I used Amplitude mostly for data analytics and analysis.
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I used Amplitude mostly for data analytics and analysis.
I loved using Amplitude because it allowed me to dive deep into product usage and analyze different customer segments. My favorite part was pinpointing exactly where customers were dropping off in their journey and understanding the events tied to conversions. This helped me focus on areas needing improvement, conduct customer interviews, and validate insights with existing reviews, leading to more informed decisions.
In terms of improvement, setting up reports in Amplitude was challenging for me as a newer user. I relied on others who had specific knowledge, which sometimes caused delays due to resource constraints. It would have been helpful if the process of creating reports was more intuitive and user-friendly, allowing me to gain insights independently without relying heavily on the product team.
I have used Amplitude for a year and a half.
Amplitude was very stable during my use. I never encountered any errors, which was refreshing compared to the occasional issues I have experienced with AWS products.
I would rate Amplitude a solid ten for scalability. I didn't see any reasons why it couldn't handle scaling effectively.
Amplitude is easier to use than Splunk because its user interface is more intuitive and user-friendly. Splunk's interface felt technical and outdated, like an old mainframe system, while Amplitude's was much easier to navigate. Although Splunk might excel in detecting anomalies, Amplitude provided a smoother experience, especially for tasks like tracking performance over time.
Setting up Amplitude initially can vary in complexity. If you have all the necessary information, like event IDs and customer lists, and your requirements are straightforward, it's relatively straightforward. However, the complexity increases with more variables and larger datasets, making it a bit more challenging to work with.
Amplitude played a crucial role in improving product decisions for our onboarding project. By analyzing user behavior, we identified key points where customers were dropping off in the activation process. This allowed us to tailor the onboarding experience, making it more personalized and engaging, resulting in higher activation rates and improved customer retention.
Integrating Amplitude with other tools in our stack improved our analytics capabilities. I didn't encounter any issues with its compatibility or functionality alongside other tools.
I would recommend Amplitude to others because of its depth of analytics and powerful visualization capabilities. Overall, I would rate it as an eight out of ten.
We use it in the process of collecting access revenue data from various vendors. Our next step involves integrating this data into Snowflake tables, after which we present it through dashboards for visualization and analysis.
Snowflake Analytics serves as the primary data repository, fulfilling the data warehousing requirements of our customers. Therefore, all incoming data from our vendors is stored within Snowflake as part of our data warehouse.
Snowflake Analytics efficiently handles large-scale data analytics, which has been crucial for managing our current database volume. We're satisfied with the refresh rate and the loading process of new data into the tables, as it aligns with industry standards, leaving us with no complaints in this regard.
We have designated specific roles for administrative tasks, which are concealed from regular developers and production support personnel. Access control mechanisms are employed to grant access only to individuals who require it for administrative purposes, and they are the biggest benefits.
The transition to Snowflake has impacted our customers' overall data strategy and cost management significantly. Initially, we were using Redshift with AWS integration, but encountered challenges with scaling and compute costs. High data volumes led to increased costs for the entire solution. Therefore, we migrated to Snowflake, resulting in a reduction of costs by approximately thirty percent.
The most valuable features primarily revolve around data ingestion, such as copy commands. Additionally, we find Streams to be particularly useful in our operations, as they provide convenient functionalities.
The Snowflake features I find most beneficial for data analysis are primarily related to analytics, particularly their features like materialized views and queues, which are especially useful for dashboarding purposes. Another noteworthy addition is their new solution, hybrid tables.
The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms.
I have been working with it for three years.
In terms of stability, over the past three years, we haven't experienced any glitches in the production environment such as database unavailability. We have had a consistent and reliable performance without any issues, so there are no complaints in this regard. I would rate it eight out of ten.
It provides excellent scalability. I would rate it eight out of ten. The majority of our current clientele utilizes Snowflake, comprising approximately fifty to sixty percent of our client base. The rest of them still operate on legacy systems, with a gradual migration process underway. Our customers are enterprise businesses.
If I were to rate the support on a scale of one to ten, it would fall around six. Occasionally, there are delays in receiving responses, while at other times, we receive prompt assistance.
Neutral
The initial setup is complex and can be challenging to configure. I would rate it three out of ten.
For a medium-complexity dashboard, the deployment process typically takes around an hour to implement all the features. This estimate applies to mid-range features, excluding high-end functionalities. Our deployment process utilizes Git actions, seamlessly integrated within Git. Each deployment involves creating a pull request, through which Git actions are triggered to deploy changes to the master branch.
The pricing is on the higher side. I would rate it seven out of ten.
Since Snowflake primarily functions as a data warehouse, it is well-suited for OLAP solutions. Despite incorporating some OLTP features, Snowflake remains best suited for OLAP purposes. My advice to anyone considering Snowflake is to carefully evaluate the cost in comparison to their current solution. They should assess whether they would derive significant benefits from switching to Snowflake, especially if they are currently using platforms like Databricks or BigQuery. Comparing costs beforehand is crucial before making the transition to Snowflake. Overall, I would rate it seven out of ten.