Industry: Software Development
JetBrains products used: Datalore
Organization Size: 100-250
Country: Israel
The Hunters SOC platform empowers security teams to automatically identify and respond to incidents that matter across their entire attack surface, at a predictable cost. Through built-in detection engineering, data correlation, and automatic investigation, Hunters help teams overcome volume, complexity, and false positives. Hunters also mitigates real threats faster and more reliably than SIEMs, ultimately reducing customers’ overall security risk.
Hi, I’m Netanel Golani, a Threat Hunting Expert at Hunters.
I’m a member of team Axon – technology professionals whose mission is to deliver cybersecurity expertise, battle tested initiatives, and actionable insights to customers.
The team also delivers rapid responses to emerging threats, proactive threat-hunting and on-demand investigations.
The Hunters SOC platform empowers security teams to automatically identify and respond to incidents that matter across their entire attack surface, at a predictable cost. Through built-in detection engineering, data correlation, and automatic investigation, Hunters help teams overcome volume, complexity, and false positives. Hunters also mitigates real threats faster and more reliably than SIEMs, ultimately reducing customers’ overall security risk.
We mostly use Python and SQL for daily research. We found Jupyter Notebook to be a preferable framework for our investigations and data analysis methodology because of its flexibility and ease of use.
We were looking for a comprehensive tool that everyone in our data science, analytics, and engineering teams would be happy to use and integrate with our internal Python based tools.
It has only been a month since the data science team at Hunters started using Datalore, and we have already seen productivity and usability improvements in our daily workflow – especially when working with numerous customer data sources.
The most important feature for us at Hunters was the ability to connect multiple database types and get smart coding assistance for both SQL and Python in the same notebook. The seamless transition from SQL query results to a Pandas dataframe helped the team speed up internal research and perform more sophisticated investigations. Part of the analytics team at Hunters uses DataGrip, the SQL IDE by JetBrains, so they were very happy to see some of the DataGrip features they like in Datalore as well.
There could be several teams involved in one project, and we were excited that sharing code in notebooks was as easy as sending a link.
“It really fostered teams to collaborate more and this way we started delivering research results faster.”
— Netanel Golani, a Threat Hunting Expert at Hunters
Automatic statistics and visualizations for data frames also worked well for us. We do a lot of pivot tables, so the Datalore team planned an improvement to the Visualize tab to help us accomplish this kind of task straight out of the box as well.
While data science and data analytics teams at Hunters are using and enjoying Datalore on a daily basis, Hunters also has a data engineering team that is responsible for putting data science work into production. This team is also interested in adopting Datalore. Data engineers at Hunters use Apache Flink, a big data framework for streaming data. Native support for Apache Flink is planned for future Datalore releases, while in the meantime teams can access Flink through dedicated APIs.
Moreno Raimondo Vendra, Senior Machine Learning engineer, TrueLayer
Datalore enabled our team to ergonomically access our data while meeting the security requirements, which was a game changer for us. As a result, we could collaborate much more easily both within our Machine Learning team and with our stakeholders.
Chad Rosenberg, Head of Technology, The Center for New Data
Datalore just gives us ways to work on our data that we won’t get in Airflow, like debugging the pipeline results, trying the webhooks, and quickly visualizing the data with automatic plotting features. Being able to use the native Snowflake connector in Datalore, as well as the programmatic ones in pandas, has definitely been a time saver when working on shared notebooks.
Surya Rastogi, Senior Staff Data Scientist, Chainalysis
One of our biggest challenges is that the blockchain space is rapidly expanding and there is always new data to be acquired and analyzed. As a company we have a lot of data acquisition and processing functions, and we expect them to keep growing.