Big Data Query
When it comes to querying big data using standard SQL you have come to the right place as this is what Presto was designed to do. Presto, the leading open source distributed query engine, gives users the ability to interactively query any data and any data source at any scale. And it is proven – Presto has been in large-scale production deployment at internet-scale companies like Facebook, Uber, and Twitter for several years and the project is enjoying continued innovation as a result.
Ahana’s mission is to simplify ad hoc analytics for organizations of all shapes and sizes by making open source Presto a fully integrated, cloud-native, managed service for AWS – the easiest Presto experience there is.
Utilizing Big Data
The role of a data analyst, and those of other members within a data team, is to discover patterns, trends and correlations in large datasets. Large data analytics is the umbrella term for such activities as generating reports, creating dashboards, 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 or files, which may be in different file formats, in relational databases and in object storage like S3.
In the past, data analysts and data teams (which usually is comprised of a variation of members including: data engineers, data architects, and data scientists) typically queried data stored in traditional relational databases and data warehouses – like a data warehouse. 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 increasingly stored in the cloud and/or in distributed clusters. To query these huge and disperse datasets, you need a distributed query engine like 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 actively querying expansive and diverse datasets. It is an open source, distributed SQL query engine that is specifically designed to run big data queries 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.
One of the main benefits of 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.
This is why Presto is becoming the de facto standard for Big Data Querying.
A Comprehensive Guide to Data Warehouse Types
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. Learn more about what these data warehouse types are, what they are not, and the benefits they provide to data analytics team members within organizations.
Presto vs Snowflake: Data Warehousing Comparisons
Presto is an open-source SQL query engine, developed by Facebook, for large-scale data lakehouse analytics. Snowflake is a cloud data warehouse that offers a cloud-based information storage and an analytics service. Learn more about the differences between Presto and Snowflake in this article.