle large volumes of data and can be integrated with Azure's autoscaling capabilities. It leverages Azure's distributed computing resources for efficient and parallel execution of data integration workflows. Dremio is also highly scalable and can handle large-scale data processing, thanks to its distributed architecture. It optimizes query performance through data caching, indexing, and workload-aware query routing.
- Data Exploration and Visualization:
Azure Data Factory primarily focuses on data integration and orchestration and does not provide built-in data exploration or visualization capabilities. However, it can integrate with other Azure services like Power BI, Azure Synapse Analytics, and Azure Analysis Services for advanced data analytics and visualization. Dremio, on the other hand, offers powerful self-service data exploration and visualization capabilities. It includes a built-in SQL editor, a data virtualization layer, and integrates with popular BI tools like Tableau and Power BI.
In summary, Azure Data Factory and Dremio are both powerful data platforms, but they differ in their integration approach, data transformation capabilities, data storage and processing options, data governance and security features, scalability and performance optimizations, and data exploration and visualization capabilities.
We need to perform ETL from several databases into a data warehouse or data lake. We want to
- keep raw and transformed data available to users to draft their own queries efficiently
- give users the ability to give custom permissions and SSO
- move between open-source on-premises development and cloud-based production environments
We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.
My question is which is the best tool to do the following:
- Create pipelines to ingest the data from multiple sources into the data lake
- Help me in aggregating and filtering data available in the data lake.
- Create new reports by combining different data elements from the data lake.
I need to use only open-source tools for this activity.
I appreciate your valuable inputs and suggestions. Thanks in Advance.
Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at www.zetaris.com for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.
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?
Pros of Azure Data Factory
Pros of Dremio
- Nice GUI to enable more people to work with Data3
- Connect NoSQL databases with RDBMS2
- Easier to Deploy2
- Free1
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Cons of Azure Data Factory
Cons of Dremio
- Works only on Iceberg structured data1