Best Columnar Databases - Page 2

How Many Columnar Databases Products Does G2 Track?

Total Products under this Category: 27

Category Stats (Oct 2026)

  • Average Rating: 4.32/5 (↓0.01 vs Sep 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Snowflake (+0.04%) - Among all products in this category, Snowflake recorded the largest rating increase compared to last month

Last updated: October 20, 2026

How Does G2 Rank Columnar Databases Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 3,400+ Authentic Reviews
  • 27+ Products
  • Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

G2 Grid® for Columnar Databases

G2 Grid® for Columnar Databases plotting products by satisfaction and market presence

Highlighted products: Google Cloud BigQuery, Snowflake, Amazon Redshift, ClickHouse, Rocket Vertica, MariaDB, StarTree, and MonetDB.

Underlying data: [Grid® JSON](https://www.g2.com/categories/columnar-databases/grids.json?focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=amazon-redshift&focus%5B%5D=clickhouse&focus%5B%5D=rocket-vertica&focus%5B%5D=mariadb&focus%5B%5D=startree&focus%5B%5D=monetdb)

Azure Table Storage

Azure Table storage stores large amounts of structured data. The service is a NoSQL datastore which accepts authenticated calls from inside and outside the Azure cloud.

Average Rating: 4.1/5.0

Total Reviews: 17

How Do G2 Users Rate Azure Table Storage?

  • Data Model: 9.4/10 (Category avg: 8.7/10)
  • Data Types: 9.4/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 8.1/10 (Category avg: 8.4/10)
  • Integrated Cache: 10.0/10 (Category avg: 8.5/10)

Who Is the Company Behind Azure Table Storage?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • Twitter: @microsoft
    13,091,739 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT

Who Uses This Product?

  • Company Size: 59% Small, 29% Large

What Do G2 Reviewers Say About Azure Table Storage?

AI-generated summary from verified user reviews

Pros
  • Users value the cost-effectiveness and efficiency of Azure Table Storage for managing large amounts of data seamlessly.
  • Users value the cost efficiency of Azure Table Storage, appreciating its economical storage solutions for large data volumes.
  • Users recognize the efficiency of Azure Table Storage in managing large datasets economically for various applications.
  • Users find Azure Table Storage to be an amazing tool for resource management that integrates seamlessly with Microsoft services.
  • Users love the seamless integrations of Azure Table Storage, enhancing resource management alongside other Microsoft services.
Cons
  • Users find that scalability issues arise due to limited query support and lack of essential relational features.
  • Users find the limited SQL capabilities of Azure Table Storage inadequate for complex relational data use cases.

What Are Recent G2 Reviews of Azure Table Storage?

What Are G2 Users Discussing About Azure Table Storage?

Azure Cosmos DB

Azure Cosmos DB is a fully managed, globally distributed NoSQL and vector database service designed to support mission-critical applications with ultra-low latency and elastic scalability. It enables developers to build AI-powered applications and agents by providing seamless integration with AI services, allowing for efficient storage and querying of both NoSQL data and vectors. With its schema-agnostic JSON document model, Azure Cosmos DB simplifies the development process by automatically indexing all data, eliminating the need for manual schema or index management. The service offers comprehensive Service Level Agreements (SLAs), ensuring less than 10-millisecond read and write latencies and 99.999% availability, making it a reliable choice for applications requiring high performance and global reach. Key Features and Functionality: - Global Distribution: Azure Cosmos DB allows for turnkey global distribution, enabling data to be replicated across multiple regions worldwide, providing high availability and low latency access to data. - Elastic Scalability: The service offers elastic scaling of throughput and storage, allowing developers to scale resources up or down based on demand without downtime. - Multi-Model Support: It natively supports multiple data models, including document, key-value, graph, and column-family, catering to diverse application needs. - AI Integration: Built-in vector search capabilities simplify the development of AI applications by efficiently storing and querying vectors alongside NoSQL data. - Automatic Indexing: All data is automatically indexed, facilitating fast and efficient queries without the need for manual index management. - Comprehensive SLAs: Azure Cosmos DB provides industry-leading SLAs covering throughput, latency, availability, and consistency, ensuring predictable performance. Primary Value and Solutions Provided: Azure Cosmos DB addresses the challenges of building and managing globally distributed applications by offering a fully managed database service that ensures high availability, low latency, and elastic scalability. Its integration with AI services and support for multiple data models empower developers to create intelligent, responsive applications without the complexity of managing infrastructure. By automatically handling data distribution, scaling, and indexing, Azure Cosmos DB allows organizations to focus on innovation and delivering value to their users, making it an ideal solution for applications requiring real-time data access and global reach.

