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PostGIS VS Apache Spark

Compare PostGIS VS Apache Spark and see what are their differences

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PostGIS logo PostGIS

Open source spatial database

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • PostGIS Landing page
    Landing page //
    2021-12-18
  • Apache Spark Landing page
    Landing page //
    2021-12-31

PostGIS features and specs

  • Open Source
    PostGIS is open-source, meaning it is free to use and has a strong community support for development and troubleshooting.
  • Integration with PostgreSQL
    PostGIS extends PostgreSQL, a robust relational database management system, providing powerful geospatial capabilities along with traditional SQL features.
  • Rich Geospatial Functions
    PostGIS offers a comprehensive range of geospatial functions and data types, making it suitable for complex spatial queries and analyses.
  • Cross-platform Support
    Being cross-platform, PostGIS can run on various operating systems including Windows, Linux, and macOS, offering flexibility in deployment.
  • Active Community and Documentation
    PostGIS benefits from an active user community and extensive documentation, which aids in learning and problem-solving.
  • Scalability
    Built on PostgreSQL, PostGIS inherits its scalability features, which support large datasets and extensive query capabilities.
  • Customization and Extension
    PostGIS's open architecture allows for customization and the development of extensions to meet specific geospatial needs.

Possible disadvantages of PostGIS

  • Complexity
    The setup and maintenance of PostGIS can be complex for users without prior experience in PostgreSQL or geospatial databases.
  • Performance Overhead
    For extremely large datasets and very high-performance needs, the additional geospatial functionality can introduce some performance overhead.
  • Learning Curve
    There is a significant learning curve associated with mastering PostGIS, particularly for users who are not familiar with GIS or SQL.
  • Resource Intensive
    Running intensive geospatial queries can be resource-intensive, requiring significant memory and processing power.
  • Limited Advanced GIS Features
    While PostGIS offers extensive GIS features, it may fall short compared to specialized GIS software for certain advanced spatial analytics or visualization tasks.
  • Dependency on PostgreSQL
    As PostGIS is an extension to PostgreSQL, users are dependent on PostgreSQL updates and limitations, which might not always align with geospatial needs.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of PostGIS

Overall verdict

  • Yes, PostGIS is highly regarded for its capabilities in managing and analyzing spatial data. It is a powerful tool for those needing advanced spatial functionalities and is often recommended due to its open-source nature and extensive community support.

Why this product is good

  • PostGIS is considered a robust spatial database extender for PostgreSQL, offering extensive support for geographic objects, which enables it to manage and analyze spatial data efficiently. It provides a wide range of functions for spatial queries, including geometry and geography data types, and it supports spatial indexing and topological relationships. Its integration with PostgreSQL ensures reliability, scalability, and performance, making it a popular choice for GIS professionals.

Recommended for

  • Geographic Information System (GIS) professionals
  • Organizations managing spatial databases
  • Developers building applications requiring spatial data processing
  • Environmental scientists and urban planners
  • Businesses needing location-based data analysis

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

PostGIS videos

+ Add

Como Instalar o PostgreSQL com PostGIS | ALL com GEO

More videos:

  • Review - Paul Ramsey: This Is PostGIS
  • Review - A New Dimension To PostGIS : 3D

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to PostGIS and Apache Spark)

PostGIS

Apache Spark

Maps
100 100%
Maps
0% 0
Databases
0 0%
Databases
100% 100
Database Tools
100 100%
Database Tools
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare PostGIS and Apache Spark

PostGIS Reviews

The Top 10 Alternatives to ArcGIS
For those in the engineering and GIS community, PostGIS is a well-known open source extension for the PostgreSQL database that allows for spatial data to be stored, managed, and queried. The software enables users to conduct complex geospatial analyses and – because it is built on top of the powerful open-source database PostgreSQL – it can handle large datasets with ease....
Source: equatorstudios.com

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than PostGIS. While we know about 70 links to Apache Spark, we've tracked only 1 mention of PostGIS. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PostGIS mentions (1)

  • Efficient Distance Querying in MySQL
    This is an interesting article about strategies to use when traditional indexes just won't do, but for the love of the index please use MySQL's (or postgres' or sqlite's) built in spatial index for this particular class of problems. It will does this sort of thing much, much more efficiently than 99% of in house solutions. https://dev.mysql.com/doc/refman/8.0/en/spatial-types.html... - Source: Hacker News / over 3 years ago

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 2 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 2 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 4 months ago
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What are some alternatives?

When comparing PostGIS and Apache Spark, you can also consider the following products

Slick - A jquery plugin for creating slideshows and carousels into your webpage.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Sequel Pro - MySQL database management for Mac OS X

Hadoop - Open-source software for reliable, scalable, distributed computing

DataGrip - Tool for SQL and databases

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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