PrestoDB Drives Massive Value for Enterprise Use
The ever-evolving technology has the power to completely change the world around us, including the modern business landscape. In fact, we can say that a new industrial revolution is afoot. Technologies such as cloud-based technologies, quantum computing, machine learning and artificial intelligence, data analytics, big data, and IoT will have the last say in everything we do.
They will be the precursors of modern digitization, driving the subsequent economic activity and global GDP. Since the entire modern world depends on data analytics and science, gathering as much data as possible has become the ultimate goal of every business going through digital transformation.
However, data collection and structuring come with a range of challenges. Gathering and analyzing huge chunks of data isn’t only time-consuming but also very expensive. So, how can companies collect, structure, and analyze massive amounts of data in the most cost-effective manner? The answer couldn’t be simpler – PrestoDB.
What Is PrestoDB?
PrestoDB is a distributed/shared, open-source SQL query engine designed to handle data analytic queries at any scale. It interfaces both relational and non-relational database sources like MS SQL Server, PostgreSQL, MySQL, HBase, MongoDB, Hadoop HDFS, and Amazon S3. The biggest advantage of using PrestoDB is that it can gather data regardless of its storage destination/location.
In other words, the user doesn’t need to transport data into a separate, structured channel like a data warehouse or a relational database. In addition, PrestoDB uses a parallel architecture instead of a scalable, memory-based structure. This feature is particularly useful for enterprises as it allows PrestoDB to gather and analyze impressive amounts of data in mere seconds.
Because of all these data-handling perks, PrestoDB has easily become a number one option for an array of businesses with specific big data pipeline needs that run interactive data queries on Hadoop and AWS S3.
The best example of how effective PrestoDB can be is Facebook. The biggest social media network in the world uses PrestoDB to gather, analyze and process 1PB of data daily.
Main Reasons to Adopt PrestoDB
Running analytics on massive chunks of data remains the main goal of countless businesses today. Since PrestoDB can run such analytics cost-efficiently and fairly quickly, it has various use cases across different industries. Let’s see some of the most important characteristics of PrestoDB to explain why you should adopt this excellent data science technology.
1. It’s easy to integrate PrestoDB with your ecosystem
Integrating PrestoDB with your ecosystem is almost effortless due to its seamless integration properties. It doesn’t require any modification to your ongoing ecosystem. PrestoDB simply adds a faster data access interface to your system by providing another computing layer for much faster data analytics.
Since Presto doesn’t need to store data to analyze it, it gives you the advantage of resource scalability for handling massive data resources on demand. More importantly, this advantage makes PrestDB an excellent solution for cloud environments. Cloud deployments allow a business to auto-scale to optimize resource costs.
2. Unified SQL interface for querying data from multiple sources
When it comes to data analysis, SQL isn’t only the oldest but also the most popular language used for analyzing data. SQL is an excellent solution for building data dashboards and exploring data. Since PrestoDB is essentially a federated data query engine, it can check data accuracy and validity from NoSQL sources such as Kafka, RDBMS, Elasticsearch, and Cassandra, as well as distributed file systems.
3. Key features for top performance
PrestoDB is a high-end data query engine solution designed for top performance by providing a myriad of cutting edge data science optimizations and features, such as:
- Data pipeline execution
- In-memory processing
- Code generation
- Shareable Java Virtual Machine process on worker nodes
- Unified SQL interface
- Data analytics without storing
Because of such features, PrestoDB is a better data query solution than Apache Spark or Apache Hive. Both Apache solutions can handle vast chunks of data but are not as efficient or cost-effective as Presto interactive queries.
4. Query federation
PrestoDB has one advantage over any other similar data query solution – it provides a unified SQL interface that is compatible with all supported data sources. Thanks to this feature, PrestoDB allows users to extract data from a range of underlying systems without the need to understand SQL dialects and connections of the said systems.
5. Cloud-enabled design
Finally, PrestoDB is designed to run computing and storage separately, making it the most convenient tool for managing data in cloud environments. You can run multiple Presto clusters simultaneously since it doesn’t store any data.
More importantly, you can auto-scale your PrestoDB projects depending on the amount of data for processing without any data loss. Thanks to these numerous advantages, PrestoDB is an excellent solution for data-driven enterprises that need to cut costs on data science strategies.
Presto allows these companies to auto-scale any data science project regardless of the supported source. It completely eliminates the need to move or store data for further processing. Aside from being an excellent solution for cloud environments, Presto helps companies save time, effort, and resources while running data analytics across sources.
PrestoDB is an excellent data query tool for businesses that need to directly interface with various data sources, including relational databases like Microsoft SQL Server and MySQL, and raw data stored in HDFS data blocks and AWS S3 data lakes.
It provides businesses with the processing and methodology required to analyze real-time data and ensures that data is analyzed with low overhead, cost-effectively, and promptly in cloud-based architectures and storage.