Average Rating: 4.2/5.0

Total Reviews: 59

How Do G2 Users Rate Azure Cosmos DB?

  • Data Model: 6.7/10 (Category avg: 8.7/10)
  • Data Types: 6.7/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.4/10)
  • Integrated Cache: 5.0/10 (Category avg: 8.5/10)

Who Is the Company Behind Azure Cosmos DB?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • Twitter: @microsoft
    13,091,739 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 44% Large, 28% Small

What Do G2 Reviewers Say About Azure Cosmos DB?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Azure Cosmos DB, finding it simple to configure and effective for teams.
  • Users praise the fast performance and vast features of Azure Cosmos DB, enhancing flexibility and integration options.
  • Users find Azure Cosmos DB offers seamless integrations, making it easy to connect with various Microsoft services and APIs.
  • Users value the seamless scalability of Azure Cosmos DB, allowing instant resource management without hassle.
  • Users appreciate the customization options of Azure Cosmos DB, allowing tailored experiences and integration with various APIs.
Cons
  • Users find Azure Cosmos DB to be expensive, often leading to high costs if not managed properly.
  • Users are concerned about the high costs of Azure Cosmos DB, especially with unoptimized storage and partitioning.
  • Users find the complexity issues of Azure Cosmos DB challenging, especially with confusing cost structures and limitations.
  • Users find the complex usage of Azure Cosmos DB challenging, especially for those without advanced database skills.
  • Users find that Azure Cosmos DB can become expensive if storage is not optimized and partitioned effectively.

What Are Recent G2 Reviews of Azure Cosmos DB?

What Are G2 Users Discussing About Azure Cosmos DB?

Google Cloud BigTable

Cloud Bigtable is Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail. Bigtable is designed to handle massive workloads at consistent low latency and high throughput, so it's a great choice for both operational and analytical applications, including IoT, user analytics, and financial data analysis.

Average Rating: 4.4/5.0

Total Reviews: 37

How Do G2 Users Rate Google Cloud BigTable?

  • Data Model: 9.2/10 (Category avg: 8.7/10)
  • Data Types: 10.0/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.4/10)
  • Integrated Cache: 10.0/10 (Category avg: 8.5/10)

Who Is the Company Behind Google Cloud BigTable?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Company Size: 41% Medium, 32% Large

What Do G2 Reviewers Say About Google Cloud BigTable?

AI-generated summary from verified user reviews

Pros
  • Users highlight the ease of use in managing cloud features with Google Cloud BigTable, appreciating its smooth UI.
  • Users appreciate the ease of use of Google Cloud BigTable, enjoying its seamless integration and smooth interface.
  • Users value the seamless integrations of Google Cloud BigTable, enhancing their development and data management experience effortlessly.
  • Users appreciate the seamless integration of services in Google Cloud BigTable for efficient application development and data management.
  • Users praise the efficiency of data analytics in Google Cloud BigTable, enabling seamless access and processing of big data.
Cons
  • Users highlight the high costs of Google Cloud BigTable, finding pricing unpredictable and often overwhelming.
  • Users find Google Cloud BigTable expensive, with unpredictable billing and a steep learning curve for beginners.
  • Users find the billing issues with Google Cloud BigTable frustrating due to unpredictable costs and high pricing.
  • Users find Google Cloud BigTable's complexity challenging, especially due to its overwhelming tools and costly pricing structure.
  • Users find learning difficult due to limited language support and complex usage, requiring significant programming knowledge.

What Are Recent G2 Reviews of Google Cloud BigTable?

What Are G2 Users Discussing About Google Cloud BigTable?

