AtScale vs Sigma Computing

Overview

AtScale

Stacks25

Followers83

Votes0

Sigma Computing

Stacks21

Followers27

Votes0

Sigma Computing vs AtScale: What are the differences?

Developers describe Sigma Computing as "No-code business intelligence and analytics solution". It is modern analytics built for the cloud. Trusted by data-first companies, it provides live access to cloud data warehouses using an intuitive spreadsheet interface that empowers business experts to ask more questions without writing a single line of code. With the full power of SQL, the cloud, and a familiar interface, business users have the freedom to analyze data in real time without limits. On the other hand, AtScale is detailed as "The virtual data warehouse for the modern enterprise". Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

Sigma Computing and AtScale are primarily classified as "Business Intelligence" and "Big Data" tools respectively.

Some of the features offered by Sigma Computing are:

Ad Hoc Reports
Benchmarking
Dashboard

On the other hand, AtScale provides the following key features:

Multiple SQL-on-Hadoop Engine Support
Access Data Where it Lays
Built-in Support for Complex Data Types

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Detailed Comparison

AtScale	Sigma Computing
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.	It is modern analytics built for the cloud. Trusted by data-first companies, it provides live access to cloud data warehouses using an intuitive spreadsheet interface that empowers business experts to ask more questions without writing a single line of code. With the full power of SQL, the cloud, and a familiar interface, business users have the freedom to analyze data in real time without limits.
Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment	Ad Hoc Reports; Benchmarking; Dashboard; Key Performance Indicators ;Performance Metrics; Predictive Analytics; Visual Analytics; Embedded Dashboards; SQL runner; Self-service Analytics; Visual Data Modeling
Statistics
Stacks 25	Stacks 21
Followers 83	Followers 27
Votes 0	Votes 0
Integrations
Python Amazon S3 Tableau Power BI Qlik Sense Azure Database for PostgreSQL	Google BigQuery PostgreSQL Amazon Redshift Snowflake

What are some alternatives to AtScale, Sigma Computing?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Apache Spark

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.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

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.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.