AtScale Adaptive Analytics (A3) vs erwin Data Intelligence by Quest comparison

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Executive Summary

We performed a comparison between AtScale Adaptive Analytics (A3) and erwin Data Intelligence by Quest based on real PeerSpot user reviews.

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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 biggest impact for us is that erwin generates DDL extremely quickly. We're able to pull in metadata, map it to a target, generate DDL to create the tables, and generate SSIS packages. Previously, especially going back 10 to 15 years ago, hundreds of hours had to be spent to manually perform these tasks. This solution completely automates it and gets it 90% done. We can then pass it off to a developer to create the items in SSIS.""It is a central place for everybody to start any ETL data pipeline builds. This tool is being heavily used, plus it's heavily integrated with all the ETL data pipeline design and build processes. Nobody can bypass these processes and do something without going through this tool.""Data Intelligence has provided more profound insights into legacy data movements, lineages, and definitions in the short term. We have linked three critical layers of data, providing us with an end-to-end lineage at the column level.""The possibility to write automation scripts is the biggest benefit for us. We have several products with metadata and metadata mapping capabilities. The big difference when we were choosing this product was the ability to run automation scripts against metadata and metadata mappings. Right now, we have a very high level of automation based on these automation scripts, so it's really the core feature for us.""The solution saves time in data discovery and understanding our entire organization's data.""Data Intelligence creates a single source of truth for all of our metadata. This solution is better for data warehousing, but the metadata features speed up our development work. It's easy to create and manage mappings because we can export them to Informatica and pick up the work where we left off.""The solution gives us data lineage which means we can see an impact if we make a change. The ability for us to have that in this company is brilliant because we used to have 49 data stewards from some 23 different groups within six major departments. Each one of those was a silo unto itself. The ability to have different glossaries — but all pointed to the same key terms, key concepts, or key attributes — has made life really simple.""Overall, DI's data cataloging, data literacy, and automation have helped our decision-makers because when a source wants to change something, we immediately know what the impact is going to be downstream."

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

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"The solution's Arabic language processing is limited. The results are limited when you use the interface in Arabic.""Everything about Data Intelligence is complex. Though we've used the tool for five years, we're still only using about 30 to 40 percent of its capabilities. It would be helpful if we could customize and simplify the user interface because there are so many redundant things.""We still need another layer of data quality assessments on the source to see if it is sending us the wrong data or if there are some issues with the source data. For those things, we need a rule-based data quality assessment or scoring where we can assess tools or other technology stacks. We need to be able to leverage where the business comes in, defining some business rules and have the ability to execute those rules, then score the data quality of all those attributes. Data quality is definitely not what we are leveraging from this tool, as of today.""There may be some opportunities for improvement in terms of the user interface to make it a little bit more intuitive. They have made some good progress. Originally, when we started, we were on version 9 or 10. Over the last couple of releases, I've seen some improvements that they have made, but there might be a few other additional areas in UI where they can make some enhancements.""The metadata ingestion is very nice because of the ability to automate it. It would be nice to be able to do this ingestion, or set it up, from one place, instead of having to set it up separately for every data asset that is ingested.""The integration with various metadata sources, including erwin Data Modeler, isn't smooth in the current version. It took some experimentation to get things working. We hope this is improved in the newer version. The initial version we used felt awkward because Erwin implemented features from other companies into their offering.""The SDK behind this entire product needs improvement. The company really should focus more on this because we were finding some inconsistencies on the LDK level. Everything worked fine from the UI perspective, but when we started doing some deep automation scripts going through multiple API calls inside the tool, then only some pieces of it work or it would not return the exact data it was supposed to do.""We chose to implement on an Oracle Database because we also had the erwin Data Modeler and Web Portal products in-house, which have been set up on Oracle Databases for many years. Sometimes the Oracle Database installation has caused some hiccups that wouldn't necessarily have been caused if we had used SQL Server."

