AWS Redshift is a well-known data warehousing service that can handle exabytes of data. You might be thinking about using the service for real-time analytics, combining multiple data sources, log analysis, and other things.
Presto is a distributed SQL query engine that was built from the ground up for fast analytic queries against any size of data. Non-relational data sources like Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, MongoDB, and HBase, as well as relational data sources like MySQL, PostgreSQL, Amazon Redshift, Microsoft SQL Server, and Teradata, are supported.
This article talks about the different features of Redshift and Presto and gives an idea of Presto Vs Redshift.
Table of Contents
- What is Redshift?
- What is Presto?
- Understanding Presto Vs Redshift
- When to Use Redshift
- When to Use Presto
What is Redshift?
AWS Redshift is a cloud-based serverless data warehouse provided by Amazon as a part of Amazon Web Services. It is a fully managed and cost-effective data warehouse solution. AWS Redshift is designed to store petabytes of data and perform real-time analysis to generate insights.
AWS Redshift is a column-oriented database that stores the data in a columnar format compared to traditional databases stored in a row format. Amazon Redshift has its own compute engine to perform computing and generate critical insights.
To know more about AWS Redshift, follow the official documentation here.
AWS Redshift Architecture
AWS Redshift has straightforward Architecture. It contains a leader node and cluster of compute nodes that perform analytics on data. The below snap depicts the schematics of AWS Redshift architecture:
AWS Redshift offers JDBC connectors to interact with client applications using major programming languages like Python, Scala, Java, Ruby, etc.
Key Features of AWS Redshift
- Redshift allows users to write queries and export the data back to Data Lake.
- Redshift can seamlessly query the files like CSV, Avro, Parquet, JSON, ORC directly with the help of ANSI SQL.
- Redshift has exceptional support for Machine Learning, and developers can create, train and deploy Amazon Sagemaker models using SQL.
- Redshift has an Advanced Query Accelerator (AQUA) which performs the query 10x faster than other cloud data warehouses.
- Redshift’s Materialistic view allows you to achieve faster query performance for ETL, batch job processing, and dashboarding.
- Redshift has a petabyte scalable architecture, and it scales quickly as per need.
- Redshift enables secure sharing of the data across Redshift clusters.
- Even when thousands of queries are running at the same time, Amazon Redshift delivers consistently fast results.
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- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms such as WordPress, FTP/SFTP, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks, MySQL, TokuDB, DynamoDB, PostgreSQL Databases to name a few.
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
What is Presto?
Presto is an open-source SQL engine that is developed for distributed processing of data of any size. PrestoDB has excellent support for non-relational data sources like Amazon S3, Cassandra, HBase, MongoDB, etc, as well as relational data sources like MySQL, PostgreSQL, Redshift, and many more.
PrestoDB started as a project on Facebook, and later in 2013, they open-sourced it under Apache Foundation. The Data team at Facebook designs PrestoDB at Facebook to process 300 PB of data in the Hadoop ecosystem.
Presto follows the approach of querying the data where it resides, and hence it doesn’t move the data to a separate analytics system. It runs the query over the data parallelly and distributed and follows in-memory computation architecture. It is popular among big tech giants like Facebook, Netflix, Airbnb, Nasdaq, and many more.
Key Features of Presto
- Presto supports ANSI SQL to process the data, and hence it is easy to use by people familiar with SQL.
- Presto has multi-data source supports, making it easy for users to query the data either in file systems like S3 or in databases like MySQL.
- PrestoDB follows in-memory computation, and it performs fast distributed SQL query processing.
- Presto supports a DataDog Monitoring system that allows users to monitor the load on the system.
- PrestoDB can process PetaBytes of data and efficiently run an interactive query against it.
Understanding Presto Vs Redshift
- Presto Vs Redshift: Open Source
- Presto Vs Redshift: Customer Support
- Presto Vs Redshift: Data Sources
- Presto Vs Redshift: Pricing
- Presto Vs Redshift: Cloud Deployment
Now that we have a basic idea about Presto and Redshift let us compare the two on the different parameters to understand the logical differences between the two leading Data warehouse systems.
Presto Vs Redshift: Open Source
PrestoDB is an open-source distributed SQL query engine that queries the data they reside. Users can use Presto in an isolated fashion. PrestoDB comes as a roll-your-own model, which means users need their hardware and carry out installation and configurations. This takes a few days to make it a production-ready system.
Amazon Redshift is the proprietary tool of AWS infrastructure which provides a fully managed data warehouse solution. AWS Redshift comes as a ready-to-deploy model, which means users can use Redshift with just a few clicks, and it is production-ready.
Presto Vs Redshift: Customer Support
PrestoDB is an open-source tool, and hence there is no customer support available unless you’re using PrestoDB with the vendors like Teradata.
On the other hand, AWS Redshift is a fully managed solution provided by AWS; they have 24×7 customer support available.
Presto Vs Redshift: Data Sources
Presto has excellent support for non-relational data sources like AWS S3, Cassandra, HBase, MongoDB, and relational data sources like MySQL, Redshift, etc. With the help of a connector, Presto can be connected to Redshift and run interactive queries against the data.
On the other hand, AWS Redshift first migrates the data from other sources into its data warehouse and then runs the interactive query against the data.
Presto Vs Redshift: Pricing
Being open-source, PrestoDB comes free of cost. Users need to handle the hardware and infrastructure on their own.
On the other hand, AWS Redshift comes with different pricing plans such as Pay-as-you-go, Free tier, On-demand pricing, and many more. You can look for more pricing plans here.
Presto Vs Redshift: Cloud Deployment
Presto can be deployed on Cloud. For example, on AWS, users need to select an EC2/EMR machine to deploy Presto and later use the Presto cluster with Athena to query the data.
Redshift is already a fully managed cloud solution, and hence users can use it with just a few clicks. AWS will manage all the hardware, installation, and infrastructure.
When to Use Redshift
Redshift is a multi-tenant, fully managed cloud data warehouse provided by AWS. If you want a production-ready to go system and don’t want to manage the hardware and infrastructure, Redshift is a great choice. However, the cost associated with the same data and query is more in Redshift than in Presto.
When to Use Presto
Based on the above discussion, Presto DB is a great option if you want to move from a monolithic multi-tenant system like Redshift to a more robust multi-cluster architecture where you will get better tuning and hardware control. With the Presto DB, you can manage rach cluster to specific use and allocate resources differently depending on workload.
In this blog post, we compared the two leading data warehousing solutions, i.e., Presto vs Redshift, by different aspects and found out which scenario is best suited for Redshift and which one is best for Presto.
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