The time constraint is a big barrier when it comes to driving insights from different and ever-enlarging Data Warehouses. Choosing the right tool for analytics operation in a timely manner is a new challenge faced by companies. Organizations are keen on investing in Big Data Analytics.
Analyzing large datasets, understanding the hidden pattern, and getting insights into market trends and customer behavior studies are new normals in the world of Big Data Analytics.
Snowflake and Presto provide query-engine services for Big Data Analytics. Both tools are well known, and tech giants such as Microsoft, Facebook, and Youtube use these tools for analytics purposes.
If you are in search of the right tool for Data Analytics and struggling to choose between Snowflake and Presto which tool can help, you landed in right place. This article aims to list down the important key aspects revolving around Snowflake vs Presto, such as pricing, architecture, scalability, etc., along with both of these tools.
Table of Contents
- What is Snowflake?
- What is Presto?
- Understanding Key Differences between Snowflake and Presto
- Which Companies are Using Snowflake?
- Which Companies are Using Presto?
What is Snowflake?
Snowflake is a Cloud-computing based Data Warehousing solution. Along with a Data Warehousing solution, Snowflake also provides data analytics ability. The Snowflake data platform is built on top of a brand new SQL query engine with an innovative architecture natively designed for the cloud.
However, what sets Snowflake apart is its architecture and data-sharing capabilities. The Snowflake architecture allows customers to scale storage and computing separately, allowing customers to consume and pay for storage and computing separately. The sharing feature also allows organizations to quickly and easily share managed and secure data in real-time.
Key Features of Snowflake
Here are some of the major benefits of leveraging Snowflake as a SaaS solution:
- Flexible Scalability: Snowflake multi-cluster architecture supports separating out compute and storage resources. This architecture leverages the ability to scale up, scale down, scale in and scale out as per business requirements. Users can easily scale up resources when they need huge volumes of data to load faster.
With Snowflake the users are privileged with auto-scaling capabilities that allow Snowflake to automatically begin and stop clusters during unexpected resource-intensive processing.
- No Hardware and Software Setup: Snowflake provides true SaaS offerings that can run completely on cloud infrastructure itself, which means you don’t need to install, configure, or manage any hardware or software. All software updates and installation are handled by Snowflake itself.
- Zero Administrative Effort Needed: Snowflake Data Warehousing solution is also known as a Data Warehouse as a Service (DWaas). It ensures that its users don’t have to invest significant resources in services such as Database Administration or set up dedicated IT teams to manage or set up the process. Advanced features like auto-scaling and virtual increase in warehouse-size, as well as increasing clusters, help to promote cluster management with minimum human intervention.
- Security and Data Governance: Snowflake comes with a wide range of security features including two-factor authentication, access control, secure data sharing, data encryption, and much more to offer.
You can read more about Snowflake here.
What is Presto?
Presto, or, PrestoDB is an open-source, distributed SQL query engine mainly designed for running fast analytic queries against data of sizes ranging from gigabytes to petabytes. Presto was introduced by Facebook in 2013 as a project to run interactive analytic queries against a 300PB Data Warehouse that was built with a large Hadoop/HDFS-based cluster ecosystem.
Presto supports not only non-relational data sources such as Cassandra, MongoDB, HBase, Hadoop Distributed File System(HDFS), and Amazon S3 but is also compatible with relational data sources such as MySQL, Amazon Redshift, Microsoft SQL Server, PostgresSQL, and Teradata. It also supports proprietary data stores.
You can combine data from multiple sources in a single Presto query, allowing you to analyze your entire organization. Analysts can also leverage response time ranging from sub-seconds to minutes with Presto. Presto provides fast analytics without having to rely on an expensive commercial solution or excessive hardware for processing.
It is important to note that Presto is not a database. You cannot store data in Presto, but you can use it as a computing engine for your Data Lakehouse. Presto can be used in public clouds as well as in private cloud infrastructures (on-premises or hosted). So, instead of storing data at a specific location, Presto follows the premise of querying data where it resides.
Key Features of Presto
The major features of Presto are as follows:
- No Data Movement for Analysis: Since Presto doesn’t have internal storage, it connects to other data stores and reads data from them. Presto can connect to various data stores, including all the popular SQL and NoSQL databases, as well as unmodeled data directly from S3 and HDFS.
- Reduce Wait Time for Preprocessing: The ability to read unmodeled raw data directly from S3 eliminates the need to wait for ETL to preprocess the data. Once saved in the Data Lake, it will be accessible. This gives you instant visibility into your current data, regardless of where the data is stored.
