

Snowflake and Apache Hadoop compete in the data management and processing category. Snowflake seems to have the upper hand with its advanced features and quicker time to ROI, though Apache Hadoop remains a strong contender due to its cost-effectiveness and scalability.
Features: Snowflake offers automatic scaling, SQL accessibility, and multi-format support. It also provides flexible configuration and a managed service operation with advanced data processing capabilities. Apache Hadoop is known for its scalability, cost-effectiveness due to being open-source, and robust data storage with fault tolerance, which is ideal for managing large datasets and unstructured data.
Room for Improvement: Snowflake could improve its cost transparency, user interfaces, and built-in analytics, as well as enhance integration for cost optimization and security measures. Apache Hadoop needs better integration with newer data technologies and more efficient incremental data processing. It also requires improved user interfaces and enhanced real-time processing capabilities.
Ease of Deployment and Customer Service: Snowflake is appreciated for its flexibility in deployment across public and private clouds, and strong customer service support. Apache Hadoop, primarily on-premises, delivers solid customer support, though users may experience longer wait times compared to commercial alternatives.
Pricing and ROI: Snowflake uses a consumption-based pricing model, which can be cost-effective with efficient use but may become expensive without careful management. Apache Hadoop's open-source licensing lowers costs significantly, though transitions to newer versions can incur expenses. Snowflake offers a quicker time to ROI with its advanced features, while both promise cost-effectiveness with the right use case.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
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.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
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.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
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.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
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.
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.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
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.
| Product | Market Share (%) |
|---|---|
| Snowflake | 10.1% |
| Apache Hadoop | 3.6% |
| Other | 86.3% |



| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 20 |
| Large Enterprise | 58 |
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.
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