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 warehousing and analytics domain. Snowflake holds an advantage due to its separate scaling for computational and storage needs and high scalability features, although Snowflake Analytics brings additional analytical capabilities with some complexity.
Features: Snowflake offers scalability, high performance, and flexible architecture that supports various formats like CSV and JSON. It includes unique features like Snowpipe for data ingestion and a robust relational framework. Its distributed architecture allows seamless adjustments of node and cluster sizes. Snowflake Analytics enhances this with more analytical functionalities, though it comes with increased complexity in its use.
Room for Improvement: Snowflake could improve geospatial query support and snowpipe auto-ingest, while its cost predictability needs better clarity. More connectors and an improved user interface are also needed. Snowflake Analytics requires enhanced machine learning and AI capabilities, better transactional processing, and smoother integration with Power BI and AWS.
Ease of Deployment and Customer Service: Both Snowflake and Snowflake Analytics offer easy deployment in public cloud environments thanks to their flexible cloud architecture. Snowflake excels in support with quick responsiveness and dedicated account managers, although Snowflake Analytics sometimes receives a slower response in support.
Pricing and ROI: Snowflake provides competitive pay-as-you-go pricing based on actual compute and storage usage, offering transparency and flexibility. Despite higher costs than Redshift, users find its pricing model satisfying due to its efficiency in reducing operational costs. Meanwhile, Snowflake Analytics can become expensive with higher computation needs but offers robust solutions that may justify the costs.
Snowflake Analytics has positively impacted our organization by saving about eight to ten hours per week, which we can use for advanced analytics and automation tasks.
We sought this documentation multiple times but faced difficulty in obtaining it.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
I am satisfied with the work of technical support from Snowflake; they are responsive and helpful.
The Snowflake Analytics documentation is excellent.
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.
Recently, Snowflake has introduced streaming capabilities, real-time and dynamic tables, along with various connectors.
Storage is unlimited because they use S3 if it is AWS, so storage has no limit.
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.
Snowflake is highly stable and performs well even with large data sets exceeding terabytes, maintaining stability throughout.
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 very stable; I have never experienced any crash downs or server issues.
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.
What things you are going with to ask the support and how we manage the relationship matters a lot.
If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial.
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.
When it comes to cloud support, the setup cost is very cheap compared to other platforms, such as Oracle or PostgreSQL, which typically require higher costs.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
Snowflake charges per query, which amounts to a very minor cost, such as $0.015 per query.
Snowflake is better and cheaper than Redshift and other cloud warehousing systems.
Snowflake Analytics is quite economical.
We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.
I have used the Snowflake Zero-Copy Cloning feature in the past while prototyping data in lower environments. This feature is helpful as it saves a lot of time during the data replication process.
Snowflake has contributed to significant cost savings.
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.
Snowflake Analytics supports data security with a single sign-on feature and complies with framework regulations, which is highly beneficial.
Previously, we faced issues with slow queries due to traditional systems, but within Snowflake, we can assign separate virtual warehouses for reporting as well as data processing, ensuring that it does not impact tool performance and does not delay reporting to business users.
| Product | Mindshare (%) |
|---|---|
| Snowflake | 15.1% |
| Snowflake Analytics | 3.3% |
| Other | 81.6% |
| Company Size | Count |
|---|---|
| Small Business | 30 |
| Midsize Enterprise | 20 |
| Large Enterprise | 61 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 13 |
| Large Enterprise | 23 |
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?
What Benefits Should You Look for?
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.
Snowflake Analytics offers advanced capabilities in data warehousing and cloud data migration, with support for machine learning and business intelligence tasks. Its scalable architecture supports large data volumes while enhancing cost efficiency through decoupled computation and storage.
As a flexible, managed environment, Snowflake Analytics enhances data sharing and integration across multiple cloud platforms. It allows seamless data pipeline creation, supports advanced analytics, and facilitates reporting and visualization. Despite facing integration challenges with legacy systems and complex queries, Snowflake's continuous improvements aim to address these issues, making it a reliable choice for organizations transitioning to the cloud.
What features define Snowflake Analytics?Enterprises across industries utilize Snowflake Analytics for its robust data handling and cloud integration capabilities. It serves sectors in need of efficient data warehousing, real-time analytics, and machine learning support, making it suitable for cloud migration and enhancing business intelligence operations.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
