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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Presto vs Snowflake

Presto vs Snowflake

OverviewDecisionsComparisonAlternatives

Overview

Presto
Presto
Stacks394
Followers1.0K
Votes66
Snowflake
Snowflake
Stacks1.2K
Followers1.2K
Votes27

Presto vs Snowflake: What are the differences?

Presto and Snowflake are both popular data warehousing platforms used for analyzing large volumes of data. While they share some similarities, there are key differences that set them apart.

1. Data Storage and Architecture: One major difference between Presto and Snowflake lies in their data storage and architecture. Presto is designed to query data stored in different storage systems, such as Hadoop Distributed File System (HDFS) or cloud storage services, making it a more versatile choice. On the other hand, Snowflake has its own native storage system, which offers better performance and scalability for data warehousing workloads.

2. SQL Dialect and Compatibility: Another difference lies in the SQL dialect and compatibility. Presto uses a standard SQL dialect with support for features commonly found in relational databases, making it easier for SQL developers to work with. Snowflake, while also offering ANSI SQL compatibility, has its own unique syntax and features, which may require some learning curve for developers transitioning from other platforms.

3. Query Optimization: Presto and Snowflake also differ in their approach to query optimization. Presto performs query optimization at runtime, analyzing data statistics and adding optimizations on the fly. This allows for more dynamic decision-making during query execution. On the other hand, Snowflake utilizes a cost-based optimizer that optimizes and compiles queries before execution, resulting in potentially faster and more consistent performance.

4. Data Protection and Security Features: When it comes to data protection and security, Snowflake offers advanced features such as automatic data encryption, granular access controls, and role-based access management. These features ensure secure data storage and prevent unauthorized access. While Presto can also be secured using external tools, it may require additional configuration and setup for implementing similar levels of security.

5. Scalability and Concurrency: Snowflake is known for its excellent scalability and concurrency capabilities. With its multi-cluster architecture, Snowflake can handle thousands of simultaneous queries efficiently, making it a suitable choice for organizations with large user bases and high concurrency requirements. While Presto is also scalable, it may require manual configuration and optimization to achieve optimal performance under high-concurrency scenarios.

6. Cost Model and Pricing: Lastly, Presto and Snowflake differ in their cost model and pricing structure. Presto is an open-source framework that can be freely used, making it a cost-effective option for organizations with limited budgets. Snowflake, on the other hand, follows a subscription-based pricing model, which includes different pricing tiers based on storage and compute resource usage. This can be advantageous for organizations that require predictable costs and prefer a pay-as-you-go approach.

In summary, Presto and Snowflake differ in terms of data storage and architecture, SQL dialect and compatibility, query optimization, data protection and security features, scalability and concurrency, as well as cost model and pricing structure. Organizations should carefully consider these differences when choosing between these two platforms, based on their specific requirements and use cases.

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Advice on Presto, Snowflake

Ashish
Ashish

Tech Lead, Big Data Platform at Pinterest

Nov 27, 2019

Needs adviceonApache HiveApache HivePrestoPrestoAmazon EC2Amazon EC2

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

3.72M views3.72M
Comments
Karthik
Karthik

CPO at Cantiz

Nov 5, 2019

Decided

The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.

225k views225k
Comments

Detailed Comparison

Presto
Presto
Snowflake
Snowflake

Distributed SQL Query Engine for Big Data

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Statistics
Stacks
394
Stacks
1.2K
Followers
1.0K
Followers
1.2K
Votes
66
Votes
27
Pros & Cons
Pros
  • 18
    Works directly on files in s3 (no ETL)
  • 13
    Open-source
  • 12
    Join multiple databases
  • 10
    Scalable
  • 7
    Gets ready in minutes
Pros
  • 7
    Public and Private Data Sharing
  • 4
    Good Performance
  • 4
    User Friendly
  • 4
    Multicloud
  • 3
    Great Documentation
Integrations
PostgreSQL
PostgreSQL
Kafka
Kafka
Redis
Redis
MySQL
MySQL
Hadoop
Hadoop
Microsoft SQL Server
Microsoft SQL Server
Python
Python
Apache Spark
Apache Spark
Node.js
Node.js
Looker
Looker
Periscope
Periscope
Mode
Mode

What are some alternatives to Presto, Snowflake?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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