Overview :
In today’s world data has become the most important part of life and storing and using the data for different purposes has become an essential business objective. Thus due to which many technologies come into place and one of them is Data Analytics which is become a major of today’s industry. Data Analytics is a process of gathering relevant information which can help in better decision-making by analyzing raw data. But in the case of big data which consists of a large amount of data and to analyze a large amount of data becomes quite complex. Thus to minimize the complexity and enhance the process of analytic a new technology comes into a place known as Apache Presto.
Apache Presto :
Presto is a SQL query engine, which designed and developed by Facebook for the data analyst to run interactive queries on a large number of databases in Apache Hadoop. Presto architecture allows data sources from different sources such as AWS s3, MySQL and Teradata, etc. Presto has now become an open-source software for the use of the community under the Apache License. Presto has built-in java API’s which make it easy to integrate with various data infrastructure components. Presto is having the functionality of distributed parallel processing system due to which it is capable of processing interactive query analysis and at a low latency rate. Presto helps in avoidance several issues of java code related to memory allocation and garbage collection. Presto has a connector architecture that helps in the smooth running of the Hadoop system and also supports multiple Hadoop distributions.
Features of Apache Presto :
Here, we will discuss the features of Apache Presto as follows.
- The architecture of Presto is very playable and extensible to the new demand.
- Presto supports various pluggable connectors to provide metadata and data for different queries.
- Presto having the functionality of pipelining execution of the queries leads to avoidance of unnecessary Input/Output latency overhead.
- Presto also provides the functionality to data analysts to create user-defined functions according to the demand of the problem solution.
- Presto supports vectorized columnar processing which increases the efficiency of the query.
Advantages of Apache Presto :
Here, we will discuss the advantages of Apache Presto as follows.
- Presto runs the scale of the queries without downtime from gigabytes to petabytes.
- Presto is very simple and easy to understand and debug on your own computer.
- Presto supports ANSI SQL which makes it unique and popular among other analytic tools.
- Presto has built-in functionality in which each command is passed through a master coordinator that indicates the selection of nodes to run a job through the schedular.
- The memory engine of Apache Presto helps in processing a large amount of data in the fastest way.
Disadvantages of Apache Presto :
Here, we will discuss the disadvantages of Apache Presto as follows.
- Presto has priority queue-based query allocation,, thus some queries wait for a longer period of time to be processed.
- The design structure of Presto does not support the query related to joins for a large amount of database.
- Presto does leverage disk space rather than used memory for processing but generally, people prefer one system to use for all their purposes.
Applications of Apache Presto :
Here, we will discuss the applications of Apache Presto as follows.
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Airbnb –
Hundreds of employees present in Airbnb used the Apache Presto architecture to process large queries and thus forms an integral part of the organization.
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Teradata –
Teradata helps in providing end-to-end solutions in data analytics and data warehousing .teradata act as a contributor to Presto due it which the analytical needs of most of the company are fulfilled.