- Support for ANSI SQL: Presto is designed to support standard ANSI SQL semantics, including complex queries, aggregations, joins, left/right outer joins, sub-queries, window functions, distinct counts, and approximate percentiles.
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Understanding Key Differences between Snowflake and Presto
- Snowflake vs Presto: Architecture
- Snowflake vs Presto: Customer Support
- Snowflake vs Presto: Pricing
- Snowflake vs Presto: Source Code Availability
- Snowflake vs Presto: Data Security and Ownership
- Snowflake vs Presto: Scalability and Synchronization
Snowflake vs Presto: Architecture
The Snowflake architecture is a hybrid system that combines the characteristics of both the traditional shared disk architecture and the shared-nothing database architecture. Specifically designed natively for the cloud, it combines an innovative SQL query engine with three core layers: database storage, query processing, and cloud services.
A typical Presto deployment includes a Presto coordinator and any number of Presto workers.
- Presto Coordinator: Used by all Presto workers to submit queries and manage analysis, planning, and query execution.
- Presto Worker: Process the query, adding workers to speed up query processing.
Broadly speaking, the Presto architecture looks as shown in the image below:
Snowflake vs Presto: Customer Support
Snowflake is a fully managed solution; they have 24×7 customer support available along with comprehensive documentation for their users.
PrestoDB is an open-source tool, so customer support is not available unless you are using PrestoDB with a provider such as Ahana and Teradata.
Snowflake vs Presto: Pricing
Snowflake charges each virtual warehouse every hour, so pricing is highly dependent on usage patterns. Also, data storage and calculation are billed separately, so you need to consider storage costs after calculating usage costs.
Presto is an open-source analytics solution, no cost is required to use it. All required setup, maintenance, and error resolution are done by the user himself/herself.
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Check out what makes Hevo amazing:
- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Snowflake vs Presto: Source Code Availability
Snowflake is not open-source software. The data that has been aggregated and moved to Snowflake is in a unique format that is only available to Snowflake users. Passing all the data to Snowflake’s cloud data model is an ideal recipe for vendor lock-in.
PrestoDB is an open-source distributed SQL query engine that queries the data where it resides, with PrestoDB offered as a unique model. Therefore, the hardware can be used to install and configure a production-ready system within a few days.
Snowflake vs Presto: Data Security and Ownership
Snowflake offers industry-leading features that guarantee the highest levels of security for your accounts and users, as well as all the data you store in Snowflake. Security features like Network/site access, User & Group Administration, user authentication, Object security, Data Security, and Security validations are available in Snowflake.
Presto provides embedded system access control. The system access control plug-in enforces global level permissions before connector level permissions. You can use one of Presto’s built-in plugins or provide your own plugin according to system access control guidelines. Presto provides three built-in plugins: allow-all, read-only, and file.
- Allow- all: All operations are authorized under this plugin. This plugin is enabled by default.
- Read-only: This plugin allows you to perform all operations that read data or metadata, such as SELECT or SHOW. You can also set session properties at the system or catalog level. However, operations such as CREATE, INSERT and DELETE that write data or metadata are prohibited.
- File: This plugin allows you to specify access control rules in a file. Authorization checks are enforced using a config file specified by the configuration property security.config-file.
Snowflake vs Presto: Scalability and Synchronization
Snowflake puts a limit on the maximum number of concurrent users in a single virtual warehouse. If you have more than 8 concurrent users, you need to start another virtual warehouse. Query performance is good for simple queries, but applying more complex joins to larger datasets will slow down performance.
Presto is built for enforcing fast analytics queries on large datasets ranging from petabytes to terabytes. Along with fast response time, Presto offers 10-50 concurrent queries at a time.
Which Companies are Using Snowflake?
Currently, about two-fifths of Fortune 500 companies use Snowflake software. Leading cloud computing giants such as Microsoft, Amazon, and Alphabet’s Google are using Snowflake software at their production level.
Which Companies are Using Presto?
Tech giant organizations like Facebook, Airbnb, Netflix, Atlassian, Nasdaq, and many more are using Presto in production on a very large scale.
Facebook’s Presto implementation is used by over 1,000 employees, runs over 30,000 queries, and processes petabytes of data every day. Netflix runs about 3,500 queries per day on average in Presto clusters. Airbnb builds and opens sources Airpal, a web-based query execution tool built on Presto. A wider Presto community can be found on this forum and on Presto’s Facebook page.
In this blog post, we compared the two leading Data Analytics solutions, i.e., Snowflake vs Presto, by different aspects and found out which scenario is best suited for Snowflake and which one is best for Presto.
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