Top 10 GI Big Data Alternatives & Competitors

Top 10 Alternatives to GI Big Data Recently Reviewed By G2 Community

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Google Cloud BigQuery
By Google
4.5/5
(1,208)
Product Description
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.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

Microsoft SQL Server
By Microsoft
4.4/5
(2,262)
Product Description
SQL Server 2017 brings the power of SQL Server to Windows, Linux and Docker containers for the first time ever, enabling developers to build intelligent applications using their preferred language and environment. Experience industry-leading performance, rest assured with innovative security features, transform your business with AI built-in, and deliver insights wherever your users are with mobile BI.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

Snowflake
By Snowflake, Inc.
4.6/5
(678)
Product Description
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.
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Categories in common with GI Big Data:
Big Data Processing and Distribution
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Posit
By Posit
4.5/5
(566)
Product Description
In addition to our open-source data science software, RStudio produces RStudio Team, a unique, modular platform of enterprise-ready professional software products that enable teams to adopt R, Python, and other open-source data science software at scale.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

Teradata Vantage
By Teradata
4.3/5
(360)
Product Description
The Teradata Database easily and efficiently handles complex data requirements and simplifies management of the data warehouse environment.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

Qubole
By Qubole
4/5
(259)
Product Description
Qubole delivers a Self-Service Platform for Big Data Analytics built on Amazon, Microsoft and Google Clouds
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Categories in common with GI Big Data:
Big Data Processing and Distribution

Kyvos Semantic Layer
By Kyvos Insights
4.8/5
(252)
Product Description
Kyvos semantic intelligence layer powers and accelerates every AI and BI initiative. It enables lightning-fast analytics at massive scale on all BI tools and unmatched savings on any data platform. Kyvos’ semantic performance layer provides a fully functional conversational analytics experience, governed access to unified data and ultra-wide, deep data models. Leading enterprises trust Kyvos as a scalable, infrastructure-agnostic universal source for fast insights and AI-ready data access.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

OpenText Vertica
By OpenText
4.3/5
(216)
Product Description
Vertica offers a software-based analytics platform designed to help organizations of all sizes monetize data in real time and at massive scale.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

Hadoop HDFS
By The Apache Software Foundation
4.4/5
(141)
Product Description
The Hadoop Distributed File System (HDFS) is a scalable and fault-tolerant file system designed to manage large datasets across clusters of commodity hardware. As a core component of the Apache Hadoop ecosystem, HDFS enables efficient storage and retrieval of vast amounts of data, making it ideal for big data applications. Key Features and Functionality: - Fault Tolerance: HDFS replicates data blocks across multiple nodes, ensuring data availability and resilience against hardware failures. - High Throughput: Optimized for streaming data access, HDFS provides high aggregate data bandwidth, facilitating rapid data processing. - Scalability: Capable of scaling horizontally by adding more nodes, HDFS can accommodate petabytes of data, supporting the growth of data-intensive applications. - Data Locality: By processing data on the nodes where it is stored, HDFS minimizes network congestion and enhances processing speed. - Portability: Designed to be compatible across various hardware and operating systems, HDFS offers flexibility in deployment environments. Primary Value and Problem Solved: HDFS addresses the challenges of storing and processing massive datasets by providing a reliable, scalable, and cost-effective solution. Its architecture ensures data integrity and availability, even in the face of hardware failures, while its design allows for efficient data processing by leveraging data locality. This makes HDFS particularly valuable for organizations dealing with big data, enabling them to derive insights and value from their data assets effectively.
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Categories in common with GI Big Data:
Big Data Processing and Distribution

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