I appreciate Hex for its impressive agent integration and the ability to seamlessly alternate between SQL and Python cells, which stands out as a killer feature for me. This functionality allows me to extract data and plot it immediately, significantly enhancing my workflow efficiency, especially in creating reports starting from raw data. The ease of the initial setup is another aspect I found commendable, as it was 'super'. Overall, Hex has been a valuable tool in my work, leading me to highly recommend it with a perfect score of 10 out of 10. Review collected by and hosted on G2.com.

I find that the kernel timeout messages in Hex could be clearer, which currently causes some confusion, especially when working under tight deadlines. Additionally, the publishing options available are not as comprehensive as they could be, which limits the flexibility and reach when sharing reports or results. Review collected by and hosted on G2.com.

I use Hex for a wide variety of analytics, data science, and data transformation tasks, which significantly broadens its applicability in my work. I particularly appreciate how Hex helps accelerate insights, auto-identify the correct datasets and joins, and analyze code for efficiency gains swiftly. The agent feature is incredibly powerful; it often gets me 80%+ of the way to completing a task very fast, allowing me to focus on manual edits or optimizations effectively. I have been using this agent since its release for a range of tasks, especially in writing Python code, and it continues to improve frequently. Moreover, I found the onboarding process very easy, which facilitated a smooth adoption of Hex in my workflow. Overall, I am highly satisfied with Hex and would wholeheartedly recommend it to others, as evidenced by my willingness to rate it a perfect 10 on the recommendation scale. Review collected by and hosted on G2.com.

I find the agent feature sometimes struggles to switch effectively between query and dataframe mode. This can be problematic, especially when specific tasks require distinct modes, such as using dataframe mode for particular operations or query mode for handling larger datasets efficiently. Additionally, the size of notebooks tends to grow rapidly, which is often due to the creation of multiple cells instead of optimizing or editing existing ones. This rapid expansion with 'dormant' disconnected cells makes it challenging for me to grasp the full scope of a notebook, leading to difficulties in understanding and additional time required to comprehend what the notebook is doing. Review collected by and hosted on G2.com.

I like the ability to chat with an agent and get thoughtful analysis created. It's just like any other bot experience, but this one creates SQL and visuals for me. Review collected by and hosted on G2.com.

It's not always getting things exactly right the first time, which leads to an iterative approach needed to get what you want. Review collected by and hosted on G2.com.

I like Hex's nice UX and shortcuts, which makes it easy to navigate. The cache for slow queries is also really helpful, allowing me to iterate with cached results. Review collected by and hosted on G2.com.

CTE is very confusing, and if a cell is running on an external cluster, not using dataframes, the CTE's result is not reused, causing it to rerun. Also, dataframes only use DuckDB, which is not MySQL compatible. I don't use apps, so I just want a notebook, not an app, and projects permissioning is confusing. Review collected by and hosted on G2.com.

I really appreciate Hex for data analysis and table explorations because it offers quick access to data, which is essential for my workflow. Its speed is impressive, allowing me to perform tasks efficiently. I also find the app builder to be nice, making the creation and sharing of visualizations smooth and easy. Plus, Hex helps with fixing queries using AI and building Python code, which enhances my productivity. Review collected by and hosted on G2.com.

The notebook organization isn't flexible enough for me. I'd like to be able to create columns while moving code blocks. Also, getting detailed information about dataframe columns, like value distribution percentages, would be helpful. Review collected by and hosted on G2.com.

I love using Hex for ad hoc analytics due to its native SQL connection, which simplifies data retrieval. The ability to seamlessly integrate Python within the platform is highly valued as it allows me to build and share tools with my colleagues efficiently. Hex's ease-of-use, particularly with data in AWS, significantly enhances my workflow by enabling rapid task completion without the need for exporting or additional file management in Python or SQL. The Python + AI assistant feature is fantastic; sometimes the AI anticipates my needs and improves my typing through forward-filling, making coding a smoother experience. I also appreciate the ability to use the embedded version of Hex within our internal analytics platform, consolidating notebooks in one location, which I find extremely helpful. The initial setup of Hex was very easy, and based on my positive experience, I would wholeheartedly recommend Hex to others. Review collected by and hosted on G2.com.

I dislike the CTRL+F functionality inside a cell in Hex. It does not allow me to search for instances within a large cell effectively. Instead, it requires me to use CTRL+F outside the cell, which then searches the entire notebook for the term, leading to inefficiencies in the workflow. Moreover, when using CTRL+F inside a cell, pressing ENTER does not take me to the next instance of the searched term automatically, which is an expected behavior that would greatly enhance usability. Additionally, limitations in the amount of datapoints that can be displayed with the native visuals is a bit restricting. I constantly work with data that has lots of rows and lots of points. If there would be some default behavior that would somehow display an aggregate, a rolling median, or something and warn me about it rather than just say "too many data points" it would be useful. I'm typically more interested in the trends. Review collected by and hosted on G2.com.