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Tembo

Tembo is a multi-workload Postgres managed service that enables organizations to harness the full power of Postgres for transactional, analytical, and AI workloads. With robust SaaS and self hosted deployment options, Tembo enables everyone – from the smallest startups to the Fortune 500 – to go “all in” on Postgres, achieving unprecedented stability and efficiency across a variety of applications and use cases. With Tembo, customers get all the stability, reliability, and extensibility of Postgres open source with enhanced observability, compliance, and developer experience.

Average Rating: 4.7/5.0

Total Reviews: 26

How Do G2 Users Rate Tembo?

  • Data Model: 8.9/10 (Category avg: 8.7/10)
  • Data Types: 9.4/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 10.0/10 (Category avg: 8.4/10)
  • Integrated Cache: 8.9/10 (Category avg: 8.5/10)

Who Is the Company Behind Tembo?

  • Seller: Tembo
  • Year Founded: 2022
  • HQ Location: Cincinnati, US
  • Twitter: @tembo_io
    3 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    32 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 85% Small, 15% Medium

What Do G2 Reviewers Say About Tembo?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Tembo, finding it simple to set up and manage tasks effectively.
  • Users love the user-friendly dashboard of Tembo, which simplifies task management and enhances team collaboration.
  • Users value the easy integrations with various data sources, streamlining their workflow and enhancing data management.
  • Users appreciate the ease of setup with Tembo, enabling quick deployment and user-friendly navigation.
  • Users value the easy integrations of Tembo, enhancing efficiency and simplifying complex data management tasks effortlessly.
Cons
  • Users find the limited flexibility of Tembo frustrating, as it restricts integration and customization options.
  • Users find the limited integration to only AWS a significant drawback, wishing for broader cloud service support.
  • Users highlight the restrictive cloud limitations of Tembo, particularly its exclusive support for AWS.
  • Users find Tembo to be expensive, especially for small businesses due to variable pricing and additional service costs.
  • Users find the limited customization of Tembo restrictive, impacting flexibility and functionality for their specific needs.

What Are Recent G2 Reviews of Tembo?

Tinybird

Tinybird is a fully managed ClickHouse® service designed for software developers and AI-native product teams by enabling them to create large-scale real-time analytics projects with minimal effort. Tinybird makes integrating the open source ClickHouse database into applications simpler, faster, and more reliable, allowing engineers to focus on feature development rather than infrastructure management. Tinybird eliminates the complexities associated with traditional database management, making it an ideal choice for teams looking to leverage the power of ClickHouse without the overhead of server maintenance and scaling concerns. The target audience for Tinybird includes software developers, data engineers, technical founders, and AI-native product teams building real-time analytics capabilities in their applications. With the increasing demand for real-time data processing, Tinybird caters to teams that need to deliver insights quickly and efficiently. Use cases for Tinybird span various industries, including SaaS, e-commerce, finance, crypto, AI, and IoT, where real-time data analysis is crucial for decision-making and operational efficiency. By providing a managed service, Tinybird allows software engineers to deploy analytics features in days rather than months, significantly accelerating project timelines. Key features of Tinybird include a hosted ClickHouse database plus managed data ingestion and API layers, which simplify the process of integrating analytics into applications. The built-in authentication tools enhance security and data privacy, with support for row-level access policies using JWTs. Free observability logs storage and querying allow users to keep tabs on usage and performance. AI-native features, including Tinybird Code - a CLI agent with deep ClickHouse expertise - plus the Tinybird MCP Server, make integrating analytics features into LLM apps simpler and more robust. Additionally, Tinybird's architecture is designed to handle scaling automatically, allowing teams to focus on their core development tasks without worrying about understanding a new database or worrying about infrastructure details. For those who desire infrastructure control, Tinybird offers self-managed deployment, for free. This unique combination of features enables users to ship data-driven features rapidly while maintaining high performance and reliability. Tinybird stands out in the real-time analytics database landscape by providing the performance of one of the world's fastest OLAP databases without the associated complexity. By abstracting the technical challenges of managing clusters and provisioning resources, Tinybird empowers teams to innovate and iterate on their products more quickly. The service's emphasis on ease of use and rapid deployment makes it an attractive option for organizations looking to harness the power of real-time analytics without the burden of extensive operational overhead. With Tinybird, users can unlock the potential of their data and drive impactful insights, all while enjoying a seamless and efficient development experience.