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Pricing and Cost Advice
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  • "The licensing cost is around $7,000 for user. This is an estimation."
  • "There is an additional fee for the server maintenance."
  • "The whole suite, not just the DI but the modeling software, the harvester, Mapping Manager — everything we have — is about $100,000 a year for our renewals. That works out to each module being something like $8,000 to $10,000."
  • "erwin's pricing was cheaper than its competitors."
  • "You buy a seat license for your portal. We have 100 seats for the portal, then you buy just the development licenses for the people who are going to put the data in."
  • "erwin is cheaper than other solutions and this should appeal to other buyers. It has a good price tag."
  • "We operate on a yearly subscription and because it is an enterprise license we only have one. It is not dependent on the number of users."
  • "Smart Data Connectors have some costs, and then there are user-based licenses. We spend roughly $150,000 per year on the solution. It is a yearly subscription license that basically includes the cost for Smart Data Connectors and user-based licenses. We have around 30 data stewards who maintain definitions, and then we have five IT users who basically maintain the overall solution. It is not a SaaS kind of operation, and there is an infrastructure cost to host this solution, which is our regular AWS hosting cost."
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    Top Answer:The data mapping manager is the most valuable feature.
    Top Answer:The data quality assessment requires third-party components and a separate license. I would like to have better integration around the data quality. I would appreciate the inclusion of a… more »
    Ranking
    34th
    out of 58 in Data Governance
    Views
    168
    Comparisons
    138
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    4th
    out of 58 in Data Governance
    Views
    4,864
    Comparisons
    1,458
    Reviews
    11
    Average Words per Review
    1,311
    Rating
    8.4
    Comparisons
    Also Known As
    AtScale, AtScale Intelligence Platform
    erwin DG, erwin Data Governance
    Learn More
    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.



    The erwin Data Intelligence Suite (erwin DI) combines data catalog and data literacy capabilities for greater awareness of and access to available data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed. Automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog. Then make it accessible and understandable within context via role-based views. This complete metadata-driven approach to data governance facilitates greater IT and business collaboration.

    Sample Customers
    Rakuten, TD Bank, Aetna, Glaxo-Smith Kline, Biogen, Toyota, Tyson
    Oracle, Infosys, GSK, Toyota Motor Sales, HSBC
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm26%
    Manufacturing Company13%
    Computer Software Company8%
    Healthcare Company6%
    REVIEWERS
    Insurance Company28%
    Pharma/Biotech Company22%
    Computer Software Company22%
    Healthcare Company6%
    VISITORS READING REVIEWS
    Computer Software Company13%
    Financial Services Firm13%
    Government10%
    Insurance Company8%
    Company Size
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise8%
    Large Enterprise77%
    REVIEWERS
    Small Business5%
    Midsize Enterprise21%
    Large Enterprise74%
    VISITORS READING REVIEWS
    Small Business25%
    Midsize Enterprise12%
    Large Enterprise63%
    Buyer's Guide
    Data Governance
    June 2024
    Find out what your peers are saying about Microsoft, Collibra, Informatica and others in Data Governance. Updated: June 2024.
    772,649 professionals have used our research since 2012.

    AtScale Adaptive Analytics (A3) is ranked 34th in Data Governance while erwin Data Intelligence by Quest is ranked 4th in Data Governance with 18 reviews. AtScale Adaptive Analytics (A3) is rated 5.0, while erwin Data Intelligence by Quest 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 erwin Data Intelligence by Quest writes "Enabled us to centralize a tremendous amount of data into a common standard, and uniform reporting has decreased report requests". AtScale Adaptive Analytics (A3) is most compared with Denodo, Dremio, Kyvos, ThoughtSpot and Collibra Lineage, whereas erwin Data Intelligence by Quest is most compared with Microsoft Purview Data Governance, Collibra Governance, Alation Data Catalog, Informatica Axon and BigID.

    See our list of best Data Governance vendors.

    We monitor all Data Governance 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.