Share your experience using Interana

I mainly use Snowflake Analytics for creating the data lake in Snowflake, specifically the medallion architecture, where we have our data lake with different layers including staging, raw, and then the final layer where our data model sits, enabling us to do reporting on the top of this data model, and we also maintain sandboxes for AI/ML use cases.

Majorly, we have an entire data lake in Snowflake Analytics with a huge volume of data and perform data analysis and prepare reports for the same.

Regarding data model transformations, I have used features such as functions with tasks and user-defined functions, which help in transformation. Snowflake Analytics notebooks and Snowpark allow writing procedures in JavaScript, Python, or Java, providing strong support for transformations.

I am using Snowflake Analytics on the cloud.

There are many capabilities which Snowflake Analytics offers that I find valuable, such as the storage and compute engine that allows working with any cloud system such as AWS or Azure, alongside its efficiencies in storage computation and cost-effectiveness, which saves money compared to on-premise systems.

We also have features such as pre-cached results, Time Travel, and fail-safe, which are very useful for restoring data if deleted accidentally, and the streams and data pipes that facilitate real-time ingestion are great features as well.

Snowflake Analytics offers multiple new connectors, allowing me to connect it with Kafka, and with Snowpark, I can work with any programming language such as Python, Java, or Scala for data processing and analysis.

The data sharing feature offered by Snowflake Analytics is good because it allows sharing specific sets of data to end customers or users from different Snowflake Analytics accounts without exposing the entire dataset for data security reasons.

Snowflake Analytics' support for machine learning models and real-time insights has enhanced significantly. Originally, it wasn't strong in AI/ML, but now it has multiple models and forecasting capabilities, providing good competition to tools such as Databricks and Spark.

In BI, I have worked majorly with Microsoft Power BI, and the integration with Snowflake Analytics is very easy. The way we integrate Snowflake Analytics with other on-premise systems just requires the warehouse details, username, passwords, and the account name, along with multiple options such as client ID and credentials for logging in and creating a session.

The end-to-end encryption provided by Snowflake Analytics is very important because, in my previous firm, working in finance and investment management, data encryption is necessary due to the sensitive nature of customer data and the involvement of people's money. It's crucial to have encryption in transit and at rest, along with data masking features which Snowflake Analytics offers.

There are some minor issues encountered with Snowflake Analytics, such as problems when working with identity or row number generating different results or issues with referential integrity, which are being reported on the Snowflake Analytics website, indicating they are looking into these problems for future updates.

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.

I have been working with Snowflake Analytics for four years now.

Snowflake Analytics has been stable and reliable in my experience. In our production environment, it has been running for about four to five years without issues.

Maintaining security and data governance becomes easier with an entire data lake in place, and the scalability improves performance. We can scale the warehouses as needed based on query performance, and the overall cost-effectiveness enhances resource utilization while ensuring secure access to data sets and business use cases.

I often communicate with the technical support of Snowflake Analytics. Initially, for developers, we had a team from Snowflake Analytics that facilitated multiple sessions, and recently we had a two-day session where the Snowflake Analytics team provided a demo on Cortex AI and its features.

I would rate the technical support from Snowflake Analytics an eight based on professionalism and how helpful they are.

Positive

Earlier, we were predominantly working on an on-premise system. Besides Snowflake Analytics, we explored other options such as Databricks for creating a data lake, but Snowflake Analytics looked more promising.

I have used Snowflake Analytics functionalities extensively, working with semi-structured, unstructured, and structured data with all offerings such as stream, Snowflake Analytics pipelines, Snowflake Analytics Snowpipe, Snowflake Analytics notebooks, and Snowflake Analytics integration with Microsoft Power BI. Recently, I have started exploring the AI models and functions which Snowflake Analytics is offering, such as document extraction or image analysis using AI Cortex.

Snowflake Analytics has positively impacted my company by making data sharing easier among multiple applications and teams.

Snowflake Analytics is definitely affordable, which is why our organization decided to adopt it.

We save money transitioning from on-premise systems to Snowflake Analytics, benefiting from its cloud infrastructure, which offers scale-up and scale-down computing capabilities and enables having a data lake in one place.

Apart from pricing, factors such as performance, easy integration with the cloud, scalability, and cost-effectiveness influenced our decision to choose Snowflake Analytics.

I rate Snowflake Analytics a nine out of ten.

For Google Analytics 360, I can describe a few use cases and their purposes.

The customizable reporting feature is important in general, and it helps with making data-driven decisions.

The audience targeting feature in the product helps to improve advertising reach and engagement.

I can share positive impacts and benefits seen from Google Analytics 360.

My general rating for Google Analytics 360 is nine out of ten, where ten is the best. This is the most important question regarding room for improvement. They could add new features or enhance existing ones.

The product has been good so far without any user-friendliness issues.

Regarding stability, there haven't been any latency issues or glitches.

The product needs to be evaluated based on its scalability and potential limitations.

The technical support from Google has room for improvement.

Positive

The installation process needs to be evaluated to determine if it is easy or potentially tricky.

The pricing needs to be evaluated to determine if the solution is affordable or expensive, and if there are other analytics vendors being used currently or in the past.

Several positive aspects about the product stand out as advantages.

Valuable insights can be gained from the unsampled reports available in the product.

The cloud type needs to be specified whether it's public, private, or hybrid.

The overall rating for Google Analytics 360 is 9 out of 10, where 10 represents the best product.