Average Rating: 4.1/5.0

Total Reviews: 14

How Do G2 Users Rate Tinybird?

  • Has the product been a good partner in doing business?: 10.0/10 (Category avg: 8.4/10)

Who Is the Company Behind Tinybird?

  • Seller: Tinybird
  • Year Founded: 2019
  • HQ Location: New York, US
  • LinkedIn® Page: www.linkedin.com
    52 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 50% Medium, 36% Small

What Do G2 Reviewers Say About Tinybird?

AI-generated summary from verified user reviews

Pros
  • Users find Tinybird to be incredibly easy to use, enabling seamless integration and efficient data analytics development.
  • Users value the ease of integration and real-time analytics offered by Tinybird, enhancing their data experience.
  • Users love the easy integrations of Tinybird, enabling seamless connections and fast development of real-time analytics.
  • Users appreciate Tinybird's ease of integration and exploration, making data analytics simple and efficient for developers.
  • Users value the easy integrations with apps like Confluent Cloud for streamlined real-time analytics and development.
Cons
  • Users report poor customer support, highlighting slow response times and insufficient documentation for new users.
  • Users note a lack of features in Tinybird, limiting integrations and hindering data flow and scalability.
  • Users experience a steep learning curve with Tinybird, making navigation and feature utilization challenging for newcomers.
  • Users experience a learning difficulty with Tinybird, facing challenges in navigation and utilizing its features effectively.
  • Users face limited customization with Tinybird, which restricts adaptability and complicates integration with other platforms.

What Are Recent G2 Reviews of Tinybird?

What Are G2 Users Discussing About Tinybird?

CelerData Cloud

CelerData Cloud is the fastest, secure analytical engine that powers customer-facing and AI-driven analytics at scale, delivering consistently reliable and unbeatable performance with a future-proof architecture—ensuring real-time access to open data without ingestion delays or costly data pipelines. Powered by StarRocks, CelerData delivers 3X the performance/cost of any other solution on the market and is the only platform uniquely designed to enable users to simplify their lakehouse architecture and ditch the need for a data warehouse. CelerData is used worldwide by market-leading brands including Coinbase, Pinterest, Demandbase, and Expedia to generate critical new insights for these data-driven companies.

Average Rating: 4.8/5.0

Total Reviews: 3

How Do G2 Users Rate CelerData Cloud?

  • Data Model: 8.3/10 (Category avg: 8.7/10)
  • Data Types: 8.3/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 10.0/10 (Category avg: 8.4/10)

Who Is the Company Behind CelerData Cloud?

  • Seller: CelerData
  • Year Founded: 2022
  • HQ Location: Menlo Park, US
  • LinkedIn® Page: www.linkedin.com
    78 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 67% Small, 33% Large

What Do G2 Reviewers Say About CelerData Cloud?

AI-generated summary from verified user reviews

Pros
  • Users value the excellent customer support from CelerData Cloud, enhancing their overall experience and confidence in the product.
  • Users value the incredibly fast query performance of CelerData Cloud, enhancing their data strategy transformation confidence.
  • Users value the incredibly fast query performance of CelerData Cloud, supported by a reliable and helpful support team.
  • Users appreciate the fast communication with CelerData Cloud, enabling effective data transformation and support at scale.
  • Users value the incredibly fast query performance of CelerData Cloud, enhancing their data strategy implementation and support.

What Are Recent G2 Reviews of CelerData Cloud?

Hypertable

Hypertable delivers scalable database capacity at maximum performance to speed up your big data application and reduce your hardware footprint.

Average Rating: 4.0/5.0

Total Reviews: 1

How Do G2 Users Rate Hypertable?

  • Data Model: 8.3/10 (Category avg: 8.7/10)
  • Data Types: 10.0/10 (Category avg: 8.5/10)
  • Integrated Cache: 10.0/10 (Category avg: 8.5/10)

Who Is the Company Behind Hypertable?

Who Uses This Product?

  • Company Size: 100% Medium

What Do G2 Reviewers Say About Hypertable?

