# Best Columnar Databases

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   Columnar databases store data by columns rather than by rows. The data storage format in these solutions makes them faster and more efficient for instant analytical queries. These databases are used mainly in data warehouses to handle and process massive volumes of data from multiple sources by serving as a basis for business intelligence tools. These databases support document creation, retrieval via query, updating and editing, and deletion of information. Columnar stores, because of their data storage format, help minimize resource usage related to queries on big data sets. Businesses interested in implementing a database for data warehousing and big data processing may opt for a columnar database.

There are other database types similar but slightly different than columnar databases software including [object-oriented databases software](https://www.g2.com/categories/object-oriented-databases), [graph databases](https://www.g2.com/categories/graph-databases), [key-value databases](https://www.g2.com/categories/key-value-databases), and more.

To qualify for inclusion in the Columnar Databases category, a product must:

- Provide data storage
- Store data in columnar format
- Allow users to retrieve data





## Category Overview

**Total Products under this Category:** 27


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 3,300+ 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.


## Best Columnar Databases At A Glance

- **Leader:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
- **Highest Performer:** [ClickHouse](https://www.g2.com/products/clickhouse/reviews)
- **Easiest to Use:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
- **Top Trending:** [Snowflake](https://www.g2.com/products/snowflake/reviews)
- **Best Free Software:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)


---

**Sponsored**

### QuestDB

QuestDB is an open-source, SQL-first time-series database built for the most demanding workloads—from trading floors to mission control. A multi-tier design keeps hot data in native partitions and cold history in Parquet/object storage, queried via one SQL layer. Vectorized, columnar execution delivers high-throughput ingest and millisecond queries. Open formats (Parquet/Arrow) make it AI-ready and lock-in free. Deploy self-hosted or in your cloud (BYOC).



[Visit company website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=2546&secure%5Bdisplayable_resource_id%5D=1761&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=1761&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=131714&secure%5Bresource_id%5D=2546&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fcolumnar-databases%3Fpage%3D6&secure%5Btoken%5D=c1b83f1a31ada3866c41c72bb7c04767c7dc2e317575cda6ee463422590a3165&secure%5Burl%5D=https%3A%2F%2Fquestdb.com&secure%5Burl_type%5D=company_website)

---

## Top-Rated Products (Ranked by G2 Score)
  ### 1. [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
  BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. Store 10 GiB of data and run up to 1 TiB of queries for free per month.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 1,157

**User Satisfaction Scores:**

- **Data Model:** 9.1/10 (Category avg: 8.7/10)
- **Data Types:** 8.7/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.4/10)
- **Integrated Cache:** 8.5/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 37% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (156 reviews)
- Speed (143 reviews)
- Fast Querying (120 reviews)
- Integrations (118 reviews)
- Query Efficiency (114 reviews)

**Cons:**

- Expensive (127 reviews)
- Query Issues (78 reviews)
- Cost Issues (63 reviews)
- Cost Management (60 reviews)
- Learning Curve (54 reviews)

  ### 2. [Snowflake](https://www.g2.com/products/snowflake/reviews)
  Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 666

**User Satisfaction Scores:**

- **Data Model:** 9.2/10 (Category avg: 8.7/10)
- **Data Types:** 9.2/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.4/10)
- **Integrated Cache:** 9.2/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc)
- **Company Website:** https://www.snowflake.com
- **Year Founded:** 2012
- **HQ Location:** San Mateo, CA
- **Twitter:** @SnowflakeDB (240 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (10,857 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Mid-Market, 43% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (89 reviews)
- Scalability (68 reviews)
- Data Management (67 reviews)
- Features (66 reviews)
- Integrations (61 reviews)

**Cons:**

- Expensive (53 reviews)
- Cost (36 reviews)
- Cost Management (32 reviews)
- Learning Curve (25 reviews)
- Feature Limitations (21 reviews)

  ### 3. [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews)
  Tens of thousands of customers use Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets 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.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 367

