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

AtScale vs Datameer

OverviewComparisonAlternatives

Overview

AtScale
AtScale
Stacks25
Followers83
Votes0
Datameer
Datameer
Stacks5
Followers12
Votes0

AtScale vs Datameer: What are the differences?

  1. Data Integration Approach: AtScale primarily focuses on connecting to a wide range of data sources, enabling users to access and analyze their data from various platforms seamlessly. Datameer, on the other hand, emphasizes a self-service approach to data integration, allowing users to transform, clean, and prepare data within the platform itself, reducing the need for external tools.
  2. Data Modeling Capabilities: AtScale offers robust data modeling capabilities with its Semantic Layer, providing a unified view of data for analysis across multiple sources. Datameer, while also providing data modeling functionalities, is more geared towards providing agile data preparation and visualization solutions, allowing for quick insights to be gained from the data.
  3. Scalability and Performance: AtScale is known for its ability to handle large datasets and complex queries, making it suitable for enterprise-level analytics with high performance. Datameer, on the other hand, excels in providing a user-friendly interface for agile analytics, focusing on ease of use and quick turnaround times for analysis tasks.
  4. Advanced Analytical Features: AtScale offers advanced analytical features such as OLAP capabilities, machine learning integration, and granular security control for fine-tuning access to data. Datameer, while also capable of supporting advanced analytics, shines in its ability to provide intuitive visualization tools and data exploration functionalities for users of varying expertise levels.
  5. Governance and Compliance: AtScale provides strong governance and compliance features, allowing administrators to manage access control, data lineage, and auditing processes efficiently. Datameer also offers governance capabilities but focuses more on empowering business users with self-service analytics while maintaining compliance with data regulations.
  6. Cost and Licensing Model: AtScale typically follows an enterprise-grade licensing model, which may involve higher costs but provides comprehensive support and features. In contrast, Datameer offers flexible pricing options, including per-user licensing, making it more suitable for organizations looking for cost-effective solutions with scalable user accessibility.

In Summary, AtScale and Datameer differ in their data integration approach, data modeling capabilities, scalability, performance, advanced analytical features, governance, and compliance, as well as cost and licensing models to cater to different enterprise needs.

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Detailed Comparison

AtScale
AtScale
Datameer
Datameer

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

It is a single application that helps you get any data into Hadoop, bring it together, analyze it, and visualize it as quickly and easily as possible. No coding required. Everything in it is self-service and intuitive, from our wizard-based data integration, to a spreadsheet with point-and-click analytics, to our blank canvas to for building custom visualizations.

Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
Data integration; Data visualization; Dynamic data management; Open infrastructure; Pre-built application; Self-service analytics.
Statistics
Stacks
25
Stacks
5
Followers
83
Followers
12
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
Amazon S3
Amazon S3
Microsoft Azure
Microsoft Azure
MySQL
MySQL
Oracle
Oracle
PostgreSQL
PostgreSQL
Beehive
Beehive
Snowflake
Snowflake

What are some alternatives to AtScale, Datameer?

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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