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Presto DB VS Apache Spark for Azure HDInsight

Compare Presto DB VS Apache Spark for Azure HDInsight and see what are their differences

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Presto DB logo Presto DB

Distributed SQL Query Engine for Big Data (by Facebook)

Apache Spark for Azure HDInsight logo Apache Spark for Azure HDInsight

This article provides an introduction to Spark in HDInsight and the different scenarios in which you can use Spark cluster in HDInsight.
  • Presto DB Landing page
    Landing page //
    2023-03-18
  • Apache Spark for Azure HDInsight Landing page
    Landing page //
    2023-03-17

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.

Apache Spark for Azure HDInsight features and specs

  • Scalability
    Apache Spark on Azure HDInsight can easily scale to handle large datasets by distributing data across multiple nodes, making it suitable for big data processing.
  • Integration with other Azure Services
    Apache Spark on Azure HDInsight seamlessly integrates with other Azure services like Azure Blob Storage, Azure SQL Database, and Power BI, enhancing its capabilities within the Azure ecosystem.
  • Real-time Data Processing
    Spark supports real-time data analytics, enabling faster processing using features such as Spark Streaming to handle data as it arrives.
  • Ease of Use
    HDInsight's managed Spark service simplifies cluster creation, configuration, and management, allowing users to focus more on data analysis rather than infrastructure.
  • Support for Multiple Languages
    Spark supports various programming languages such as Scala, Java, Python, and R, providing flexibility in how users can write their processing logic.

Possible disadvantages of Apache Spark for Azure HDInsight

  • Complexity in Tuning
    Despite its power, Spark can be complex to tune and optimize, which may require significant expertise to achieve optimal performance.
  • Cost
    Running Apache Spark on Azure HDInsight can become expensive, especially with large-scale deployments and continuous operations, requiring careful cost management.
  • Resource Management
    Efficient resource management can be challenging as Spark requires careful allocation of memory and CPU to ensure optimal job execution and performance.
  • Learning Curve
    For users new to big data technologies or the Spark ecosystem, there can be a steep learning curve associated with understanding and effectively using Spark on HDInsight.
  • Dependency on Azure
    While integration with Azure services is a pro, it also means a strong dependency on the Azure platform, which might not be ideal for organizations looking to remain cloud-agnostic.

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 Presto DB and Apache Spark for Azure HDInsight)

Presto DB

Apache Spark for Azure HDInsight

Data Dashboard
89 89%
Data Dashboard
11% 11
Big Data
0 0%
Big Data
100% 100
Database Tools
100 100%
Database Tools
0% 0
Big Data Analytics
100 100%
Big Data Analytics
0% 0

User comments

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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.

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 / about 2 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 / about 2 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 / 2 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 / 5 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 / about 3 years ago
View more

Apache Spark for Azure HDInsight mentions (0)

We have not tracked any mentions of Apache Spark for Azure HDInsight yet. Tracking of Apache Spark for Azure HDInsight recommendations started around Mar 2021.

What are some alternatives?

When comparing Presto DB and Apache Spark for Azure HDInsight, you can also consider the following products

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.

Snowflake - Snowflake is the only data platform built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.

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

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.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

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