**User Satisfaction Scores:**

- **Data Model:** 8.4/10 (Category avg: 8.7/10)
- **Data Types:** 8.4/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.4/10)
- **Integrated Cache:** 8.2/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,223,984 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Senior Data Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 40% Enterprise, 39% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Integrations (7 reviews)
- Easy Integrations (5 reviews)
- Fast Querying (5 reviews)
- Scalability (5 reviews)

**Cons:**

- Complexity (5 reviews)
- Feature Limitations (5 reviews)
- Software Limitations (5 reviews)
- Query Issues (4 reviews)
- Query Optimization (4 reviews)

  ### 4. [ClickHouse](https://www.g2.com/products/clickhouse/reviews)
  ClickHouse is the fastest and most resource efficient real-time database and data warehouse. ClickHouse is optimized to serve a wide range of data-intensive workloads, from powering interactive user-facing dashboards to running ad-hoc historical analysis over petabytes of data. Today, ClickHouse Cloud is used by enterprises all over the world, including Lyft, Sony, IBM, GitLab, Twilio, HubSpot, and many more. ClickHouse is available open-source or through cloud services on AWS, GCP, and soon, Azure.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 22

**User Satisfaction Scores:**

- **Data Model:** 9.2/10 (Category avg: 8.7/10)
- **Data Types:** 9.0/10 (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:** 8.1/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [ClickHouse](https://www.g2.com/sellers/clickhouse)
- **Year Founded:** 2021
- **HQ Location:** Palo Alto, US
- **Twitter:** @ClickhouseDB (16,444 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/clickhouseinc/ (447 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 52% Small-Business, 39% Mid-Market


#### Pros & Cons

**Pros:**

- Easy Integrations (2 reviews)
- Integrations (2 reviews)
- Query Efficiency (2 reviews)
- Query Speed (2 reviews)
- Speed (2 reviews)

**Cons:**

- Beginner Unfriendliness (1 reviews)
- Complex Usage (1 reviews)
- Query Optimization (1 reviews)
- Required Expertise (1 reviews)
- Training Required (1 reviews)

  ### 5. [MariaDB](https://www.g2.com/products/mariadb/reviews)
  MariaDB frees companies from the costs, constraints and complexity of proprietary databases, enabling them to reinvest in what matters most – rapidly developing innovative, customer-facing applications. MariaDB uses pluggable, purpose-built storage engines to support workloads that previously required a variety of specialized databases. With complexity and constraints eliminated, enterprises can now depend on a single complete database for all their needs, whether on commodity hardware or their cloud of choice. Deployed in minutes for transactional or analytical use cases, MariaDB delivers unmatched operational agility without sacrificing key enterprise features including real ACID compliance and full SQL. Trusted by organizations such as Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow, Verizon and Walgreens – MariaDB meets the same core requirements as proprietary databases at a fraction of the cost. No wonder it’s the fastest growing open source database. Real business relies on MariaDB™.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 217

**User Satisfaction Scores:**

- **Data Model:** 8.8/10 (Category avg: 8.7/10)
- **Data Types:** 8.9/10 (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:** 9.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [MariaDB](https://www.g2.com/sellers/mariadb)
- **Year Founded:** 2014
- **HQ Location:** Espoo, Finland
- **Twitter:** @mariadb (453,268 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1214250/ (332 employees on LinkedIn®)
- **Ownership:** NYSE: MRDB

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 43% Small-Business, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (2 reviews)
- Easy Integrations (2 reviews)
- Query Speed (2 reviews)
- Backup Services (1 reviews)
- Connectivity (1 reviews)

**Cons:**

- Beginner Unfriendliness (1 reviews)
- Connectivity Issues (1 reviews)
- Difficult Learning (1 reviews)
- Error Handling (1 reviews)
- Feature Limitations (1 reviews)

