Amazon EMR vs Snowflake

Overview

Amazon EMR

Stacks542

Followers682

Votes54

Snowflake

Stacks1.2K

Followers1.2K

Votes27

Amazon EMR vs Snowflake: What are the differences?

Introduction

In this article, we will discuss the key differences between Amazon EMR and Snowflake. These two platforms offer different services and functionalities for data processing and analytics in the cloud. Understanding their differences can help organizations choose the right solution for their specific needs.

Scalability: Amazon EMR is designed for processing large-scale data sets using distributed computing and is highly scalable. It allows users to add or remove compute resources as needed to handle varying workloads. Snowflake, on the other hand, is a cloud-based data warehouse that offers instant scalability for data storage and analytics workloads. It uses a unique multi-cluster, shared data architecture to provide scalable performance to handle large datasets.
Data Warehouse vs. Data Processing Platform: Amazon EMR is primarily a data processing platform that allows users to run various big data processing frameworks like Apache Spark and Hadoop. It provides tools for data ingestion, processing, and analysis. Snowflake, on the other hand, is a fully-managed cloud-based data warehouse that supports SQL-based analytics and querying capabilities. It is optimized for handling structured and semi-structured data and provides excellent performance for analytical workloads.
Storage and Computation Separation: In Amazon EMR, data is stored in a separate storage layer like Amazon S3, and the computing resources are provisioned as needed for data processing tasks. This separation of storage and computation allows for efficient data processing and provides flexibility in choosing the storage layer. Snowflake, on the other hand, combines storage and computation in a single cloud-native data platform. Data is stored in Snowflake's proprietary storage format, and the compute resources are automatically provisioned by Snowflake based on the workload.
Concurrency and Performance: Amazon EMR provides varying levels of performance based on the compute resources provisioned and the selected big data processing frameworks. The performance can be optimized by fine-tuning the cluster configurations. Snowflake, on the other hand, guarantees excellent performance for concurrent users, as it automatically scales up or down the resources based on the workload. It uses a unique multi-cluster architecture to provide an elastic and scalable environment that can handle multiple workloads simultaneously.
Pricing Model: Amazon EMR follows an on-demand pricing model where users pay for the compute resources used and the storage consumed. Users can choose different instance types and sizes based on their requirements. Snowflake, on the other hand, follows a consumption-based pricing model where users pay for the storage used, the amount of data processed, and the compute resources used. Snowflake provides transparent pricing based on usage, which can be advantageous for organizations with variable workloads.
Security and Governance: Amazon EMR provides various security features like encryption, access controls, and integration with AWS Identity and Access Management (IAM) for secure data processing. Snowflake, on the other hand, provides comprehensive security features with built-in encryption at rest and in transit, fine-grained access controls, and support for enterprise-level security standards like SOC 2, GDPR, and HIPAA. It offers robust governance capabilities, including auditing, monitoring, and compliance reporting.

In summary, Amazon EMR is a scalable data processing platform that allows users to run big data processing frameworks, while Snowflake is a fully-managed cloud-based data warehouse optimized for analytics. Both platforms offer different features and functionalities, and the choice depends on specific requirements such as scalability, storage and computation separation, performance, pricing, and security.

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Advice on Amazon EMR, Snowflake

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Detailed Comparison

Amazon EMR	Snowflake
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.	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.
Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster;Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.;Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase.	-
Statistics
Stacks 542	Stacks 1.2K
Followers 682	Followers 1.2K
Votes 54	Votes 27
Pros & Cons
Pros 15 On demand processing power 12 Don't need to maintain Hadoop Cluster yourself 7 Hadoop Tools 6 Elastic 4 Backed by Amazon	Pros 7 Public and Private Data Sharing 4 Good Performance 4 User Friendly 4 Multicloud 3 Great Documentation
Integrations
No integrations available	Python Apache Spark Node.js Looker Periscope Mode

What are some alternatives to Amazon EMR, Snowflake?

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.

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

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.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

Airbyte

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

Treasure Data

Treasure Data's Big Data as-a-Service cloud platform enables data-driven businesses to focus their precious development resources on their applications, not on mundane, time-consuming integration and operational tasks. The Treasure Data Cloud Data Warehouse service offers an affordable, quick-to-implement and easy-to-use big data option that does not require specialized IT resources, making big data analytics available to the mass market.

Related Comparisons

Amazon EMR vs Snowflake: What are the differences?

Introduction

Scalability: Amazon EMR is designed for processing large-scale data sets using distributed computing and is highly scalable. It allows users to add or remove compute resources as needed to handle varying workloads. Snowflake, on the other hand, is a cloud-based data warehouse that offers instant scalability for data storage and analytics workloads. It uses a unique multi-cluster, shared data architecture to provide scalable performance to handle large datasets.
Data Warehouse vs. Data Processing Platform: Amazon EMR is primarily a data processing platform that allows users to run various big data processing frameworks like Apache Spark and Hadoop. It provides tools for data ingestion, processing, and analysis. Snowflake, on the other hand, is a fully-managed cloud-based data warehouse that supports SQL-based analytics and querying capabilities. It is optimized for handling structured and semi-structured data and provides excellent performance for analytical workloads.
Storage and Computation Separation: In Amazon EMR, data is stored in a separate storage layer like Amazon S3, and the computing resources are provisioned as needed for data processing tasks. This separation of storage and computation allows for efficient data processing and provides flexibility in choosing the storage layer. Snowflake, on the other hand, combines storage and computation in a single cloud-native data platform. Data is stored in Snowflake's proprietary storage format, and the compute resources are automatically provisioned by Snowflake based on the workload.
Concurrency and Performance: Amazon EMR provides varying levels of performance based on the compute resources provisioned and the selected big data processing frameworks. The performance can be optimized by fine-tuning the cluster configurations. Snowflake, on the other hand, guarantees excellent performance for concurrent users, as it automatically scales up or down the resources based on the workload. It uses a unique multi-cluster architecture to provide an elastic and scalable environment that can handle multiple workloads simultaneously.
Pricing Model: Amazon EMR follows an on-demand pricing model where users pay for the compute resources used and the storage consumed. Users can choose different instance types and sizes based on their requirements. Snowflake, on the other hand, follows a consumption-based pricing model where users pay for the storage used, the amount of data processed, and the compute resources used. Snowflake provides transparent pricing based on usage, which can be advantageous for organizations with variable workloads.
Security and Governance: Amazon EMR provides various security features like encryption, access controls, and integration with AWS Identity and Access Management (IAM) for secure data processing. Snowflake, on the other hand, provides comprehensive security features with built-in encryption at rest and in transit, fine-grained access controls, and support for enterprise-level security standards like SOC 2, GDPR, and HIPAA. It offers robust governance capabilities, including auditing, monitoring, and compliance reporting.

Amazon EMR vs Snowflake

Overview

Amazon EMR vs Snowflake: What are the differences?