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What is Sparksee?
Sparksee, formerly known as DEX, enhances both spatial efficiency and performance while keeping a streamlined design that allows for quick analysis of large-scale networks. It accommodates a variety of programming languages, such as .Net, C++, Python, Objective-C, and Java, which provides flexibility across numerous operating systems. The graph data is systematically organized using bitmap data structures, allowing for impressive compression ratios. These bitmaps are segmented into chunks that are aligned with disk pages, which improves input/output locality and leads to superior performance. By utilizing bitmaps, calculations are performed using binary logic instructions that support efficient processing in pipelined architectures. Additionally, the system includes comprehensive native indexing, ensuring swift access to all graph data structures. Connections between nodes are also represented as bitmaps, which further decreases their storage requirements. To optimize resource utilization, advanced I/O techniques are employed to limit the number of data pages loaded into memory. Each distinct value within the database is stored singularly, effectively reducing redundancy and enhancing overall efficiency. Consequently, this amalgamation of characteristics positions Sparksee as an exceptionally capable tool for conducting extensive graph data analyses, making it invaluable for researchers and data scientists alike.
What is Apache Pinot?
Pinot is designed to optimize the handling of OLAP queries with low latency when working with static data. It supports a variety of pluggable indexing techniques, such as Sorted Index, Bitmap Index, and Inverted Index. Although it does not currently facilitate joins, this can be circumvented by employing Trino or PrestoDB for executing queries. The platform offers an SQL-like syntax that enables users to perform selection, aggregation, filtering, grouping, ordering, and distinct queries on the data. It comprises both offline and real-time tables, where real-time tables are specifically implemented to fill gaps in offline data availability. Furthermore, users have the capability to customize the anomaly detection and notification processes, allowing for precise identification of significant anomalies. This adaptability ensures users can uphold robust data integrity while effectively addressing their analytical requirements, ultimately enhancing their overall data management strategy.
Integrations Supported
Integrations Supported
Amazon S3
Apache Kafka
Astro by Astronomer
Azure Data Lake
Hadoop
Hue
Kestra
Onehouse
OpenMetadata
StarTree
API Availability
API Availability
Pricing Information
Pricing not provided
Pricing Information
Pricing not provided
Supported Platforms
Android
iPhone
iPad
Windows
Mac
Linux
Supported Platforms
SaaS
Customer Service / Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Company Facts
Organization Name
Sparsity Technologies
Date Founded
2010
Company Location
Spain
Company Website
sparsity-technologies.com
Company Facts
Organization Name
Apache Corporation
Date Founded
1954
Company Location
United Statess
Company Website
pinot.apache.org
Categories and Features
Graph Databases
Not specified
Categories and Features
Columnar Databases
Not specified
OLAP Databases
Not specified
Real-Time Analytic Databases
Not specified