  ### 6. [MonetDB](https://www.g2.com/products/monetdb/reviews)
  An Open-Source Database System


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Data Model:** 8.6/10 (Category avg: 8.7/10)
- **Data Types:** 8.6/10 (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:** 8.3/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [MonetDB](https://www.g2.com/sellers/monetdb)
- **Year Founded:** 2013
- **HQ Location:** Amsterdam, NL
- **LinkedIn® Page:** https://www.linkedin.com/company/monetdb-solutions (8 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 56% Small-Business, 38% Mid-Market


#### Pros & Cons

**Pros:**

- Fast Querying (8 reviews)
- Features (5 reviews)
- Speed (5 reviews)
- Performance (4 reviews)
- Usability (3 reviews)

**Cons:**

- Complex Setup (3 reviews)
- Connectivity Issues (2 reviews)
- Limited Customization (1 reviews)
- Limited Features (1 reviews)
- Poor Documentation (1 reviews)

  ### 7. [OpenText Vertica](https://www.g2.com/products/opentext-vertica/reviews)
  Vertica is the unified analytics platform, based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability. Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads, arming them and their customers with predictive business insights. Vertica provides a unified analytics platform across major public clouds and on-premises data centers, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed platform, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 195

**User Satisfaction Scores:**

- **Data Model:** 8.4/10 (Category avg: 8.7/10)
- **Data Types:** 8.0/10 (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:** 8.5/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [OpenText](https://www.g2.com/sellers/opentext)
- **Year Founded:** 1991
- **HQ Location:** Waterloo, ON
- **Twitter:** @OpenText (21,588 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2709/ (23,339 employees on LinkedIn®)
- **Ownership:** NASDAQ:OTEX

**Reviewer Demographics:**
  - **Who Uses This:** Senior Software Engineer, Data Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 44% Enterprise, 39% Mid-Market


  ### 8. [StarTree](https://www.g2.com/products/startree/reviews)
  StarTree Cloud is a fully-managed user-facing real-time analytics Database-as-a-Service (DBaaS) designed for OLAP at massive speed and scale. Based on Apache Pinot™, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 29

**User Satisfaction Scores:**

- **Data Model:** 8.3/10 (Category avg: 8.7/10)
- **Data Types:** 8.1/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.4/10)
- **Integrated Cache:** 8.7/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [StarTree](https://www.g2.com/sellers/startree)
- **Company Website:** https://www.startree.ai/
- **Year Founded:** 2019
- **HQ Location:** Mountain View, California
- **Twitter:** @startreedata (2,263 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/startreedata/ (123 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Financial Services
  - **Company Size:** 38% Small-Business, 31% Enterprise


#### Pros & Cons

**Pros:**

- Analytics (4 reviews)
- Fast Querying (4 reviews)
- Large Datasets (4 reviews)
- Performance (4 reviews)
- Big Data Handling (3 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Complex Setup (3 reviews)
- Difficult Setup (3 reviews)
- Insufficient Documentation (3 reviews)
- Poor Documentation (3 reviews)

  ### 9. [CrateDB](https://www.g2.com/products/cratedb/reviews)
  The real-time database for analytics, search, and AI. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source, multi-model, distributed and containerized database that runs queries in milliseconds, regardless of data complexity, volume and velocity.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 82

**User Satisfaction Scores:**

- **Data Model:** 9.0/10 (Category avg: 8.7/10)
- **Data Types:** 9.2/10 (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:** 10.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [CrateDB](https://www.g2.com/sellers/cratedb)
- **Company Website:** https://cratedb.com/
- **Year Founded:** 2013
- **HQ Location:** Redwood City, CA
- **Twitter:** @cratedb (4,184 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/crateio/ (44 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Data Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 54% Small-Business, 31% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (12 reviews)
- SQL Usage (11 reviews)
- Easy Integrations (10 reviews)
- Flexibility (10 reviews)
- Features (9 reviews)

**Cons:**

- Lack of Features (5 reviews)
- Software Limitations (4 reviews)
- Limited Features (3 reviews)
- Poor Documentation (3 reviews)
- Complex Configuration (2 reviews)

  ### 10. [Apache Parquet](https://www.g2.com/products/apache-parquet/reviews)
  Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 26

