Presto DB

Website

prestodb.io

$ Details

Categories

#Database Tools #Data Dashboard #Big Data Analytics #SQL #Big Data Tools #Big Data #SQL Query Engine

Apache Druid

Website

druid.apache.org

$ Details

Categories

#Databases #Relational Databases #Big Data #Data Analysis

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 Druid features and specs

• Real-Time Data Ingestion
  Apache Druid supports real-time data ingestion, which allows users to immediately query and analyze freshly ingested data, making it ideal for applications that require up-to-the-minute insights.
• High Performance
  Druid is designed to provide fast query performance, especially for OLAP (Online Analytical Processing) queries. Its architecture leverages techniques like indexing, compression, and shard-based parallel processing to deliver quick results, even on large data sets.
• Scalability
  Druid's architecture allows it to scale horizontally, supporting both large amounts of data and numerous concurrent queries. This makes it suitable for systems that need to handle high scalability requirements.
• Flexible Data Exploration
  It supports complex queries, including group-bys, filters, and aggregations, which are essential for exploratory data analysis. Users can perform a wide range of data slicing and dicing operations.
• Rich Multi-Tenancy Support
  Druid supports multi-tenancy, enabling different user groups to access and query the database simultaneously without performance degradation, thus accommodating diverse data analytics requirements within the same system.

Possible disadvantages of Apache Druid

• Complex Setup and Configuration
  Setting up and configuring Apache Druid can be complex and resource-intensive. It requires a good understanding of its architecture and components, which may pose a steep learning curve for beginners.
• Resource Heavy
  Druid can be resource-intensive, often requiring significant CPU, memory, and disk resources, especially when handling large scale data and high query loads. This can result in increased infrastructure costs.
• Limited Transactional Support
  Druid is not designed for transactional workloads and lacks full ACID compliance. It is optimized for read-heavy analytical queries rather than write-heavy transactional operations.
• Complexity in Handling Updates
  Updating or deleting existing records in Druid is not straightforward and often involves re-indexing data. This can complicate use cases where mutable data is a common requirement.
• Limited Tooling and Ecosystem
  Compared to more established databases and analytical engines, Druid's ecosystem and available tooling for development, monitoring, and management might be less extensive, potentially requiring custom solutions.

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An introduction to Apache Druid

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