AtScale Adaptive Analytics (A3) vs Dremio comparison

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237 views|201 comparisons
0% willing to recommend
Dremio Logo
2,683 views|2,043 comparisons
100% willing to recommend
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The GUI interface is nice and easy to use."

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"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory.""Dremio gives you the ability to create services which do not require additional resources and sterilization.""Everyone uses Dremio in my company; some use it only for the analytics function.""We primarily use Dremio to create a data framework and a data queue.""Dremio allows querying the files I have on my block storage or object storage.""Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."

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Cons
"The product was not able to meet our 10 second refresh requirements.""The organization of the icons is not saved across users.""There was an issue with the incremental aggregation not working as indicated."

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"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people.""I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported.""Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement.""It shows errors sometimes.""Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake.""We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

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Pricing and Cost Advice
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  • "Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
  • "Dremio is less costly competitively to Snowflake or any other tool."
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    Top Answer:Dremio allows querying the files I have on my block storage or object storage.
    Top Answer:Every tool has a value based on its visualization, and the pricing is worth its value.
    Top Answer:Dremio's interface is good, but it has a few limitations. I cannot do a lot of things with ANSI SQL or basic SQL. I cannot use the recursive common table expression (CTE) in Dremio because the support… more »
    Ranking
    5th
    Views
    237
    Comparisons
    201
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    Average Words per Review
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    9th
    Views
    2,683
    Comparisons
    2,043
    Reviews
    6
    Average Words per Review
    530
    Rating
    8.7
    Comparisons
    Also Known As
    AtScale, AtScale Intelligence Platform
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    Dremio
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    Overview

    AtScale is the leading provider of intelligent data virtualization for big data analytical workloads, empowering citizen data scientists to accelerate and scale their business’ data analytics and science capabilities and ultimately build insight-driven 

    AtScale connects people to live disparate data without the need to move or extract it, leveraging existing investments in big data platforms, applications and tools. AtScale creates automated data engineering using a single set of semantics so consumers can query live data (either on premise or in the cloud) in seconds without having to understand how or where it is stored—providing security, governance and predictability in data usage and storage costs.

    Benefits:

    No data movement: AtScale is agnostic to data platforms and data location, whether on-premises or in the cloud, in a data lake or a data warehouse.

    Automatic “smart” aggregate creation: AtSacle’s intelligent aggregates adapt to the data model and how it is used, automating the data engineering tasks required to support those activities and reducing time spent from weeks to hours.

    Use your existing BI and AI tools: AtScale provides access to live, atomic-level data without the user needing to understand where or how to access the data, so you can keep using your tools of choice.

    No more extracts or shadow IT: AtScale eliminates the need for extracts with a single, consistent, governed view of live data, regardless of which BI and AI tools are used.

    Data-as-a-service: AtScale allows metadata to be created once, with centrally defined business rules and calculations, exposing data assets as a service.

    Data platform portability: Models built in AtScale are portable, with no need to recreate them for different platforms. AtScale can easily be repointed to new data platforms, making migration seamless to business users.

    Faster time-to-insight: AtScale reduces time-to-insight from weeks and months to minutes and hours. AtScale virtual models can be created and deployed in no time, with no ETL or data engineering.

    Future-proof your data architecture: AtScale alleviates the complexities of data platform and analytics tool integration, making cloud, hybrid-cloud and multi-cloud data architectures a reality without compromising performance, security, agility or existing governance and security policies.

    Features:

    Design CanvasTM: AtScale’s Design Canvas visually and intuitively connects to any data platform, allowing you to create virtual multidimensional cubes without ETL.

    Autonomous Data Engineering: Just-in-time query optimization that anticipates the needs of the data consumer.

    Universal Semantic LayerTM: A workspace with a Design Canvas for your data consumers to define business meaning and get a single-source-of-truth.

    Security & Data Governance: Centralized security policy to decentralize access using the tenants of Zero Trust.

    Virtual Cube Catalog: A gateway to data that is easily discoverable and frictionless—and available to use every day, en masse.

    AtScale connects people to live disparate data without the need to move or extract it, leveraging existing investments in big data platforms, applications and tools. AtScale creates automated data engineering using a single set of semantics so consumers can query live data (either on premise or in the cloud) in seconds without having to understand how or where it is stored—providing security, governance and predictability in data usage and storage costs.



    Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.

    At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.

    What Are Dremio's Key Features?

    • Data Reflections: Reduces query times by creating optimized representations of source data, which can accelerate performance without the complexity of traditional data warehousing solutions.
    • Semantic Layer: Allows users to define business metrics and dimensions centrally, ensuring consistency and governance across all analytics tools.
    • Built-in Security Features: Provides robust security measures, including column- and row-level security, ensuring compliance with data governance and privacy standards.
    • Support for Multiple Data Formats and Sources: Enables querying directly against a variety of data formats (Parquet, JSON, etc.) and sources without the need for conversion or replication.

    What Benefits Should Users Expect?

    When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.

    How is Dremio Implemented Across Different Industries?

    Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.

    What About Dremio’s Pricing, Licensing, and Support?

    Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.

    Sample Customers
    Rakuten, TD Bank, Aetna, Glaxo-Smith Kline, Biogen, Toyota, Tyson
    UBS, TransUnion, Quantium, Daimler, OVH
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm26%
    Manufacturing Company12%
    Computer Software Company7%
    Healthcare Company6%
    VISITORS READING REVIEWS
    Financial Services Firm31%
    Computer Software Company11%
    Manufacturing Company8%
    Retailer4%
    Company Size
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise8%
    Large Enterprise75%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise11%
    Large Enterprise73%

    AtScale Adaptive Analytics (A3) is ranked 5th in Data Virtualization while Dremio is ranked 9th in Data Science Platforms with 6 reviews. AtScale Adaptive Analytics (A3) is rated 5.0, while Dremio is rated 8.6. The top reviewer of AtScale Adaptive Analytics (A3) writes "The GUI interface is nice and easy to use, but the organization of the icons is not saved across users". On the other hand, the top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". AtScale Adaptive Analytics (A3) is most compared with Denodo, ThoughtSpot, SAP BusinessObjects Business Intelligence Platform, Kyvos and Alation Data Catalog, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and Teradata.

    We monitor all Data Virtualization reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.