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Apache Pinot VS Presto DB

Compare Apache Pinot VS Presto DB and see what are their differences

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Apache Pinot logo Apache Pinot

Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency.

Presto DB logo Presto DB

Distributed SQL Query Engine for Big Data (by Facebook)
Not present
  • Presto DB Landing page
    Landing page //
    2023-03-18

Apache Pinot features and specs

  • Real-time Analytics
    Apache Pinot is designed for real-time analytics on large-scale data. It is capable of ingesting data from streaming sources like Apache Kafka, providing low-latency query capabilities on freshly ingested data.
  • High Throughput
    Pinot can handle high query loads and large datasets efficiently. Its architecture is optimized for distributed processing and fast query execution, making it suitable for use cases with high query throughput requirements.
  • Columnar Storage
    Pinot utilizes a columnar storage format, which allows efficient compression and fast retrieval of highly selective query results, reducing I/O and improving query performance.
  • Scalability
    Pinot is highly scalable and can be deployed across a distributed infrastructure. This makes it suitable for both growing startups and large enterprises with expanding data needs.
  • Integration with Big Data Ecosystem
    Apache Pinot integrates seamlessly with other big data technologies like Apache Kafka, Hadoop, and Spark, making it easier for organizations to adopt it in existing tech stacks.

Possible disadvantages of Apache Pinot

  • Complex Setup
    Deploying and configuring a Pinot cluster can be complex, especially for organizations without experience in distributed systems, requiring careful planning and resources.
  • Maintenance Overhead
    Running a Pinot cluster involves ongoing maintenance tasks such as monitoring, scaling, and upgrading the system, which can add to the operational overhead.
  • Learning Curve
    Organizations may encounter a steep learning curve when adopting Apache Pinot, especially if team members are not familiar with its architecture and operational procedures.
  • Limited Use Cases
    While Pinot is powerful for real-time analytics, it may not be the best choice for transactional or general-purpose database use cases, limiting its applicability in certain scenarios.
  • Resource Intensive
    Running Pinot efficiently requires a significant amount of computational resources, which might be a concern for organizations with limited infrastructure or budget.

Presto DB features and specs

  • High-Performance Query Engine
    Presto is designed for high-performance querying, capable of performing complex analytics and large-scale data processing at interactive speeds.
  • Distributed SQL Query Engine
    Presto can scale out to large clusters of machines, allowing for efficient distribution of queries over multiple servers to handle big data workloads.
  • Versatility
    Supports querying data from multiple data sources such as Hadoop, relational databases, NoSQL databases, and cloud object storage within a single query.
  • ANSI-SQL Compatibility
    Presto supports ANSI SQL, making it easier for users familiar with SQL to adapt and write queries without a steep learning curve.
  • Open Source
    Presto is an open-source project, which means it benefits from continuous community contributions and improvements, keeping it up-to-date and robust.
  • Extensible
    Presto's architecture is designed to be extensible, allowing users to add custom functions and connectors, tailored to specific needs.

Possible disadvantages of Presto DB

  • Resource Intensive
    High performance comes with significant resource requirements, necessitating robust infrastructure to realize its full potential.
  • Complex Configuration
    Setting up and configuring Presto can be complex and time-consuming, often requiring expertise and an understanding of its various components.
  • Limited Support for Transactions
    Presto is primarily designed for reading data and performing analytics, and it has limited support for transactional processing compared to traditional relational databases.
  • Community Support
    While it has a vibrant open-source community, users may find the support less comprehensive than that provided by commercial enterprise solutions.
  • Latency for Small Queries
    Designed for big data and complex queries, Presto may exhibit higher latency for small, simple queries compared to specialized databases optimized for such use cases.
  • Maintenance Overhead
    Managing and maintaining a Presto cluster can be labor-intensive, requiring ongoing tuning and maintenance to ensure optimal performance and reliability.

Analysis of Presto DB

Overall verdict

  • PrestoDB is considered a strong choice for organizations needing to perform fast and complex analytic queries. Its ability to execute SQL queries on big data at lightning speeds makes it an attractive tool for data-driven organizations. However, the choice of PrestoDB depends on specific use cases, existing infrastructure, and the team's familiarity with its architecture and operational demands.

Why this product is good

  • PrestoDB is a highly-regarded distributed SQL query engine that excels in speed and efficiency for querying large datasets. It's designed for running interactive analytic queries against data sources of all sizes. Some of its core strengths include its ability to query data across a wide variety of sources, scalability, and strong community support. It's often chosen for its capability to integrate seamlessly in environments requiring fast data processing and analysis without the need to move or transform data extensively.

Recommended for

    PrestoDB is ideal for technology firms, data-driven companies, and organizations in need of real-time data analytics. It is especially well-suited for those with existing big data frameworks (like Hadoop, Kafka, and Cassandra) who require a performant query engine to leverage large datasets efficiently. It's recommended for teams familiar with distributed systems who need the flexibility and speed offered by PrestoDB's architecture.

Category Popularity

0-100% (relative to Apache Pinot and Presto DB)

Apache Pinot

Presto DB

Databases
36 36%
Databases
64% 64
Data Dashboard
5 5%
Data Dashboard
95% 95
Big Data
100 100%
Big Data
0% 0
Database Tools
0 0%
Database Tools
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 Apache Pinot and Presto DB

Apache Pinot Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
The biggest value behind Apache Pinot is that you can index each column, which allows it to process data at a super fast speed. “It’s like taking a pivot table and saving it to disk. So you can get this highly dimensional data with pre-computed aggregations and pull those out in what seems like supernaturally fast time,” says Tim Berglund, Developer Relations at StarTree....
Source: embeddable.com

Presto DB Reviews

We have no reviews of Presto DB yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Presto DB seems to be more popular. It has been mentiond 10 times since March 2021. 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.

Apache Pinot mentions (0)

We have not tracked any mentions of Apache Pinot yet. Tracking of Apache Pinot recommendations started around May 2025.

Presto DB mentions (10)

  • Data Warehouses and Data Lakes: Understanding Modern Data Storage Paradigms 📦
    Follow Presto at Official Website, Linkedin, Youtube, and Slack channel to join the community. - Source: dev.to / 5 months ago
  • Introduction to Presto: Open Source SQL Query Engine that's changing Big Data Analytics
    In today's data-driven world, organizations face a constant challenge: how to analyse massive datasets quickly and efficiently without moving data between disparate systems. Presto, an open-source distributed SQL query engine that's revolutionizing how we approach big data analytics. - Source: dev.to / 5 months ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    Presto: Presto is an open-source distributed SQL query engine that enables querying data from various sources. It provides fast and interactive analytics capabilities, supporting a wide range of data formats and integration with different storage systems. - Source: dev.to / 6 months ago
  • Using IRIS and Presto for high-performance and scalable SQL queries
    The rise of Big Data projects, real-time self-service analytics, online query services, and social networks, among others, have enabled scenarios for massive and high-performance data queries. In response to this challenge, MPP (massively parallel processing database) technology was created, and it quickly established itself. Among the open-source MPP options, Presto (https://prestodb.io/) is the best-known... - Source: dev.to / 9 months ago
  • Parsing logs from multiple data sources with Ahana and Cube
    Presto is an open-source distributed SQL query engine, originally developed at Facebook, now hosted under the Linux Foundation. It connects to multiple databases or other data sources (for example, Amazon S3). We can use a Presto cluster as a single compute engine for an entire data lake. - Source: dev.to / over 3 years ago
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What are some alternatives?

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

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Hashquery - A Python framework for defining and querying BI models in your data warehouse.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

ViyaDB - In-Memory Analytical Database

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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