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Presto VS Apache Spark

Compare Presto VS Apache Spark and see what are their differences

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Presto logo Presto

Next generation front-of-house technology

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.
  • Presto Landing page
    Landing page //
    2023-05-10
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Presto features and specs

  • Scalability
    Presto is highly scalable, allowing it to perform queries on large datasets across various data sources without data movement. It's designed to handle petabytes of data efficiently.
  • High Performance
    Presto provides low latency in query execution, making it suitable for interactive analytics. It optimizes query processing with techniques like in-memory computation and distributed query execution.
  • Flexibility
    It supports querying data from a wide variety of data sources including HDFS, S3, SQL databases like MySQL and PostgreSQL, and NoSQL databases like Cassandra, making it highly versatile.
  • SQL Compliance
    Presto uses ANSI SQL, providing a smooth transition for users familiar with SQL, thus lowering the learning curve.
  • Open Source
    Being open-source, Presto benefits from community contributions and support, allowing rapid development and innovation.

Possible disadvantages of Presto

  • Complex Setup
    Setting up and configuring Presto can be complex, requiring a fair understanding of its architecture and integration points, particularly for large-scale deployments.
  • Resource Intensive
    Presto can require significant resources, especially memory, due to its focus on in-memory processing, which can be a constraint on resource-limited environments.
  • Maintenance Overhead
    Managing and maintaining Presto clusters involves overhead in terms of monitoring, tuning, and upgrading, which can be demanding compared to hosted solutions.
  • Community Support Limitations
    While it has an active community, Presto lacks the level of enterprise support and comprehensive documentation available from proprietary solutions, which can be a barrier for some organizations.
  • Limited User Management
    Presto has limited built-in user management and security features, necessitating additional tools or systems to handle these aspects effectively.

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 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.

Presto videos

+ Add

Nike Presto React 2019 Review & On Feet

More videos:

  • Review - OFF WHITE NIKE AIR PRESTO REVIEW
  • Review - Nike Air Presto "Black Out" Unboxing Review

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 Presto and Apache Spark)

Presto

Apache Spark

Databases
8 8%
Databases
92% 92
Relational Databases
100 100%
Relational Databases
0% 0
Big Data
0 0%
Big Data
100% 100
Data Warehousing
100 100%
Data Warehousing
0% 0

User comments

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Reviews

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

Presto Reviews

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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 Presto. While we know about 70 links to Apache Spark, we've tracked only 1 mention of Presto. 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.

Presto mentions (1)

  • McDonald's is ending its drive-thru AI test
    I remember seeing a video from WSJ about the drive-thru AI chatbot from https://presto.com/ being used in a fastfood restaurant, to be honest it works quite well, I wonder if McDonald's will switch to them. - Source: Hacker News / 12 months 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 1 month 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 1 month 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 / 3 months ago
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What are some alternatives?

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

VeloDB - Modern Real-Time Data Warehouse

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

Snowflakepowe.red - Snowflake Computing is delivering a data warehouse for the cloud.

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

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

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

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