**User Satisfaction Scores:**

- **Data Model:** 9.0/10 (Category avg: 8.7/10)
- **Data Types:** 8.8/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 7.5/10 (Category avg: 8.4/10)
- **Integrated Cache:** 8.6/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF (66,116 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 48% Mid-Market, 30% Small-Business


  ### 11. [KX](https://www.g2.com/products/kx-kx/reviews)
  We power the time-aware data-driven decisions that enable fast-moving organizations to realize the full potential of their AI investments and outpace competitors. Our technology delivers transformational value by addressing data challenges around completeness, timeliness, and efficiency. We enable organizations to understand change over time and generate faster, more accurate insights — at any scale, and with cost efficiency. Our technology is essential to the operations of the world's top investment banks, aerospace and defense, high-tech manufacturing, healthcare and life sciences, automotive, and fleet telematics organizations. The primary audience for KX encompasses line-of-business leaders, developers, data scientists, and data engineers who require sophisticated analytics capabilities to create high-performance, data-driven applications. With its unmatched speed and scalability, KX allows users to efficiently process large volumes of data, whether in cloud environments, on-premises, or at the edge. This flexibility ensures that organizations can integrate KX technology into their existing workflows seamlessly, enhancing their analytical capabilities without causing disruptions to ongoing operations. KX distinguishes itself in the analytics landscape through its independently benchmarked performance, recognized as the fastest available on the market. This speed is vital for businesses that depend on real-time data insights to inform their decision-making processes. By enabling users to uncover richer, actionable insights quickly, KX facilitates faster and more informed choices, driving competitive advantage and transformative growth. Its ability to manage complex data sets and deliver insights promptly is particularly advantageous for industries that operate in fast-paced environments, where timely information is critical. Key features of KX include advanced time series and vector data analytics capabilities, which enable efficient management and analysis of extensive data volumes. Furthermore, KX integrates seamlessly with popular analytics tools, enhancing their performance and allowing users to maximize their existing investments. The platform's architecture is designed for high performance, ensuring that organizations can scale their analytics operations as needed without sacrificing speed or efficiency. With a global presence across North America, Europe, and Asia Pacific, KX is trusted by leading organizations to spearhead their data and AI initiatives. By providing a powerful analytics solution, KX not only enhances operational efficiency but also fosters a culture of innovation, empowering businesses to remain competitive in an increasingly data-driven world.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 50

**User Satisfaction Scores:**

- **Data Model:** 8.8/10 (Category avg: 8.7/10)
- **Data Types:** 9.5/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.4/10)
- **Integrated Cache:** 9.1/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [KX](https://www.g2.com/sellers/kx-a145756d-91d3-463e-a51d-9e13b1ac577c)
- **Year Founded:** 1996
- **HQ Location:** NY, USA
- **Twitter:** @kxsystems (4,169 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kx-systems (527 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Banking
  - **Company Size:** 57% Enterprise, 25% Small-Business


#### Pros & Cons

**Pros:**

- Speed (11 reviews)
- Performance (9 reviews)
- Tool Power (7 reviews)
- Efficiency (6 reviews)
- Fast Processing (6 reviews)

**Cons:**

- Learning Curve (12 reviews)
- Difficult Learning (7 reviews)
- Steep Learning Curve (7 reviews)
- Complexity (2 reviews)
- Expensive (2 reviews)

  ### 12. [Hbase](https://www.g2.com/products/hbase/reviews)
  A scalable, distributed database that supports structured data storage for large tables. Use HBase when you need random, realtime read/write access to Big Data.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 103

**User Satisfaction Scores:**

- **Data Model:** 8.1/10 (Category avg: 8.7/10)
- **Data Types:** 7.2/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 8.0/10 (Category avg: 8.4/10)
- **Integrated Cache:** 7.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF (66,116 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Big Data Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 52% Enterprise, 27% Mid-Market


  ### 13. [Apache Kudu](https://www.g2.com/products/apache-kudu/reviews)
  Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Data Model:** 8.7/10 (Category avg: 8.7/10)
- **Data Types:** 6.3/10 (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:** 7.3/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF (66,116 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 54% Enterprise, 46% Mid-Market


