Need advice about which tool to choose?Ask the StackShare community!

AtScale

25
83
+ 1
0
Dremio

117
348
+ 1
8
Add tool

AtScale vs Dremio: What are the differences?

Introduction

AtScale and Dremio are two popular data virtualization platforms that provide organizations with the ability to access and analyze large datasets from various sources. While they both offer similar functionalities, there are key differences between the two. In this article, we will discuss the main differences between AtScale and Dremio.

  1. Data Source Support: AtScale supports a wide range of data sources, including traditional relational databases, Hadoop-based platforms, cloud-based storage systems, and more. On the other hand, Dremio has extensive support for data sources, including traditional databases, cloud storage platforms, NoSQL databases, file systems, and more.

  2. Data Virtualization Capabilities: AtScale primarily focuses on providing data virtualization capabilities for BI and analytics use cases. It offers features such as query optimization, caching, and semantic layer creation to enable faster data access and analysis. In contrast, Dremio is a full-fledged data lake engine that not only provides data virtualization but also advanced capabilities like data acceleration, data reflection, and data lineage.

  3. Deployment Options: AtScale is typically deployed as an on-premises software solution or hosted on a private cloud infrastructure. It offers options to integrate with existing data platforms and tools. On the other hand, Dremio is a cloud-native platform that can be deployed on public, private, or hybrid clouds. It also provides a fully managed SaaS offering for organizations that prefer a hands-off approach.

  4. Data Governance and Security: AtScale focuses on providing robust data governance and security features, including fine-grained access control, data masking, and data lineage tracking. It ensures compliance and data protection in regulated industries. Dremio also offers data governance capabilities, but with additional features like data cataloging, data classification, and policy-based access controls.

  5. Performance Optimization: AtScale uses techniques like intelligent caching and query optimization to enhance query performance. It leverages its virtualization layer to translate BI tool queries into optimized queries for underlying data sources. Dremio, on the other hand, employs various optimization techniques like data reflection and distributed query execution to accelerate query performance and deliver real-time analytics capabilities.

  6. Operating Models: AtScale follows a federated query model, where data stays in the source systems, and AtScale acts as a query federation layer. It provides a unified view of the data across the sources without physically moving or duplicating the data. Dremio, on the other hand, uses a data lake model, where data is consolidated in a central location and is made available for querying and analysis. It focuses on providing a self-service data platform for data exploration and analysis.

In Summary, AtScale and Dremio differ in terms of their data source support, data virtualization capabilities, deployment options, data governance and security features, performance optimization techniques, and operating models.

Advice on AtScale 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.

See more
Replies (3)
John Nguyen
Recommends
on
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.

See more
Recommends
on
AirflowAirflow

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.

See more
Recommends

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.

See more
karunakaran karthikeyan
Needs advice
on
DremioDremio
and
TalendTalend

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.

See more
Replies (1)
Rod Beecham
Partnering Lead at Zetaris · | 3 upvotes · 67.6K views
Recommends
on
DremioDremio

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.

See more
Manage your open source components, licenses, and vulnerabilities
Learn More
Pros of AtScale
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
      Free

    Sign up to add or upvote prosMake informed product decisions

    Cons of AtScale
    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 AtScale?

      Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

      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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use AtScale?
      What companies use Dremio?
      Manage your open source components, licenses, and vulnerabilities
      Learn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with AtScale?
      What tools integrate with Dremio?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      What are some alternatives to AtScale and Dremio?
      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.
      Apache Impala
      Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
      Druid
      Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
      Snowflake
      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.
      Looker
      We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.
      See all alternatives