I really enjoy using Hex for our internal BI style reporting and its interactive notebooks, which make exploratory data analysis and prototyping seamless. The ability to connect to a variety of data sources such as MySQL, PostgreSQL, and Snowflake and query them effectively while blending SQL with Python is exceptionally powerful. As a data person, I find it straightforward to build and maintain our projects in Hex, freeing us from the limitations of traditional BI platforms. The inclusion of the new AI agents in Hex, particularly for answering ad-hoc questions and building notebooks, has greatly exceeded my expectations. I appreciate how the AI agent enhances its capabilities by reading our Snowflake data warehouse, DBT metadata, and the Hex projects we've developed. This creates a flywheel effect where the more we use Hex, the more valuable the tool becomes. It also provides me insights into the questions being posed by others in our company, helping to identify and address any blind spots in our self-service capabilities. Review collected by and hosted on G2.com.

I wish their native dbt metricflow ingestion supported slowly changing dimensions, and I wish the pricing structure was a bit cheaper for explore seats. Review collected by and hosted on G2.com.

I find Hex incredibly effective for both performance reporting and exploratory analysis. The platform makes it really easy to dig into product funnels, understand trends over time, and craft reports that are fast and user-friendly, with the added convenience of scheduling them via email. I appreciate how quickly Hex operates once it loads, especially when handling complex tasks. The integration of SQL and Python is a major asset for me as a product manager at a fintech company. Being more familiar with SQL, I usually start there to build datasets for my charts, and then utilize Python for more complex data manipulation and visualization tasks. This seamless mix and match capability greatly enhances my workflow. Additionally, the learning curve for Hex was quite easy for me, largely facilitated by the AI assistance from both GPT/Claude and the in-app AI, though we've disabled it since. Overall, the platform stands out in terms of speed and flexibility compared to other tools I've used. Review collected by and hosted on G2.com.

I found the controls over the charts to be lacking, specifically the difficulty in isolating a specific trendline by interacting with the legend. Additionally, the editor tends to load slowly, especially when working with large dashboards, which impacts efficiency. There's a need for improved performance, perhaps through pagination or chunking to handle large data loads more effectively. Review collected by and hosted on G2.com.

I greatly enjoy using Hex because it offers a flexible environment where I can choose between SQL and Python for my data tasks, providing me the versatility needed for a wide range of analyses. The standout feature for me is the ability to chain cells in multiple languages, allowing me to seamlessly switch between SQL for querying databases and Python for transformations or statistical analysis, all within the same notebook. This fluid transition simplifies my workflow and enhances productivity, as I can tailor my approach based on the specific needs of each task. Additionally, I appreciate how straightforward the initial setup is, particularly in sharing data connections like Snowflake or Redshift, which facilitates a smooth start with the tool. Review collected by and hosted on G2.com.

I think the components in Hex are really problematic. My team initially believed the feature would simplify using the same metrics across notebooks, but due to poor design and understanding, it only results in significant bloat. The charting capabilities also leave much to be desired; the customization options are inadequate. Additionally, Hex's default dark mode makes it challenging to design good visualizations, as generally accepted best practices assert that charts are best made with a light background. Review collected by and hosted on G2.com.

I use Hex for internal performance reporting, deep dive analysis, and as an extract tool. One of the things I love about Hex is how it speeds up my analysis time by handling modeling and visualizations in the same place, significantly saving me time. The Component features are extremely valuable, allowing me and my colleagues to reuse existing code, which enhances productivity and streamlines my workflow. I also love the ability to quickly build visualizations directly from SQL or Python code, which integrates seamlessly into my existing analysis processes. The 'resolve with magic' feature is another standout as it saves time by correcting simple errors efficiently, all while maintaining a clean and intuitive interface. Since everything is within the same space, I can focus on analysis and reporting without having to switch between multiple tools. Additionally, the ability to quickly share results with stakeholders in a clean format is incredibly beneficial. I find working with Hex enhances my productivity and simplifies my tasks, making my job easier. Due to these features, I would recommend Hex to others wholeheartedly, and I rate it a 10 out of 10 for its ease in making my job simpler. Review collected by and hosted on G2.com.

The filter experience in Hex is complex and lacks intuitiveness compared to my experiences with Looker. It is not straightforward to apply filters to specific datasets or fields, which complicates the workflow. Review collected by and hosted on G2.com.