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Compare Tiger Data and dataPARC 
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At a Glance
Market Segments
Small-Business (78.8% of reviews)
Mid-Market (63.2% of reviews)
Entry-Level Pricing
Free Trial
No pricing available
Tiger Data
Star Rating
(33)4.6 out of 5
Market Segments
Small-Business (78.8% of reviews)
Pros & Cons
Entry-Level Pricing
Free Trial
Learn more about Tiger DatadataPARC
Star Rating
(39)4.9 out of 5
Market Segments
Mid-Market (63.2% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about dataPARCSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Timescale excels in real-time analytics with a score of 9.7, making it a preferred choice for businesses needing immediate insights, while dataPARC, although strong, has a slightly lower score of 9.3 in this area.
- Reviewers mention that Timescale's data management capabilities, particularly in data collection and aggregation, are rated highly at 9.6 and 9.2 respectively, which users find beneficial for handling large datasets efficiently. In contrast, dataPARC's data cleansing feature received a lower score of 8.1, indicating potential challenges in maintaining data quality.
- G2 users highlight Timescale's superior performance in historical snapshots and real-time updating, both rated at 9.6 and 9.5, which are crucial for businesses that rely on up-to-date information. Users on G2 note that dataPARC's performance in these areas is commendable but does not match the immediacy offered by Timescale.
- Reviewers say that Timescale's ease of use and setup are rated equally at 9.0, making it user-friendly for small businesses. In comparison, dataPARC also scores 9.0 for ease of use but has a slightly better ease of admin score at 8.7, suggesting it may be easier to manage for some users.
- Users report that Timescale's quality of support is exceptional, with a perfect score of 10.0, indicating that users feel well-supported. Conversely, dataPARC's support quality, while still strong at 9.6, suggests that there may be room for improvement in responsiveness or resource availability.
- Reviewers mention that Timescale's integration capabilities, particularly with pre-built connectors, are robust, which is essential for users looking to streamline their workflows. On the other hand, dataPARC's API score of 7.0 indicates that users may face challenges when integrating with other systems, which could hinder operational efficiency.
Pricing
Entry-Level Pricing
Tiger Data
Free
Free Trial
30 day free trial. No credit card required.
- Create a fully-managed Timescale instance in 30 seconds.
- Ingest data at petabyte-scale with rock-solid reliability
- Analyze data using advanced time-series capabilities
dataPARC
No pricing availableFree Trial
Tiger Data
Free Trial is available
dataPARC
No trial information availableRatings
Meets Requirements
9.3
27
9.6
32
Ease of Use
9.0
27
9.1
32
Ease of Setup
8.5
17
8.8
18
Ease of Admin
8.7
10
8.8
18
Quality of Support
9.5
24
9.6
32
Has the product been a good partner in doing business?
9.3
10
9.7
18
Product Direction (% positive)
9.1
27
9.2
30
Features by Category
Reports
Not enough data
8.1
25
Not enough data
8.1
19
Not enough data
9.7
25
Not enough data
9.0
16
Not enough data
9.4
24
Self Service
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Analytics
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Building Reports
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Reports
Not enough data
8.1
25
Not enough data
8.1
19
Not enough data
9.7
25
Not enough data
9.0
16
Not enough data
9.4
24
Reports
Not enough data
8.7
20
Not enough data
8.9
17
Not enough data
9.1
13
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Visualization
Not enough data
9.6
23
Not enough data
9.4
16
Not enough data
9.3
23
Not enough data
8.6
16
Data Updates
Not enough data
9.6
21
Not enough data
9.6
23
Collaboration
Not enough data
8.9
16
Not enough data
8.2
12
Not enough data
8.8
13
Data Updates
Not enough data
9.3
23
Not enough data
9.6
24
Not enough data
9.4
19
Data Management
Not enough data
9.2
11
Not enough data
9.3
10
Not enough data
8.9
9
Not enough data
9.6
13
Not enough data
8.3
10
Analytics
Not enough data
9.6
