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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Denodo vs Snowflake

Denodo vs Snowflake

OverviewComparisonAlternatives

Overview

Snowflake
Snowflake
Stacks1.2K
Followers1.2K
Votes27
Denodo
Denodo
Stacks40
Followers120
Votes0
GitHub Stars0
Forks0

Denodo vs Snowflake: What are the differences?

Introduction

This article provides a comparison between Denodo and Snowflake, two popular technologies in the field of data integration and analytics.

  1. Performance and Scalability: Denodo is a data virtualization tool that enables integration of data from various sources in real-time, while Snowflake is a cloud-based data warehouse that offers high performance and scalability. While Denodo focuses on data virtualization and abstraction, Snowflake provides a platform for managing and analyzing vast amounts of data.

  2. Architecture: Denodo follows a logical or virtual data warehouse architecture, where it provides a virtual layer between data sources and consuming applications. On the other hand, Snowflake offers a multi-cluster shared data architecture, which allows data to be divided and processed across multiple clusters for improved concurrency and scalability.

  3. Data Storage: Denodo does not store data directly, as it acts as a layer that accesses data from different sources and presents it in a unified view. Snowflake, on the other hand, stores data efficiently using a columnar storage format that enables fast query execution and efficient storage utilization.

  4. Data Processing: Denodo supports real-time data integration and enables data virtualization, which means it can retrieve and integrate data on-the-fly from different sources. In contrast, Snowflake focuses on batch processing and analytics, providing features like data loading, transformation, and SQL-based querying for data analysis.

  5. Security and Access Control: Denodo offers fine-grained access control and security features, allowing administrators to define access policies and enforce data governance. Similarly, Snowflake provides robust security measures, including role-based access control, data encryption, and auditing capabilities, ensuring data confidentiality and integrity.

  6. Integration Capabilities: Denodo offers extensive integration capabilities, allowing users to integrate data from various sources, including relational databases, flat files, web services, and more. Snowflake supports integration with a wide range of tools and platforms, making it easy to load and process data from different sources, including cloud storage services.

In summary, Denodo and Snowflake differ in their focus areas and capabilities. Denodo specializes in data virtualization and real-time integration, while Snowflake excels in cloud-based data warehousing and high-performance analytics. Both tools offer robust security measures and support integration with various data sources, but their architectures and approaches to data storage and processing vary.

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Detailed Comparison

Snowflake
Snowflake
Denodo
Denodo

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.

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.

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Data virtualization; Data query; Data views
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
1.2K
Stacks
40
Followers
1.2K
Followers
120
Votes
27
Votes
0
Pros & Cons
Pros
  • 7
    Public and Private Data Sharing
  • 4
    User Friendly
  • 4
    Multicloud
  • 4
    Good Performance
  • 3
    Great Documentation
No community feedback yet
Integrations
Python
Python
Apache Spark
Apache Spark
Node.js
Node.js
Looker
Looker
Periscope
Periscope
Mode
Mode
DataRobot
DataRobot
AtScale
AtScale
Vertica
Vertica
Trifacta
Trifacta
Dremio
Dremio
Apache Kylin
Apache Kylin
SAP HANA
SAP HANA

What are some alternatives to Snowflake, 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.

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

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.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

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