AtScale Adaptive Analytics (A3) vs Dremio comparison

Cancel
You must select at least 2 products to compare!
AtScale Logo
239 views|204 comparisons
0% willing to recommend
Dremio Logo
2,610 views|1,995 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."

More AtScale Adaptive Analytics (A3) Pros →

"Everyone uses Dremio in my company; some use it only for the analytics function.""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.""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.""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."

More Dremio Pros →

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

More AtScale Adaptive Analytics (A3) Cons →

"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.""I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported.""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.""It shows errors sometimes.""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.""We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

More Dremio Cons →

Pricing and Cost Advice
Information Not Available
  • "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."
  • More Dremio Pricing and Cost Advice →

    report
    Use our free recommendation engine to learn which Data Virtualization solutions are best for your needs.
    767,847 professionals have used our research since 2012.
    Questions from the Community
    Ask a question

    Earn 20 points

    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
    239
    Comparisons
    204
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    10th
    Views
    2,610
    Comparisons
    1,995
    Reviews
    6
    Average Words per Review
    530
    Rating
    8.7
    Comparisons
    Also Known As
    AtScale, AtScale Intelligence Platform
    Learn More
    Dremio
    Video Not Available
    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 lake query engine tool that creates PDSs and VDSs on top of S3 buckets. It is used for managing simple ad-hoc queries and as a greater layer for ad-hoc queries. The most valuable features of Dremio include its ability to sit on top of any data storage, generate refresh reflections and create visuals, manage changes effectively through data lineage and data providence capabilities, use open-source, and address the problem of data transfer when working with large datasets. The use cases are broad, allowing for high-performance queries from a data lake.

    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 Company11%
    Computer Software Company7%
    Healthcare Company7%
    VISITORS READING REVIEWS
    Financial Services Firm31%
    Computer Software Company11%
    Manufacturing Company8%
    Retailer4%
    Company Size
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise8%
    Large Enterprise76%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise10%
    Large Enterprise74%

    AtScale Adaptive Analytics (A3) is ranked 5th in Data Virtualization while Dremio is ranked 10th 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 "Quick database capabilities but sometimes shows minor errors". AtScale Adaptive Analytics (A3) is most compared with Denodo, ThoughtSpot, SAP BusinessObjects Business Intelligence Platform, Alation Data Catalog and Kyvos, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and Apache Hadoop.

    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.