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AtScale vs Snowflake: What are the differences?
## Key Differences between AtScale and Snowflake
AtScale and Snowflake are two popular data management tools that offer unique features and capabilities. Below are six key differences between AtScale and Snowflake:
1. **Architecture**: AtScale is a virtual cube platform that sits between data sources and BI tools, providing a semantic layer for managing data unification and abstraction. On the other hand, Snowflake is a cloud-based data warehousing platform that offers a scalable architecture with separate compute and storage layers.
2. **Data Processing Engine**: AtScale uses a multi-dimensional OLAP engine to allow users to query data with the familiar SQL language and support for MDX (MultiDimensional eXpressions). Conversely, Snowflake utilizes a distributed architecture with a unique query processing engine that separates storage and processing for optimal performance.
3. **Data Scalability**: AtScale is known for its ability to handle large datasets and complex queries efficiently, making it suitable for organizations with extensive data processing needs. Snowflake, on the other hand, provides scalable storage and processing capabilities in the cloud, allowing users to easily scale their data infrastructure based on demand.
4. **Data Sharing and Collaboration**: AtScale offers features for collaborative data modeling and sharing, allowing teams to work together on building and analyzing datasets. Snowflake provides built-in support for data sharing across multiple users and organizations, enabling easy collaboration on shared data resources.
5. **Security and Compliance**: AtScale provides robust security features such as data encryption, role-based access control, and audit logging to ensure data protection and compliance with industry regulations. Snowflake also offers advanced security functionalities, including end-to-end encryption, secure data sharing, and compliance certifications for various industry standards.
6. **Cost Structure**: AtScale typically operates on a subscription-based pricing model, where customers pay based on the number of users and data sources connected to the platform. In contrast, Snowflake offers a consumption-based pricing model where users are charged based on the amount of data processed and stored, allowing for cost optimization based on actual usage.
In Summary, AtScale and Snowflake differ in their architecture, data processing engines, scalability, data sharing capabilities, security features, and cost structures, catering to different needs in the data management space.
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Pros of Snowflake
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Pros of Snowflake
- Public and Private Data Sharing7
- Multicloud4
- Good Performance4
- User Friendly4
- Great Documentation3
- Serverless2
- Economical1
- Usage based billing1
- Innovative1
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What is AtScale?
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
What is 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.
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What companies use AtScale?
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What companies use AtScale?
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What are some alternatives to AtScale and Snowflake?
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.
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.
Tableau
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.