Kensu

Kensu

Software Development

San Francisco Bay Area, California 2,193 followers

Observe data where it counts

About us

Kensu’s Data Observability solution allows organizations to monitor their data in real-time and cut resolution time in half. Our disruptive approach goes beyond simply scanning data files and collecting application logs: it monitors data at the source in real-time, where and when the applications are using it. Please don't hesitate to contact our team if you have questions about data reliability and observability.

Industry
Software Development
Company size
11-50 employees
Headquarters
San Francisco Bay Area, California
Type
Privately Held
Founded
2018
Specialties
Data Science, Data Quality, Lineage, Data Intelligence Management, Platform, Data Observability, and Data Lineage

Locations

  • Primary

    353 Kearny Street

    San Francisco Bay Area, California 94108, US

    Get directions

Employees at Kensu

Updates

  • View organization page for Kensu, graphic

    2,193 followers

    Some backstory behind me... 👇

    View profile for Andy Petrella, graphic

    Founder @ Kensu, Author @ O'Reilly, Public Voice @ World

    "You don't have a Data Quality problem." As an evangelist and pioneer in the data observability category, I often surprise people when I say that "there is no data quality problem", and thus data observability is not a solution for this. I don't mean there is no data quality issues, I mean this is not the problem. In fact, all data have data quality issues. A.L.L. O.F. T.H.E.M. However they are not statistically significant relatively to: - The amount of data. - The amount of data sources. - The amount of usages. Therefore, if you are hunting data quality issues using so called "advanced techniques", you'll find them, oh yeah, a LOAD of them.  And, this will scare you to hell, so you will likely be tempted to hide it. Well, because, at the end of the day, data has always been used and this discovery doesn't align with the experience on the field. Right? Here is a positive message to the whole data community. Look at this from the "MOST" lens:  - Most of the time - Most of the data is good enough for  - Most of the usages If you're okay with this, then what is "the problem"? The problem is like Einstein (never!!) said: 9x10=91. It takes only one error to create distrust. And, of course, the solution cannot be to chase all errors, or, say, avoid and clean all (=100%) bad data. The solution is to *be* trustable, to make errors acceptable, because resolving is a well-understood and smooth process. That's part of the job, yup. Yes, they'll be complains, nothing can be perfect, we're all grown-up people we can deal with that (like we've all "accepted" and dealt with software bugs...) FWIW, here are some of the reasoning that lead me to bring up what is called today "data observability" and why I say "there is no data quality problem". More on this in my book: Fundamentals of Data Observability at O'Reilly https://lnkd.in/e7FT99Wh #dataquality #dataobservability #problemsolving

    Fundamentals of Data Observability: Implement Trustworthy End-to-End Data Solutions

    Fundamentals of Data Observability: Implement Trustworthy End-to-End Data Solutions

    amazon.com

  • View organization page for Kensu, graphic

    2,193 followers

    We’re thrilled to see Michel Lutz insights on “Fundamentals of Data Observability” by our CEO, Andy Petrella. 📚 It’s heartening to know that the core messages resonate with industry leaders and help build the path to modern data management and observability. At Kensu, we’re committed to transforming how business and data teams understand and leverage their data. If Michel’s review has sparked your interest, we invite you to dive deeper into the world of data observability: ° Download the book on our website: https://lnkd.in/gZdv7iD3 ° Order your hard copy on Amazon https://lnkd.in/eNT3Dsvk ° Book a demo to explore how Kensu’s capabilities can empower your data strategy: https://lnkd.in/ggG8kuxJ.

    View profile for Michel Lutz, graphic

    TotalEnergies Chief Data Officer and Digital Factory Head of Data & AI

    👊 Data quality punchline: “Garbage in, garbage out is an excuse, not an explanation!" Quote from the book “Fundamentals of Data Observability” by Andy Petrella. ✅ I recommend this book, which gives a deep insight into the more and more popular concept of data observability. Beyond data observability, taking a step back, I really enjoyed this book which gives a view of what modern data management should be: not only a matter of processes and governance (although these dimensions remain essential), but also a matter of cutting-edge technology and automation. Well done Andy! 🙌 #datamanagement #dataquality #dataobservability

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  • View organization page for Kensu, graphic

    2,193 followers

    What a fantastic way to start this New Year 🎉 After two years of hard work, Sammy El Khammal (Product Manager at Kensu) and Michele Pinto's (former Head of Engineering at Kensu) book "Data Observability for Data Engineering" is out! If you are a Data Engineer, Architect, Analyst, Scientist, or Product Owner who faces the challenges of data pipelines, or a Head of Data, Platform Manager, or Engineering Manager who oversees data reliability, this is a must-have! You can now order your book on Amazon 👉 https://lnkd.in/emnxK53a

