PipelineDB vs PostGIS

Overview

PostGIS

Stacks381

Followers377

Votes30

GitHub Stars2.0K

Forks407

PipelineDB

Stacks8

Followers20

Votes0

PipelineDB vs PostGIS: What are the differences?

Real-time Data Processing: PipelineDB is designed for real-time data processing, allowing users to continuously update and query data as it streams in. In contrast, PostGIS is primarily focused on spatial data storage, retrieval, and analysis, lacking the real-time processing capabilities of PipelineDB.
Data Types: PostGIS specializes in handling geospatial data types such as points, lines, polygons, and geographic features, providing powerful spatial analysis functions. On the other hand, PipelineDB supports standard data types for real-time processing, without the specialized geospatial data handling abilities of PostGIS.
Storage and Indexing: PostGIS utilizes advanced spatial indexing techniques such as R-tree to efficiently store and retrieve spatial data. PipelineDB, being more geared towards real-time analytics, may not prioritize specialized storage and indexing mechanisms for geospatial data like PostGIS does.
Query Language: PostGIS extends PostgreSQL with additional geospatial functions and operators to manipulate spatial data. In comparison, PipelineDB focuses on enhancing PostgreSQL for real-time analytics capabilities rather than adding specialized geospatial functionalities like PostGIS.
Scaling and Performance: PostGIS is optimized for large-scale spatial data processing and analysis, offering robust performance for spatial queries on vast datasets. While PipelineDB excels in real-time data processing, it may not provide the same level of scalability and performance for geospatial operations as PostGIS.
Data Aggregation and Streaming: PipelineDB excels at continuous data aggregation and stream processing, enabling real-time analytics workflows that collect, process, and analyze streaming data. PostGIS, while versatile in spatial data handling, may not offer the same level of capabilities for real-time data aggregation and processing as PipelineDB.

In Summary, PipelineDB and PostGIS differ in their focus on real-time data processing, geospatial data types, storage and indexing, query language, scaling and performance, and data aggregation and streaming capabilities.

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Detailed Comparison

PostGIS	PipelineDB
PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.	PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.
Processing and analytic functions for both vector and raster data for splicing, dicing, morphing, reclassifying, and collecting/unioning with the power of SQL;raster map algebra for fine-grained raster processing;Spatial reprojection SQL callable functions for both vector and raster data;Support for importing / exporting ESRI shapefile vector data via both commandline and GUI packaged tools and support for more formats via other 3rd-party Open Source tools	No Application Code; Runs on PostgreSQL; Eliminate ETL; Efficient and Sustainable
Statistics
GitHub Stars 2.0K	GitHub Stars -
GitHub Forks 407	GitHub Forks -
Stacks 381	Stacks 8
Followers 377	Followers 20
Votes 30	Votes 0
Pros & Cons
Pros 25 De facto GIS in SQL 5 Good Documentation	No community feedback yet
Integrations
PostgreSQL	PostgreSQL

What are some alternatives to PostGIS, PipelineDB?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

Related Comparisons

PipelineDB vs PostGIS: What are the differences?

Real-time Data Processing: PipelineDB is designed for real-time data processing, allowing users to continuously update and query data as it streams in. In contrast, PostGIS is primarily focused on spatial data storage, retrieval, and analysis, lacking the real-time processing capabilities of PipelineDB.
Data Types: PostGIS specializes in handling geospatial data types such as points, lines, polygons, and geographic features, providing powerful spatial analysis functions. On the other hand, PipelineDB supports standard data types for real-time processing, without the specialized geospatial data handling abilities of PostGIS.
Storage and Indexing: PostGIS utilizes advanced spatial indexing techniques such as R-tree to efficiently store and retrieve spatial data. PipelineDB, being more geared towards real-time analytics, may not prioritize specialized storage and indexing mechanisms for geospatial data like PostGIS does.
Query Language: PostGIS extends PostgreSQL with additional geospatial functions and operators to manipulate spatial data. In comparison, PipelineDB focuses on enhancing PostgreSQL for real-time analytics capabilities rather than adding specialized geospatial functionalities like PostGIS.
Scaling and Performance: PostGIS is optimized for large-scale spatial data processing and analysis, offering robust performance for spatial queries on vast datasets. While PipelineDB excels in real-time data processing, it may not provide the same level of scalability and performance for geospatial operations as PostGIS.
Data Aggregation and Streaming: PipelineDB excels at continuous data aggregation and stream processing, enabling real-time analytics workflows that collect, process, and analyze streaming data. PostGIS, while versatile in spatial data handling, may not offer the same level of capabilities for real-time data aggregation and processing as PipelineDB.

PipelineDB vs PostGIS

Overview