Presto Hadoop

Presto is an open-source, distributed SQL query engine designed for high-performance, interactive query processing on a variety of data sources, including Hadoop Distributed File System (HDFS) and other data lakes. It was developed by Facebook and is now an Apache Software Foundation project. Here’s how Presto interacts with Hadoop:

Presto and Hadoop Integration:

1. Hive Connector: Presto provides a Hive connector that allows it to query data stored in HDFS using Hive’s metadata. This means that Presto can interact with tables defined in the Hive metastore, including external tables that reference data in HDFS.

2. SQL Queries: With Presto, you can write SQL queries to analyze and process data in HDFS, just like you would with a traditional relational database. Presto optimizes these queries for performance and parallel execution across a cluster of machines.

3. High Performance: Presto is known for its speed and low-latency query execution. It can efficiently handle ad-hoc queries and interactive data exploration, making it suitable for real-time analytics on Hadoop data.

4. Connectivity: Presto supports connections to various data sources, including HDFS, Hive, relational databases, cloud storage services, and more. This flexibility allows you to join data from multiple sources within a single query.

5. Hadoop Ecosystem Integration: Presto can work alongside other Hadoop ecosystem tools and frameworks like Apache Spark, Apache HBase, and Apache Kafka. It complements these technologies by providing fast SQL querying capabilities.

6. User-Friendly: Presto’s SQL interface makes it user-friendly for data analysts and data scientists who are already familiar with SQL. It abstracts the complexities of working with distributed data.

7. Schema Evolution: Presto supports schema evolution, allowing you to query data even as its schema evolves over time. This is crucial in big data environments where data structures may change frequently.

8. Resource Management: Presto provides resource management features, such as query prioritization and resource allocation, to ensure that important queries get the necessary resources.

Use Cases:

• Interactive Analytics: Presto is well-suited for interactive analytics on large datasets, enabling users to run complex SQL queries in real time on Hadoop data.

• Data Exploration: Data analysts and scientists can use Presto to explore and analyze data in HDFS without needing to move or preprocess the data.

• Business Intelligence: Presto can serve as a SQL query engine for business intelligence tools, allowing organizations to run ad-hoc queries and generate reports on their Hadoop-stored data.

• ETL Processing: Presto can be used in ETL (Extract, Transform, Load) pipelines to transform and filter data stored in HDFS before loading it into other systems.

• Data Lake Querying: Organizations that have built data lakes on Hadoop can use Presto to query and analyze the data stored in their data lakes efficiently.