What is AtScale?
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
AtScale is a tool in the Big Data Tools category of a tech stack.
AtScale is an open source tool with GitHub stars and GitHub forks. Here’s a link to AtScale's open source repository on GitHub
Who uses AtScale?
Amazon S3, Python, Tableau, Power BI, and Birst are some of the popular tools that integrate with AtScale. Here's a list of all 9 tools that integrate with AtScale.
Why developers like AtScale?
Here’s a list of reasons why companies and developers use AtScale
Be the first to leave a pro
- Multiple SQL-on-Hadoop Engine Support
- Access Data Where it Lays
- Built-in Support for Complex Data Types
- Single Drop-in Gateway Node Deployment
AtScale Alternatives & Comparisons
What are some alternatives to AtScale?
See all alternatives
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.
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 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 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.
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.