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

AtScale vs Denodo

OverviewComparisonAlternatives

Overview

AtScale
AtScale
Stacks25
Followers83
Votes0
Denodo
Denodo
Stacks40
Followers120
Votes0
GitHub Stars0
Forks0

AtScale vs Denodo: What are the differences?

Key Differences Between AtScale and Denodo

AtScale and Denodo are both data virtualization platforms that enable organizations to access and analyze data from multiple sources in real time. However, there are several key differences that set them apart.

  1. Architecture: AtScale is built on a multi-dimensional OLAP engine, which allows for fast query performance and scalability. On the other hand, Denodo is built on a data virtualization engine, which focuses on abstracting and providing a unified view of data from different sources.

  2. Data Integration: AtScale provides native connectors to a wide range of enterprise data sources, including relational databases, Hadoop, and cloud data platforms. Denodo also offers a wide range of connectors, but it also includes features like data caching and data transformation capabilities.

  3. Semantic Layer: AtScale uses a semantic layer to provide a business-friendly view of data, allowing users to query data using familiar business terms and logic. Denodo also supports a semantic layer, but it goes a step further by providing features like data lineage, data quality, and metadata management.

  4. Enterprise Features: AtScale offers a set of enterprise-grade features, including security and governance capabilities, fine-grained access controls, and data lineage tracking. Denodo also offers similar enterprise features, but it also includes advanced data masking capabilities and support for regulatory compliance.

  5. Data Governance: AtScale provides built-in data governance capabilities, allowing organizations to define and enforce data access policies, auditing, and compliance requirements. Denodo also includes data governance features, such as data lineage and impact analysis, but it goes beyond by offering features like automatic data classification and data cataloging.

  6. Deployment Options: AtScale can be deployed on-premises or on the cloud, providing flexibility for organizations with different infrastructure requirements. Denodo also offers both on-premises and cloud deployment options, but it also includes support for hybrid cloud environments, enabling organizations to connect and query data across different cloud platforms.

In summary, AtScale focuses on fast query performance and scalability, with a strong emphasis on multi-dimensional OLAP capabilities. In contrast, Denodo focuses on data integration, providing a unified view of data from different sources, along with advanced features for data caching, transformation, and governance.

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Detailed Comparison

AtScale
AtScale
Denodo
Denodo

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

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.

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 virtualization; Data query; Data views
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
25
Stacks
40
Followers
83
Followers
120
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
DataRobot
DataRobot
Vertica
Vertica
Trifacta
Trifacta
Dremio
Dremio
Apache Kylin
Apache Kylin
SAP HANA
SAP HANA

What are some alternatives to AtScale, Denodo?

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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