Introducing G2.ai, the future of software buying.Try now
Compare Spark SQL and Tiger Data 
Featured Products
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
RaimaDB
Visit Website
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
CockroachDB
Visit Website
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
ER/Studio
Visit Website
At a Glance
Market Segments
Enterprise (53.3% of reviews)
Small-Business (78.8% of reviews)
Entry-Level Pricing
No pricing available
Free Trial
Spark SQL
Star Rating
(45)4.5 out of 5
Market Segments
Enterprise (53.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Spark SQLTiger Data
Star Rating
(33)4.6 out of 5
Market Segments
Small-Business (78.8% of reviews)
Entry-Level Pricing
Free Trial
Learn more about Tiger DataSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Timescale excels in Quality of Support, achieving a perfect score of 10.0, while Spark SQL's support quality is rated at 8.4. Reviewers mention that Timescale's support team is highly responsive and knowledgeable, making it a preferred choice for users needing assistance.
- Reviewers mention that Timescale's Continuous Queries feature is particularly beneficial for real-time data analysis, allowing users to efficiently monitor and respond to data changes. In contrast, Spark SQL does not emphasize this feature as strongly, which may impact users looking for real-time capabilities.
- G2 users highlight that Timescale's Data Replication capabilities are rated at 8.3, providing robust options for data redundancy and availability. Spark SQL, while also offering data replication, has a lower score of 8.3, indicating that users may find Timescale's implementation more reliable.
- Users on G2 report that Timescale's Ease of Use is rated at 9.0, making it accessible for small businesses and new users. In comparison, Spark SQL also scores 9.0, but reviewers mention that Timescale's user interface is more intuitive, particularly for those unfamiliar with SQL.
- Reviewers say that Timescale's Performance Analysis tools are highly effective, with users appreciating the detailed insights they provide. Spark SQL, while powerful, does not offer the same level of granularity in performance metrics, which some users find limiting.
- Users report that Timescale's Data Schema management is rated at 8.7, allowing for flexible data modeling that suits various applications. Spark SQL's schema management is rated lower, which may lead to challenges for users needing to adapt their data structures frequently.
Pricing
Entry-Level Pricing
Spark SQL
No pricing availableTiger 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
Free Trial
Spark SQL
No trial information availableTiger Data
Free Trial is available
Ratings
Meets Requirements
8.8
32
9.3
27
Ease of Use
9.0
35
9.0
27
Ease of Setup
8.7
14
8.5
17
Ease of Admin
8.2
13
8.7
10
Quality of Support
8.4
32
9.5
24
Has the product been a good partner in doing business?
8.6
14
9.3
10
Product Direction (% positive)
10.0
29
9.1
27
Features by Category
Management
Not enough data
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
Maintenance
Not enough data
Not enough data
Not enough data
8.3
5
Not enough data
8.3
5
Security
Not enough data
Not enough data
Not enough data
8.7
5
Management
8.7
26
9.0
8
8.5
26
8.9
9
8.3
23
8.6
7
8.3
26
8.6
6
Support
8.0
23
8.3
6
8.1
27
8.8
7
8.2
26
8.8
7
8.8
24
8.6
6
Security
8.7
24
9.3
7
8.3
23
9.0
8
8.4
22
8.0
5
8.5
24
9.0
7
Performance
8.7
25
8.7
5
8.5
25
8.6
7
8.6
25
8.8
7
7.9
23
9.2
8
8.6
25
8.8
8
Database Features
8.3
26
9.6
8
8.7
26
9.2
10
8.7
27
9.3
10
8.9
27
9.1
9
8.5
27
8.9
9
8.5
26
9.1
9
8.3
27
9.4
9
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
Not enough data
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
Database
Not enough data
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
Infrastructure
Not enough data
8.9
6
Not enough data
9.0
5
Not enough data
8.1
6
Configuration
Not enough data
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
Database Administration
Not enough data
8.3
11
Not enough data
7.6
9
Not enough data
8.3
11
Availability
Not enough data
8.9
11
Not enough data
8.9
11
Not enough data
8.5
8
Not enough data
9.2
12
Security
Not enough data
8.8
8
Not enough data
7.1
11
Not enough data
8.6
7
Data Management
Not enough data
8.3
7
Not enough data
8.3
9
Categories
Categories
Shared Categories
Spark SQL and Tiger Data are categorized as Relational Databases
Unique Categories
Spark SQL has no unique categories
Tiger Data is categorized as Real-time Analytic Database, Columnar Databases, Time Series Databases, Database as a Service (DBaaS), Database Management Systems (DBMS), Cloud Platform as a Service (PaaS), and Vector Database
Reviews
Reviewers' Company Size
Spark SQL
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Tiger Data
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
Information Technology and Services
31.1%
Computer Software
13.3%
Retail
6.7%
Financial Services
6.7%
Telecommunications
4.4%
Other
37.8%
Computer Software
24.2%
Financial Services
21.2%
Information Technology and Services
12.1%
Consulting
6.1%
Renewables & Environment
6.1%
Other
30.3%
Alternatives
Discussions
What type of SQL does Spark use?
2 Comments
PS
Spark SqlRead more
What is the difference between SQL and Spark SQL?
1 Comment
NK
Well it's very simple spark sql is used for big data processing, it makes querying the loads of data simple and faster.
Whereas sql isn't suitable for...Read more
What is the functionality of Spark SQL?
1 Comment
NK
Spark sql works with any kind of relational databases that works with sql like Mysql, oracle, mariadb, postgresqlRead more
Tiger Data has no discussions with answers
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
