Feb 02, 2022
handling of semi-structured data like json. It has great support for json and we can write sql on json which is amazing. the performance on semi-structured data is little poor as compared to structured data but it is still great.
Snowflake and Snowflake Analytics compete in the data management and analytics category. Snowflake appears to have the upper hand due to its robust architectural features and flexibility in separating storage and computing, while Snowflake Analytics is noted for its strengths in data sharing and analytics user experience.
Features: Snowflake offers advanced architectural features such as multi-clustering, zero copy clone, and separation of storage and computing without downtime. It boasts strong performance, scalability, and a robust system architecture for handling large datasets seamlessly. Snowflake Analytics shares core capabilities with Snowflake, with particular strengths in data sharing and providing comprehensive tools for business analytics, enhancing the user experience through easy cloning and analytics capabilities.
Room for Improvement: Snowflake could enhance its spatial components and the Snowpipe auto-ingest process, while also improving pricing transparency for better cost forecasting. Snowflake Analytics needs to improve its integration with machine learning tools and make transactional processing more seamless. Both platforms would benefit from better documentation and a more intuitive user interface.
Ease of Deployment and Customer Service: Snowflake and Snowflake Analytics offer broad deployment options primarily on public clouds, with Snowflake having private cloud and hybrid options, while Snowflake Analytics is more limited in private deployment. Both provide responsive customer service, though Snowflake is noted for its technically adept staff, whereas Snowflake Analytics could improve its response times.
Pricing and ROI: Snowflake uses a pay-as-you-go pricing model based on usage, providing flexibility but facing critiques for complexity. Snowflake Analytics follows a similar model but tends to be more expensive, especially for larger computations, yet both platforms are seen as offering strong ROI for long-term, robust data management and analytics.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The technical support from Snowflake is very good, nice, and efficient.
Recently we had a two-day session where the Snowflake Analytics team provided a demo on Cortex AI and its features.
The technical support for Snowflake Analytics is excellent based on what I have heard from others.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
It supports both horizontal and vertical scaling effectively.
Maintaining security and data governance becomes easier with an entire data lake in place, and the scalability improves performance.
Storage is unlimited because they use S3 if it is AWS, so storage has no limit.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Snowflake Analytics has been stable and reliable in my experience.
Snowflake Analytics is stable, scoring around eight point five to nine out of ten.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
Cost reduction is one area I would like Snowflake to improve.
AIML-based SQL prompt and query generation could be an area for enhancement.
If it offered flexibility similar to Oracle and supported more heterogeneous data sources and database connectivity, it would be even better.
I would prefer Snowflake Analytics to improve their support response times, as sometimes the responses we receive are not very prompt and ticket assignments may not be timely.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
Snowflake Analytics is quite economical.
Snowflake is better and cheaper than Redshift and other cloud warehousing systems.
Snowflake charges per query, which amounts to a very minor cost, such as $0.015 per query.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
Being able to perform AI and Machine Learning in the same location as the data is quite advantageous.
Snowflake Analytics supports data security with a single sign-on feature and complies with framework regulations, which is highly beneficial.
Running a considerable query on Microsoft SQL Server may take up to thirty minutes or an hour, while Snowflake executes the same query in less than three minutes.
It is a data offering where I can see data lineage, data governance, and data security.
| Product | Market Share (%) |
|---|---|
| Snowflake | 17.7% |
| Snowflake Analytics | 1.1% |
| Other | 81.2% |
| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 20 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 12 |
| Large Enterprise | 21 |
Snowflake provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
Conventional data platforms and big data solutions struggle to deliver on their fundamental purpose: to enable any user to work with any data, without limits on scale, performance or flexibility. Whether you’re a data analyst, data scientist, data engineer, or any other business or technology professional, you’ll get more from your data with Snowflake.
To achieve this, we built a new data platform from the ground up for the cloud. It’s designed with a patented new architecture to be the centerpiece for data pipelines, data warehousing, data lakes, data application development, and for building data exchanges to easily and securely share governed data. The result, A platform delivered as a service that’s powerful but simple to use.
Snowflake’s cloud data platform supports a multi-cloud strategy, including a cross-cloud approach to mix and match clouds as you see fit. Snowflake delivers advantages such as global data replication, which means you can move your data to any cloud in any region, without having to re-code your applications or learn new skills.
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