AI-generated summary from verified user reviews

Pros
  • Users value Hypertable's scalability and robust analytics for managing extensive datasets efficiently across distributed environments.
  • Users benefit from Hypertable's scalability and efficiency in handling large datasets, simplifying data analysis significantly.
  • Users value the scalability of Hypertable, which simplifies handling large datasets efficiently and supports various datatypes.
Cons
  • Users find the complexity of initial setup and security features of Hypertable to be challenging and frustrating.
  • Users face security issues with Hypertable, raising concerns about data protection and overall platform safety.

What Are Recent G2 Reviews of Hypertable?

Kinetica

Kinetica is the database for time & space. Kinetica makes it easy and fast to: - ingest massive amounts of IoT data and other contextual data sets - fuse data sets using spatial and temporal joins - analyze data using SQL based analytics for spatial, graph, and time-series analytics or running containerized ML models

Average Rating: 4.3/5.0

Total Reviews: 2

How Do G2 Users Rate Kinetica?

  • Data Model: 10.0/10 (Category avg: 8.7/10)
  • Data Types: 10.0/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.4/10)
  • Integrated Cache: 10.0/10 (Category avg: 8.5/10)

Who Is the Company Behind Kinetica?

  • Seller: Kinetica
  • Year Founded: 2016
  • HQ Location: Vienna, US
  • Twitter: @KineticaHQ
    3,461 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    66 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 100% Medium

What Are Recent G2 Reviews of Kinetica?

What Are G2 Users Discussing About Kinetica?

Tiger Data

Tiger Data, from the creators of TimescaleDB, is the #1 Postgres time-series database for developers, devices and agents. Keep sensor, on-chain, and customer data fresh while retaining years of history, all queryable in standard SQL. For IoT, Web3, and AI. Why teams choose Tiger Data: - Trusted by thousands of developers. 3M+ active databases, 2k+ customers - Up to 95% Compression. Keep years of history online at a fraction of the cost. - Production-ready without the operational pain. Multi-AZ HA, PITR, cross-region backups, SOC 2/HIPAA/GDPR, deep observability. - Scale effortlessly. Disaggregated compute & storage. Never pay for idle capacity. - Unified data architecture. Connect any data source and automatically sync it between your operational database, and data lake. - Hyperscaler procurement. Available on AWS Marketplace and Azure Marketplace. Key capabilities: - Automatic partitioning Ingest millions of data points per second without manual table management or sharding. - Incremental materialized views Precompute and cache rollups for instant dashboards and APIs. - Hybrid row/column storage Fast writes, compressed reads, optimized for real-time and historical queries. - Compression (up to 95%) Columnar encodings apply filters & aggregates directly on compressed data for faster queries and big savings. - Tiered Storage Automatically move older or less-frequently accessed data to low-cost object storage while keeping it fully queryable through the same SQL interface. - Fully managed Postgres Cloud Scale compute and storage independently, tier S3 storage to manage costs, deploy globally, and skip database ops. Industry verticals: Developers and platform teams in Industrial IoT, manufacturing, Crypto, SaaS/ML and DevOps tooling rely on Tiger to combine operational and historical data for real-time dashboards and mission-critical insights, queryable in standard-SQL. How to get started: Try Tiger Cloud for 1-month free with no credit card needed, or use us indefinitely as part of our free plan. Get started now - https://console.cloud.timescale.com/signup?utm_source=g2&utm_medium=referral&utm_campaign=free-trial-g2

Average Rating: 4.6/5.0

Total Reviews: 32

How Do G2 Users Rate Tiger Data?

  • Data Model: 5.8/10 (Category avg: 8.7/10)
  • Data Types: 6.7/10 (Category avg: 8.5/10)
  • Has the product been a good partner in doing business?: 9.3/10 (Category avg: 8.4/10)
  • Integrated Cache: 6.7/10 (Category avg: 8.5/10)

Who Is the Company Behind Tiger Data?

Who Uses This Product?

  • Top Industries: Computer Software, Financial Services
  • Company Size: 78% Small, 19% Medium

What Do G2 Reviewers Say About Tiger Data?