  ### 14. [Druid](https://www.g2.com/products/druid/reviews)
  Apache Druid is an open source real-time analytics database. Druid combines ideas from OLAP/analytic databases, timeseries databases, and search systems to create a complete real-time analytics solution for real-time data. It includes stream and batch ingestion, column-oriented storage, time-optimized partitioning, native OLAP and search indexing, SQL and REST support, flexible schemas; all with true horizontal scalability on a shared nothing, cloud native architecture that makes it easy to deploy, monitor and manage at scale. It is downloadable for free for unlimited use from druid.apache.org and also hosted in the cloud by Imply Data.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 28

**User Satisfaction Scores:**

- **Data Model:** 8.8/10 (Category avg: 8.7/10)
- **Data Types:** 7.8/10 (Category avg: 8.5/10)
- **Has the product been a good partner in doing business?:** 7.7/10 (Category avg: 8.4/10)
- **Integrated Cache:** 8.9/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Druid](https://www.g2.com/sellers/druid)
- **Year Founded:** 1998
- **HQ Location:** Rio de Janeiro, Rio de Janeiro
- **Twitter:** @druid (4 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/druid_2/ (77 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 52% Enterprise, 29% Mid-Market


  ### 15. [Apache ORC](https://www.g2.com/products/apache-orc/reviews)
  Apache ORC is a self-describing type-aware columnar file format for Hadoop workloads.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF (66,116 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 45% Enterprise, 36% Mid-Market


  ### 16. [Azure Table Storage](https://www.g2.com/products/azure-table-storage/reviews)
  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:** 4.3/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **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?:** 8.7/10 (Category avg: 8.4/10)
- **Integrated Cache:** 10.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,105,844 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Company Size:** 56% Small-Business, 31% Enterprise


#### Pros & Cons

**Pros:**

- Data Storage (3 reviews)
- Cost Efficiency (2 reviews)
- Efficiency (1 reviews)
- Features (1 reviews)
- Integrations (1 reviews)

**Cons:**

- Scalability Issues (1 reviews)
- SQL Limitations (1 reviews)

  ### 17. [Azure Cosmos DB](https://www.g2.com/products/azure-cosmos-db/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,105,844 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Enterprise, 28% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (4 reviews)
- Features (3 reviews)
- Integrations (3 reviews)
- Scalability (3 reviews)
- Customization (2 reviews)

**Cons:**

- Expensive (3 reviews)
- Cost Issues (2 reviews)
- Complexity Issues (1 reviews)
- Complex Usage (1 reviews)
- Cost Increase (1 reviews)

  ### 18. [Google Cloud BigTable](https://www.g2.com/products/google-cloud-bigtable/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Company Size:** 41% Mid-Market, 32% Enterprise


#### Pros & Cons

**Pros:**

- Cloud Storage (8 reviews)
- Ease of Use (4 reviews)
- Integrations (4 reviews)
- Application Development (3 reviews)
- Data Analytics (3 reviews)

**Cons:**

- Cost Issues (5 reviews)
- Expensive (4 reviews)
- Billing Issues (2 reviews)
- Complexity (2 reviews)
- Learning Difficulty (2 reviews)

  ### 19. [Tembo](https://www.g2.com/products/tembo/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Tembo](https://www.g2.com/sellers/tembo)
- **Year Founded:** 2022
- **HQ Location:** Cincinnati, US
- **Twitter:** @tembo_io (2 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/tembo-inc/ (31 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 85% Small-Business, 15% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (16 reviews)
- Features (12 reviews)
- Integrations (10 reviews)
- Ease of Setup (8 reviews)
- Easy Integrations (8 reviews)

**Cons:**

- Limited Flexibility (5 reviews)
- AWS Dependency (4 reviews)
- Cloud Limitations (4 reviews)
- Expensive (4 reviews)
- Limited Customization (4 reviews)