12
Not enough data
9.7
12
Not enough data
9.6
14
Agentic AI - Manufacturing Intelligence
Not enough data
Not enough data
Data Sources
Not enough data
8.3
5
Not enough data
7.5
6
Not enough data
9.2
6
Analytics
Not enough data
9.7
5
Not enough data
9.4
6
Not enough data
9.7
6
Not enough data
9.3
5
Metrics
Not enough data
9.3
5
Not enough data
9.3
5
Not enough data
9.3
5
Not enough data
9.3
5
Management
Not enough data
Not enough data
Not enough data
Not enough data
8.7
5
Not enough data
Not enough data
Not enough data
8.3
5
Not enough data
Maintenance
Not enough data
Not enough data
8.3
5
Not enough data
8.3
5
Not enough data
Security
Not enough data
Not enough data
8.7
5
Not enough data
Management
9.0
8
Not enough data
8.9
9
Not enough data
8.6
7
Not enough data
8.6
6
Not enough data
Support
8.3
6
Not enough data
8.8
7
Not enough data
8.8
7
Not enough data
8.6
6
Not enough data
Security
9.3
7
Not enough data
9.0
8
Not enough data
8.0
5
Not enough data
9.0
7
Not enough data
Performance
8.7
5
Not enough data
8.6
7
Not enough data
8.8
7
Not enough data
9.2
8
Not enough data
8.8
8
Not enough data
Database Features
9.6
8
Not enough data
9.2
10
Not enough data
9.3
10
Not enough data
9.1
9
Not enough data
8.9
9
Not enough data
9.1
9
Not enough data
9.4
9
Not enough data
Storage
Not enough data
Not enough data
Not enough data
Not enough data
Availability
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Performance
Not enough data
Not enough data
Security
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Support
Not enough data
Not enough data
Not enough data
Not enough data
Data Indexing
Not enough data
Not enough data
Not enough data
Not enough data
Filters
Not enough data
Not enough data
Not enough data
Not enough data
Query latency
Not enough data
Not enough data
Not enough data
Not enough data
Data latency
Not enough data
Not enough data
Not enough data
Not enough data
Connectors
Not enough data
Not enough data
Not enough data
Not enough data
Scale
Not enough data
Not enough data
Not enough data
Not enough data
Architecture
Not enough data
Not enough data
Not enough data
Not enough data
Development
8.7
5
Not enough data
7.7
5
Not enough data
7.7
5
Not enough data
8.7
5
Not enough data
Not enough data
Not enough data
Database
8.8
7
Not enough data
8.8
7
Not enough data
8.3
5
Not enough data
8.7
5
Not enough data
8.6
7
Not enough data
Infrastructure
8.9
6
Not enough data
9.0
5
Not enough data
8.1
6
Not enough data
Configuration
8.6
15
Not enough data
8.5
9
Not enough data
8.8
15
Not enough data
8.3
9
Not enough data
7.9
13
Not enough data
Database Administration
8.3
11
Not enough data
7.6
9
Not enough data
8.3
11
Not enough data
Availability
8.9
11
Not enough data
8.9
11
Not enough data
8.5
8
Not enough data
9.2
12
Not enough data
Security
8.8
8
Not enough data
7.1
11
Not enough data
8.6
7
Not enough data
Data Management
8.3
7
Not enough data
8.3
9
Not enough data
Categories
Categories
Shared Categories
Tiger Data and dataPARC are categorized as Time Series Databases
Unique Categories
Tiger Data is categorized as Real-time Analytic Database, Columnar Databases, Database as a Service (DBaaS), Database Management Systems (DBMS), Relational Databases, Cloud Platform as a Service (PaaS), and Vector Database
dataPARC is categorized as Manufacturing Intelligence, IoT Analytics Platforms, Data Visualization, and Time Series Intelligence
Reviews
Reviewers' Company Size
Tiger Data
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
dataPARC
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Computer Software
24.2%
Financial Services
21.2%
Information Technology and Services
12.1%
Consulting
6.1%
Renewables & Environment
6.1%
Other
30.3%
Paper & Forest Products
65.8%
Manufacturing
10.5%
Oil & Energy
7.9%
Chemicals
5.3%
Renewables & Environment
2.6%
Other
7.9%
Alternatives
Discussions
Tiger Data has no discussions with answers
What is dataPARC used for?
1 Comment
Official Response from dataPARC
dataPARC is a software suite. There are two main products, the dataPARC Historian and PARCview. The dataPARC Historian delivers optimized storage and is the...Read more
dataPARC has no more discussions with answers
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