(33)
4.7 out of 5
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Top Rated Observo Query Alternatives
(33)
4.7 out of 5
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There are not enough reviews of Observo Query for G2 to provide buying insight. Below are some alternatives with more reviews:
1
Google Cloud BigQuery
4.5
(1,148)
Analyze Big Data in the cloud with BigQuery. Run fast, SQL-like queries against multi-terabyte datasets in seconds. Scalable and easy to use, BigQuery gives you real-time insights about your data.
2
MATLAB
4.5
(742)
MATLAB is a programming, modeling and simulation tool developed by MathWorks.
3
Alteryx
4.6
(638)
Alteryx drives transformational business outcomes through unified analytics, data science, and process automation.
4
Snowflake
4.6
(626)
Snowflake’s platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything “just works”. To support a wide range of workloads, it’s optimized for performance at scale no matter whether someone’s working with SQL, Python, or other languages. And it’s globally connected so organizations can securely access the most relevant content across clouds and regions, with one consistent experience.
5
Databricks Data Intelligence Platform
4.6
(609)
Making big data simple
6
Teradata Vantage
4.3
(350)
The Teradata Database easily and efficiently handles complex data requirements and simplifies management of the data warehouse environment.
7
Qubole
4.0
(259)
Qubole delivers a Self-Service Platform for Big Data Analytics built on Amazon, Microsoft and Google Clouds
8
Azure Databricks
4.5
(218)
Accelerate innovation by enabling data science with a high-performance analytics platform that's optimized for Azure.
9
Dataiku
4.4
(175)
Dataiku is the Universal AI Platform, giving organizations control over their AI talent, processes, and technologies to unleash the creation of analytics, models, and agents.
10
dbt
4.8
(170)
dbt is a transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.
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