Qubole vs Snowflake

Overview

Qubole

Stacks36

Followers104

Votes67

Snowflake

Stacks1.2K

Followers1.2K

Votes27

Qubole vs Snowflake: What are the differences?

Introduction

Qubole and Snowflake are two popular cloud data platforms that offer different capabilities for organizations looking to manage and analyze their data efficiently.

Architecture: Qubole is a cloud-based data platform that offers a managed Hadoop environment, along with support for other big data processing frameworks like Spark and Presto. Snowflake, on the other hand, is a cloud data warehouse that is designed for high-performance querying and analytics on structured data. Snowflake's architecture separates storage and compute, allowing users to scale resources independently.
Job Scheduling: Qubole provides advanced job scheduling and workflow management capabilities, allowing users to automate and orchestrate data processing tasks more effectively. In contrast, Snowflake does not natively support job scheduling, and users often rely on external tools or services to manage job execution.
Data Processing Flexibility: Qubole is optimized for processing large volumes of unstructured and semi-structured data using various distributed computing frameworks. Snowflake, on the other hand, is a SQL-based data warehouse that excels at processing structured data for analytics and reporting purposes.
Cost Model: Qubole pricing is based on usage metrics like the number of compute hours and storage consumed, making it suitable for organizations with fluctuating workloads. Snowflake offers a usage-based pricing model as well, but also provides options for fixed-cost annual subscriptions, which may be more cost-effective for organizations with predictable data processing needs.
Data Sharing: Snowflake provides built-in support for secure data sharing between different accounts and organizations, allowing users to easily collaborate and exchange data with external parties. Qubole does not offer the same level of built-in data sharing capabilities, requiring users to implement custom solutions for sharing data securely.
Performance Optimization: Snowflake's architecture is optimized for query performance and can automatically scale compute resources based on workload demands. Qubole requires users to fine-tune resource allocations manually, which may require more expertise and effort to achieve optimal performance.

In Summary, Qubole and Snowflake differ in architecture, job scheduling, data processing flexibility, cost model, data sharing capabilities, and performance optimization.

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Advice on Qubole, 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

Qubole	Snowflake
Qubole is a cloud based service that makes big data easy for analysts and data engineers.	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.
Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management	-
Statistics
Stacks 36	Stacks 1.2K
Followers 104	Followers 1.2K
Votes 67	Votes 27
Pros & Cons
Pros 13 Simple UI and autoscaling clusters 10 Feature to use AWS Spot pricing 7 Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters 7 Real-time data insights through Spark Notebook 6 Easy to manage costs	Pros 7 Public and Private Data Sharing 4 Good Performance 4 User Friendly 4 Multicloud 3 Great Documentation
Integrations
Google Compute Engine Microsoft Azure	Python Apache Spark Node.js Looker Periscope Mode

What are some alternatives to Qubole, 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.

Amazon EMR

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

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.

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Qubole vs Snowflake: What are the differences?

Introduction

Qubole and Snowflake are two popular cloud data platforms that offer different capabilities for organizations looking to manage and analyze their data efficiently.

Architecture: Qubole is a cloud-based data platform that offers a managed Hadoop environment, along with support for other big data processing frameworks like Spark and Presto. Snowflake, on the other hand, is a cloud data warehouse that is designed for high-performance querying and analytics on structured data. Snowflake's architecture separates storage and compute, allowing users to scale resources independently.
Job Scheduling: Qubole provides advanced job scheduling and workflow management capabilities, allowing users to automate and orchestrate data processing tasks more effectively. In contrast, Snowflake does not natively support job scheduling, and users often rely on external tools or services to manage job execution.
Data Processing Flexibility: Qubole is optimized for processing large volumes of unstructured and semi-structured data using various distributed computing frameworks. Snowflake, on the other hand, is a SQL-based data warehouse that excels at processing structured data for analytics and reporting purposes.
Cost Model: Qubole pricing is based on usage metrics like the number of compute hours and storage consumed, making it suitable for organizations with fluctuating workloads. Snowflake offers a usage-based pricing model as well, but also provides options for fixed-cost annual subscriptions, which may be more cost-effective for organizations with predictable data processing needs.
Data Sharing: Snowflake provides built-in support for secure data sharing between different accounts and organizations, allowing users to easily collaborate and exchange data with external parties. Qubole does not offer the same level of built-in data sharing capabilities, requiring users to implement custom solutions for sharing data securely.
Performance Optimization: Snowflake's architecture is optimized for query performance and can automatically scale compute resources based on workload demands. Qubole requires users to fine-tune resource allocations manually, which may require more expertise and effort to achieve optimal performance.

In Summary, Qubole and Snowflake differ in architecture, job scheduling, data processing flexibility, cost model, data sharing capabilities, and performance optimization.