Apache Presto Course And Certification

Data is a major driving force for businesses and organization. Without data, the best companies can do is to guestimate, and guestimates aren’t reliable – data doesn’t lie. However, when this data become huge, reaching petabyte levels, managing them becomes difficult thus making conventional data management system ineffective.

In 2012, the world’s largest data warehouse; Facebook laid the foundation of what is today known as Apache Presto. Apache Presto was developed for the singular purpose of making management of huge data easy. Aside Facebook, Netflix and Dropbox have also stepped up to implement Apache Presto for their data management.

Simply put, Apache Presto is a Java drive query execution engine running on Hadoop distribution system, used in data analytics and processing. The Hadoop architecture gives this tool a lot of robustness.

Talking about robustness, Apache Presto has no match. It can process and query MySQL, Hive, and even HDFS data irrespective of their size without breaking into sweat. 

Apache Presto’s unique selling point lies in its ability to process huge chunk of data without delay, hence solving the problem of latency. Additionally, the difficulty in scaling data – a problem innate in most other data processing application – is almost non-existent in Apache Presto. 


Features and Benefits of Apache Presto 

+ Free of bug problems that plague other programming application

+ Scales data efficiently with little time loss

+ Has a simple to understand architecture 

+ Free of latency problems, thanks to its simple architecture 

+ Easy of creating customized functions on the fly 

+ Ease of storage 

+ Ease of installation, as well as operation

+ Comes with a robust SQL library

+ Can support multiple data source including, but not limited to, Hive, Cassandra, and Elastic Search.


Getting Started with Presto

The very first thing a user will have to do before using Apache Presto is to do some configurations. The lines of code below will get the job done: 
When the need to add Cassandra connectors arises, use the following lines of code:
cassandra.contact-points=<comma separated IPs>

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