  ### 20. [Tinybird](https://www.g2.com/products/tinybird/reviews)
  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

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Tinybird](https://www.g2.com/sellers/tinybird)
- **Company Website:** https://tinybird.co
- **Year Founded:** 2019
- **HQ Location:** New York, US
- **LinkedIn® Page:** https://www.linkedin.com/company/35704741 (54 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 50% Mid-Market, 36% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (6 reviews)
- Analytics (4 reviews)
- Easy Integrations (4 reviews)
- Features (4 reviews)
- Integrations (4 reviews)

**Cons:**

- Poor Customer Support (3 reviews)
- Lack of Features (2 reviews)
- Learning Curve (2 reviews)
- Learning Difficulty (2 reviews)
- Limited Customization (2 reviews)

  ### 21. [CelerData Cloud](https://www.g2.com/products/celerdata-cloud/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [CelerData](https://www.g2.com/sellers/celerdata)
- **Company Website:** https://celerdata.com
- **Year Founded:** 2022
- **HQ Location:** Menlo Park, US
- **LinkedIn® Page:** https://www.linkedin.com/company/starrocks (65 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 33% Enterprise


#### Pros & Cons

**Pros:**

- Customer Support (3 reviews)
- Fast Querying (3 reviews)
- Performance (3 reviews)
- Fast Communication (2 reviews)
- Fast Processing (2 reviews)


  ### 22. [Hypertable](https://www.g2.com/products/hypertable/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Hypertable](https://www.g2.com/sellers/hypertable)
- **Year Founded:** 2009
- **HQ Location:** Burlingame, US
- **LinkedIn® Page:** https://www.linkedin.com/company/hypertable-inc. (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Mid-Market


#### Pros & Cons

**Pros:**

- Analytics (1 reviews)
- Large Datasets (1 reviews)
- Scalability (1 reviews)

**Cons:**

- Complexity (1 reviews)
- Security Issues (1 reviews)

  ### 23. [Kinetica](https://www.g2.com/products/kinetica/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Kinetica](https://www.g2.com/sellers/kinetica)
- **Year Founded:** 2016
- **HQ Location:** Arlington, Virginia, United States
- **Twitter:** @KineticaHQ (3,470 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kinetica/ (71 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Mid-Market


  ### 24. [Tiger Data](https://www.g2.com/products/tiger-data/reviews)
  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:** 33

**User Satisfaction Scores:**

- **Data Model:** 7.2/10 (Category avg: 8.7/10)
- **Data Types:** 7.8/10 (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:** 7.8/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Tiger Data (creators of TimescaleDB)](https://www.g2.com/sellers/tiger-data-creators-of-timescaledb)
- **Company Website:** https://www.tigerdata.com/
- **Year Founded:** 2015
- **HQ Location:** New York, New York
- **Twitter:** @TigerDatabase (1,302 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/timescaledb/ (43 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Financial Services
  - **Company Size:** 79% Small-Business, 18% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (8 reviews)
- Easy Setup (5 reviews)
- Setup Ease (5 reviews)
- Analytics (4 reviews)
- Performance (4 reviews)

**Cons:**

- Expensive (4 reviews)
- Expensive Licensing (3 reviews)
- Missing Features (3 reviews)
- Poor UI (3 reviews)
- Slow Performance (3 reviews)

  ### 25. [TileDB](https://www.g2.com/products/tiledb/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [TileDB](https://www.g2.com/sellers/tiledb)
- **Year Founded:** 2017
- **HQ Location:** Cambridge, Massachusetts, United States
- **LinkedIn® Page:** https://www.linkedin.com/company/tiledb-inc (70 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Enterprise




## Parent Category

[IT Management Software](https://www.g2.com/categories/it-management)



## Related Categories

- [Data Warehouse Solutions](https://www.g2.com/categories/data-warehouse)
- [Time Series Databases](https://www.g2.com/categories/time-series-databases)
- [Real-time Analytic Database Software](https://www.g2.com/categories/real-time-analytic-database)