Overview

Snowflake

Stacks1.2K

Followers1.2K

Votes27

Stitch

Stacks150

Followers150

Votes12

Snowflake vs Stitch: What are the differences?

# Introduction

## Key Differences between Snowflake and Stitch

1. **Data Warehousing vs. ETL Tool Integration**: Snowflake is a cloud-based data warehousing platform that offers scalable storage and computing capabilities for analyzing big data, while Stitch is an Extract, Transform, Load (ETL) tool that focuses on integrating data from various sources into a data warehouse or database.

2. **Data Transformation Capabilities**: Snowflake provides comprehensive data transformation features, including support for SQL, Python, and other programming languages, allowing for complex data processing tasks. In comparison, Stitch primarily focuses on data extraction, loading, and minimal transformation operations, lacking the advanced data manipulation capabilities of Snowflake.

3. **Cost Structure**: Snowflake's pricing model is based on credit usage and storage capacity, providing flexibility for users to scale their resources based on their needs. On the other hand, Stitch follows a subscription-based pricing model, which may be more suitable for organizations with a predictable data integration volume.

4. **Supported Data Sources**: Snowflake supports a wide range of data sources and integrations, including structured and semi-structured data from databases, data lakes, and real-time streams. Stitch, while versatile, has a more streamlined approach and focuses on seamless integration with popular databases and SaaS applications, offering simpler data source connectivity.

5. **Security and Compliance Features**: Snowflake emphasizes robust security features, such as encryption, access controls, and compliance certifications, ensuring data protection and regulatory compliance. However, Stitch provides basic security measures and may require additional configurations for organizations with strict security requirements.

6. **Compatibility and Ecosystem**: Snowflake integrates well with various data tools, BI platforms, and analytics services, making it a versatile choice for organizations with diverse data needs. In contrast, Stitch is designed for simplicity and ease of use, with limited integration options beyond basic data warehouse and database connections.

In Summary, Snowflake excels in data warehousing capabilities and advanced data processing, while Stitch focuses on streamlined data integration and ease of use for organizations with simpler data needs.

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Advice on Snowflake, Stitch

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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Comments

Detailed Comparison

Snowflake	Stitch
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.	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.
-	Connect to your ecosystem of data sources - UI allows you to configure your data pipeline in a way that balances data freshness with cost and production database load;Replication frequency - Choose full or incremental loads, and determine how often you want them to run - from every minute, to once every 24 hours; Data selection - Configure exactly what data gets replicated by selecting the tables, fields, collections, and endpoints you want in your warehouse;API - With the Stitch API, you're free to replicate data from any source. Its REST API supports JSON or Transit, and recognizes your schema based on the data you send.;Usage dashboard - Access our simple UI to check usage data like the number of rows synced by data source, and how you're pacing toward your monthly row limit;Email alerts - Receive immediate notifications when Stitch encounters issues like expired credentials, integration updates, or warehouse errors preventing loads;Warehouse views - By using the freshness data provided by Stitch, you can build a simple audit table to track replication frequency;Scalable - Highly Scalable Stitch handles all data volumes with no data caps, allowing you to grow without the possibility of an ETL failure;Transform nested JSON - Stitch provides automatic detection and normalization of nested document structures into relational schemas;Complete historical data - On your first sync, Stitch replicates all available historical data from your database and SaaS tools. No database dump necessary.
Statistics
Stacks 1.2K	Stacks 150
Followers 1.2K	Followers 150
Votes 27	Votes 12
Pros & Cons
Pros 7 Public and Private Data Sharing 4 Multicloud 4 User Friendly 4 Good Performance 3 Great Documentation	Pros 8 3 minutes to set up 4 Super simple, great support
Integrations
Python Apache Spark Node.js Looker Periscope Mode	Stripe Twilio SendGrid Zendesk MongoDB Marketo Recurly GitLab Zapier FreshDesk Harvest

What are some alternatives to Snowflake, Stitch?

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.

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.

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.