Presto DB

Website

prestodb.io

$ Details

Categories

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

Apache Parquet

Website

parquet.apache.org

$ Details

Categories

#Databases #Big Data #Key-Value Database #Data Management

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 Parquet features and specs

• Columnar Storage
  Apache Parquet uses columnar storage, which allows for efficient retrieval of only the data you need, reducing I/O and improving query performance on large datasets.
• Compression
  Parquet files support efficient compression and encoding schemes, resulting in significant storage savings and less data to transfer over the network.
• Compatibility
  It is compatible with the Hadoop ecosystem, including tools like Apache Spark, Hive, and Impala, making it versatile for big data processing.
• Schema Evolution
  Parquet supports schema evolution, allowing changes to the schema without breaking existing data, which helps in maintaining long-lived data pipelines.
• Efficient Read Performance for Aggregations
  Due to its columnar layout, Parquet is highly efficient for processing queries that aggregate data across columns, such as SUM and AVERAGE.

Possible disadvantages of Apache Parquet

• Write Performance
  Writing data to Parquet can be slower compared to row-based formats, particularly for small inserts or updates, due to the overhead of encoding and compression.
• Complexity in File Management
  Managing and partitioning Parquet files to optimize performance can become complex, particularly as datasets grow in size and complexity.
• Not Ideal for All Workloads
  Workloads that require frequent row-level updates or involve small queries might be less efficient with Parquet due to its columnar nature.
• Learning Curve
  The need to understand the nuances of columnar storage, encoding, and compression can pose a learning curve for teams new to Parquet.

Analysis of Presto DB