Presto Big Data Query

The role of a big data analyst is to discover patterns, trends and correlations in large datasets. Big data analytics is the umbrella term for such activities as generating basic reports to running complex queries. A big data query is a request for data from a database table or from a combination of many different tables. 

In the past, analysts typically queried data stored in traditional relational databases and data warehouses. These days, the amount of data being generated is too big to be stored solely in on-prem relational databases; as a result, big data is now also stored in the cloud and/or in distributed clusters. To query these huge and disperse datasets, you need a distributed query engine such as Hive or Presto.

The good news is, if you work with terabytes or even petabytes of data, Presto is the ideal high-performance, distributed SQL query engine for querying huge and diverse datasets. It’s an open source, distributed SQL query engine that is specifically designed to run big data queries or a bigdata query against data of any size, on a wide variety of sources. It makes use of multiple connectors which allow you to access these data sources and query them in place; there is no need to move the data. Presto big data queries are widely used.

One of the main benefits of big data Presto is that it separates data storage from processing, so analysts can query big data where it is stored without having to move all of the data into a separate analytics system. With just a single Presto query, you can query big data from multiple sources with fast response times ranging from sub-seconds to minutes. Another benefit of Presto is that big data analysts can use standard query language (SQL) to query the data, which means they don’t have to learn any new complex languages.