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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. AtScale vs InPhase

AtScale vs InPhase

OverviewComparisonAlternatives

Overview

AtScale
AtScale
Stacks25
Followers83
Votes0
InPhase
InPhase
Stacks0
Followers2
Votes0

InPhase vs AtScale: What are the differences?

Developers describe InPhase as "Enterprise-wide, unified, Performance Management and Business Intelligence decision-making platform". It is the all-in-one, self-service enterprise performance management platform. Taking care of your performance management, business intelligence, analytics and dashboarding needs. Better, faster decision making from board to individual. 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.

InPhase can be classified as a tool in the "Business Intelligence" category, while AtScale is grouped under "Big Data Tools".

Some of the features offered by InPhase are:

  • Performance Management
  • Business Intelligence
  • Project Management

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
AtScale
InPhase
InPhase

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

It is the all-in-one, self-service enterprise performance management platform. Taking care of your performance management, business intelligence, analytics and dashboarding needs. Better, faster decision making from board to individual.

Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
Performance Management; Business Intelligence; Project Management; Risk Management; Multi-Browser & Device Support
Statistics
Stacks
25
Stacks
0
Followers
83
Followers
2
Votes
0
Votes
0
Integrations
Python
Python
Amazon S3
Amazon S3
Tableau
Tableau
Power BI
Power BI
Qlik Sense
Qlik Sense
Azure Database for PostgreSQL
Azure Database for PostgreSQL
Microsoft Access
Microsoft Access
Microsoft Excel
Microsoft Excel

What are some alternatives to AtScale, InPhase?

Metabase

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

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

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

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

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.

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