AI-generated summary from verified user reviews

Pros
  • Users praise the clean and intuitive UI of Tiger Data, making navigation and setup effortless.
  • Users highlight the easy setup of Tiger Data, allowing quick integration and efficient management of databases.
  • Users appreciate the ease of setup, finding it quick and straightforward to start using Tiger Data.
  • Users value the ease of access and actionable insights provided by Tiger Data, enhancing decision-making and analytics.
  • Users praise the performance of Tiger Data, highlighting its speed, accuracy, and user-friendly interface for managing data.
Cons
  • Users find the pricing to be high and inflexible, especially for smaller projects or startups.
  • Users find Tiger Data's expensive licensing burdensome, especially for hobby projects, making budgeting essential for scaling.
  • Users express concerns about missing features like advanced visualizers and experience slow UI when managing many tables.
  • Users experience poor UI with slow loading times, especially when handling large datasets, affecting overall workflow efficiency.
  • Users experience slow performance with the UI, particularly when handling large amounts of data, affecting workflow efficiency.

What Are Recent G2 Reviews of Tiger Data?

TileDB

TileDB is foundational software designed by scientists for scientific discovery. TileDB structures all data types, including data that does not fit into relational databases built for structured tabular data. Built on a powerful shape-shifting array database, TileDB handles the complexities of non-traditional “unstructured” multimodal data, such as genomic variants, bulk and single-cell transcriptomics, proteomics, biomedical imaging, as well as the frontier data of the future. Used by big pharma and biotechs to power their multiomic FAIR data platforms, TileDB is the destination for scientific breakthroughs where frontier multimodal data is driving drug and target discovery.

Average Rating: 3.8/5.0

Total Reviews: 2

How Do G2 Users Rate TileDB?

  • Data Model: 8.3/10 (Category avg: 8.7/10)
  • Data Types: 6.7/10 (Category avg: 8.5/10)
  • Integrated Cache: 6.7/10 (Category avg: 8.5/10)

Who Is the Company Behind TileDB?

  • Seller: TileDB
  • Year Founded: 2017
  • HQ Location: Cambridge, US
  • LinkedIn® Page: www.linkedin.com
    51 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 100% Large

What Are Recent G2 Reviews of TileDB?

Infinidb

Infinidb enables to deploy massively scalable high-performance analytics applications in Amazon Web Services (AWS) with dynamic provisioning.

Average Rating: 1.5/5.0

Total Reviews: 1

How Do G2 Users Rate Infinidb?

  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.4/10)

Who Is the Company Behind Infinidb?

  • Seller: Infinidb
  • HQ Location: Frisco, TX
  • Twitter: @InfiniDB
    633 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 100% Large

What Are G2 Users Discussing About Infinidb?

qikkDB | GPU accelerated columnar database

QikkDB is a GPU-accelerated columnar database designed to deliver exceptional performance for complex polygon operations and big data analytics. It is particularly effective when handling datasets comprising billions of records, providing real-time query responses that are essential for time-sensitive applications. QikkDB is compatible with both Windows and Linux operating systems, making it accessible to a wide range of users. Key Features and Functionality: - GPU Acceleration: Utilizes the parallel processing power of GPUs to significantly enhance query performance, especially for complex analytical tasks. - Columnar Storage: Employs a columnar data storage format, optimizing read and write operations and improving data compression rates. - Geospatial Processing: Offers robust support for geospatial data types and operations, enabling efficient handling of spatial queries and analyses. - Standard SQL Support: Provides compatibility with standard SQL syntax, facilitating seamless integration with existing data tools and workflows. - Cross-Platform Compatibility: Supports both Windows and Linux environments, catering to diverse deployment needs. Primary Value and Problem Solved: QikkDB addresses the challenge of processing massive datasets in real-time, a common bottleneck in traditional database systems. By leveraging GPU acceleration and a columnar storage approach, it enables users to perform complex analytical queries on billions of records with minimal latency. This capability is particularly beneficial for applications requiring rapid insights from large-scale data, such as geospatial analyses, big data analytics, and real-time decision-making processes. QikkDB's efficient data processing reduces hardware requirements and operational costs, making it a cost-effective solution for organizations dealing with extensive data volumes.

Who Is the Company Behind qikkDB | GPU accelerated columnar database?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated October 3, 2024