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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Decisions about Amazon EMR and Snowflake
Julien Lafont

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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Pros of Amazon EMR
Pros of Snowflake
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Massive data handling
  • 1
    Great support
  • 7
    Public and Private Data Sharing
  • 4
    Multicloud
  • 4
    Good Performance
  • 4
    User Friendly
  • 3
    Great Documentation
  • 2
    Serverless
  • 1
    Economical
  • 1
    Usage based billing
  • 1
    Innovative

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What is Amazon EMR?

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

What is Snowflake?

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.

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What are some alternatives to Amazon EMR and Snowflake?
Amazon EC2
It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Amazon DynamoDB
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
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.
Azure HDInsight
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
See all alternatives