Enterprise AI Search, Intranet, and Wiki in one platform. Guru lives in tools organizations already use, so no need to context switch. Users can find info across any app, have an expert help if the info can't be found, and let Guru proactively identify knowledge gaps, duplicate knowledge, and translate it to 100 languages.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
In my experience, Guru is well suited. Actually, most of the time, Guru is tailored to every issue that I have handled. From the time that you search for a specific Guru name to the summary and the steps provided, you can easily understand it, so you can easily fit the process to the issue that you are currently handling.
It's not clear how to relate/ attach other guru cards. The feature exists but is not very easy to spot.
We often find it difficult to search because we don't remember the card's title. I would love it if the tag words worked better.
Once editing a card, instead of adding someone to collaborate on a draft, it would be good to send the draft to someone via email. I find people often ignore these emails/pings if it's just a draft collaboration. The wording could be to ask someone to verify the draft.
I rated Guru a 9 out of 10 for renewal likelihood because it has significantly enhanced our processes and customer satisfaction in the automotive rental company. Guru’s user-friendly interface and organized knowledge base make it easy to access and update essential information, which is crucial for maintaining high operational standards. The tool’s integration with other platforms, like the Guru Chrome extension, streamlines our workflow, increasing efficiency and productivity. One of the standout features is the ability to favorite cards, allowing quick access to frequently used information, which saves time and ensures accuracy in customer interactions. Additionally, Guru’s analytics feature helps us identify which resources need updating, ensuring our knowledge base remains relevant and effective. The only reason it’s not a perfect 10 is occasional slow performance and occasional inaccuracies in search results. However, these minor issues do not overshadow the overall benefits Guru provides. Its consistent updates and reminders further enhance our ability to deliver excellent customer support, making it an indispensable tool for our team.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
Guru is easy to use once you can acclimate with it, and get a feel for the lay of the land. It does have a somewhat steep learning curve for new users, or agents that have not gotten previous experience with similar knowledge base solutions. It is easy to navigate and find cards, easy to tag and update tags, and it is a breeze to update or create entire cards. The text based formatting helps keep guiderails for syntax and helps maintain a professional, but not too wordy tone.
I have never encountered an outage with Guru in the year+ we've had it. I also never encountered an outage at the previous company where I used Guru. It has been extremely reliable.
There is some occasional slowness, or multiple refreshes before content will load. This doesn't happen frequently, though, and I don't get complaints from teammates about it.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
The only reason I do not give it a a ten is because I think there is still some room for improvement in meeting the different time zone needs of their customers, but overall their support is top notch. Friendly, capable, and quick.
I would rate the in-person training for Guru a solid 9 out of 10. The session was incredibly valuable as it provided comprehensive insights into using Guru effectively. I learned a lot about the tool's functionalities, which significantly enhanced my proficiency and confidence in utilizing Guru for my daily tasks. The training was engaging, informative, and tailored well to ensure I could apply what I learned immediately. Overall, it was a highly productive and beneficial learning experience.
I would rate the online training for Guru a strong 8 out of 10. While it lacked the interactive nature of in-person sessions, the content was well-structured and delivered effectively. The training modules were clear and comprehensive, covering all essential aspects of using Guru. The flexibility of online training allowed me to learn at my own pace and revisit topics as needed. Overall, it provided a solid foundation and practical understanding of Guru's functionalities, making it a valuable learning experience despite the virtual format.
You will need a very strong team of guru champions in order to get EVERYONE and EVERYTHING on Guru, it takes some craziness and over talking about guru everywhere to get people to be exicted, contribute and use. If you are starting any kind of buisness and you need KB, just go for guru as fast as you can because when you will grow you will thank yourself.
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
Guru is a vastly superior product for centralizing company knowledge, and in many ways, we feel that there is "more gas in the tank" with the tool (AI search, etc) and ways that we have yet to unlock all of its potential fully. This is primarily a resourcing issue on our side, not a fault of the Guru tool or team.
Guru is somewhat expensive compared to what our org is typically willing to spend on software solutions. Additionally, there is a price difference per seat when paid monthly versus annually, which was a big downside for us as my company didn't want to commit to an annual cost.
We have a call center team in Honduras - Guru reduces the back and forth that happens when using an overseas and remote BPO
Helps create a very solid and concrete basis for QA - if our processes are clearly documented, we can say with confidence that our scoring reflects the reality of our support.
Helps give insight into where we are failing/falling short in training. The more a card is used, the more we can say it's likely that process is difficult to recall, not trained as well, or a pain for customers and the team. All of this insight can help drive change on the team and in the business.
