Find out what your peers are saying about Snowflake Computing, Google, Databricks and others in Cloud Data Warehouse.
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.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
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.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
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.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
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.
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.
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 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.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
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.
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 22 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 13 |
| Large Enterprise | 23 |
Apache Hadoop provides a scalable, cost-effective open-source platform capable of handling vast data volumes with features like HDFS, distributed processing, and high integration capabilities.
Apache Hadoop is known for its distributed file system HDFS, which supports large data volumes efficiently. Its open-source nature allows cost-effective scalability and compatibility with tools like Spark for enhanced analytics. While it offers significant processing power, areas for improvement include user-friendliness, interface design, security measures, and real-time data handling. Users benefit from data storage for structured and unstructured data, facilitated by its distributed processing architecture. Data replication ensures fault tolerance, while its capability to integrate with tools like Apache Atlas and Talend highlights its versatility.
What are the key features of Apache Hadoop?Industries leverage Apache Hadoop for Big Data analytics, data lakes, ETL tasks, and enterprise data hubs, handling unstructured and structured data from IoT, RDBMS, and real-time streams. Its applications extend to data warehousing, AI/ML projects, and data migration, employing tools like Apache Ranger, Hive, and Talend for effective data management and analysis.
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.
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