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Denodo vs Dremio: What are the differences?


Denodo and Dremio are both data virtualization tools that offer similar functionalities but have some key differences. Below are the key differences between Denodo and Dremio.

  1. Data Source Support: Denodo supports a wide range of data sources including relational databases, big data sources, cloud platforms, and web services. It provides extensive connectivity options for integration with various data sources. On the other hand, Dremio focuses more on big data sources such as Hadoop, NoSQL databases, and cloud storage systems. It offers native support for these types of data sources and provides advanced optimization techniques for query acceleration.

  2. Query Performance: Denodo provides a data caching mechanism that helps improve query performance by reducing the number of queries sent to the underlying data sources. It also optimizes query execution through various techniques such as result set caching, query pipelining, and query parallelization. Dremio, on the other hand, focuses on query acceleration for big data workloads. It leverages technologies like Apache Arrow, columnar storage, and vectorized execution to deliver high-speed query performance on large datasets.

  3. Data Governance and Security: Denodo offers robust data governance and security features. It provides fine-grained access control, data masking, encryption, and auditing capabilities to ensure data privacy and compliance. It also supports data lineage tracking, data quality management, and metadata management. Dremio, although it provides basic security features like user authentication and authorization, lacks some advanced data governance capabilities provided by Denodo.

  4. Data Transformation and Integration: Denodo offers a comprehensive set of tools for data integration, transformation, and data virtualization. It provides a visual ETL (Extract, Transform, Load) interface, data modeling tools, and data pipeline automation capabilities. On the other hand, Dremio primarily focuses on data virtualization and exploration. While it provides limited data transformation capabilities, it lacks the advanced data integration features offered by Denodo.

  5. Deployment Options: Denodo can be deployed on-premises, in the cloud, or in a hybrid environment. It supports various cloud platforms such as AWS, Azure, and Google Cloud. It also provides options for scaling and high availability. Dremio, on the other hand, is primarily designed for deployment in cloud environments like AWS, Azure, and Kubernetes. It offers automatic scaling and infrastructure optimization for cloud-based deployments.

  6. Community and Support: Denodo has a large and active community of users and provides comprehensive technical support. It offers a range of resources including documentation, forums, knowledge base articles, and training courses. Dremio has a smaller community compared to Denodo but provides good support through documentation, forums, and customer support channels.

In summary, Denodo and Dremio differ in terms of data source support, query performance optimization, data governance, data transformation capabilities, deployment options, and community support. Depending on specific use cases and requirements, organizations can choose the tool that best aligns with their needs.

Advice on Denodo and Dremio

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.

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Replies (3)
John Nguyen
AirflowAirflowAWS LambdaAWS Lambda

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.

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Though we have always built something custom, Apache airflow ( 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.

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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.

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karunakaran karthikeyan
Needs advice

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:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. 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.

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Replies (1)
Rod Beecham
Partnering Lead at Zetaris · | 3 upvotes · 64.6K views

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 for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.

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Pros of Denodo
Pros of Dremio
    Be the first to leave a pro
    • 3
      Nice GUI to enable more people to work with Data
    • 2
      Connect NoSQL databases with RDBMS
    • 2
      Easier to Deploy
    • 1

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    Cons of Denodo
    Cons of Dremio
      Be the first to leave a con
      • 1
        Works only on Iceberg structured data

      Sign up to add or upvote consMake informed product decisions

      What is Denodo?

      It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.

      What is Dremio?

      Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

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        What are some alternatives to Denodo and Dremio?
        Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
        Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
        Distributed SQL Query Engine for Big Data
        Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
        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.
        See all alternatives