    View profile for Michele Pinto, graphic

    Chief Technology Officer at Sustainable Brand Platform

    Hello extended friends After 2 years since the start of this project, I would like to announce that our book Data Observability for Data Engineering will finally be published on December 29th. I am honoured to have had the opportunity to work on this book with my co-author Sammy El Khammal, a true talent and pioneer in the industry. Without him (and his infinite patience with my chronic delays), it would have been impossible to accomplish this goal. Our book is aimed at Data Engineers, Architects, Analysts, Scientists, and Product Owners who are faced with the challenges of data pipelines on a daily basis. It is also aimed at executives such as Heads of Data, Platform Managers and Engineering Managers who are accountable for data quality and optimization. Executives who oversee data quality and processes can gain valuable insights to increase customer trust and engagement in pipelines. Whether you handle data or lead data-oriented teams, this book provides knowledge to improve data observability in organizations. My thanks to Andy Petrella for triggering this initiative and especially for his immeasurable source of knowledge and inspiration. My thanks also go to Manikandan Kurup, Joseph Sunil, Kirti Pisat, Nivedita Singh, Heramb Bhavsar and Mouli Banerjee from Packt for their help and support throughout this fun journey and to Sunil Mandowara and Andy Rabone for offering to review the book. Enjoy it ;) #dataengineering #dataobservability #dataquality #datapipeline #datateam #data #packt #dataanalyst #datascientist

    Data Observability for Data Engineering: Ensure and monitor data accuracy, prevent and resolve broken data pipelines with actionable steps

    Data Observability for Data Engineering: Ensure and monitor data accuracy, prevent and resolve broken data pipelines with actionable steps

    amazon.com

  • View organization page for Kensu, graphic

    2,193 followers

    As we wrap up 2023, it’s time to pause and thank our team for their incredible dedication and work 🙏 To our team, clients, partners, and the broader Kensu community, we extend our warmest wishes for a holiday season full of joy, laughter, and all that makes this time of the year special! Happy Holidays from our team to yours! 🎄✨

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  • Kensu reposted this

    View profile for Sunil Mandowara, graphic

    Data Platform Architect|Engineering Lead|Lakehouse Evangelist|Microservices|APIs| |BigData|Thought Leader | Data Security|Data Observability|AIML|GenAI|GDPR

    The great achievement of 2023 Data Observability is setup #5pmDataClub DataPost68   You would not be able to avoid data observability in 2024.   We have already set up Kensu to monitor all five Dimensions of data observability and Data Lineage. #dataobservability #datalineage #datagovernance #distributedsystems #SunilMandowara

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  • View organization page for Kensu, graphic

    2,193 followers

    To illustrate the power of the Kensu x Azure Data Factory integration: Let’s say an application processes data before sending it to several pipelines. If the initial application encounters an incident, the flawed data will cascade across all the downstream pipelines, raising the risk for a single incident to spread and adversely impact end users. Provided that only 7% could identify data issues before they impact users (Source: The State of Data Observability Research Report), such a risk exists for most data teams. To eliminate potential for data problems, data teams can leverage the Rules in Kensu in conjunction with the Circuit Breaker. With the Kensu Circuit Breaker, the above initial job’s execution would be halted if data reliability standards are not met. This protects against further producing inaccurate or incomplete data and grants data teams the opportunity to resolve the problem by leveraging the insights provided by the Kensu platform. Learn more with the blog post: https://lnkd.in/gHnczas5

    Prevent data issues from cascading and deliver reliable insights with Kensu + Azure Data Factory

    Prevent data issues from cascading and deliver reliable insights with Kensu + Azure Data Factory

    kensu.io

  • View organization page for Kensu, graphic

    2,193 followers

    Our research on the State of Data Observability 2023 uncovered that data management leaders perceive the top benefits of implementing data observability to be: - Faster troubleshooting - Availability of data lineage - Populating a data catalog - Visibility in data pipelines - Prevention of data issues Learn more about the data observability market, including key success factors for implementation through the State of Data Observability research. Access a complimentary copy here: https://lnkd.in/dB8ymzid

    Kensu | State of Data Observability

    Kensu | State of Data Observability

    kensu.io

  • View organization page for Kensu, graphic

    2,193 followers

    Kensu: Powered by Snowflake ❄️ In the interview with Snowflake’s Daniel Myers, Kensu founder and Chief Product Officer Andy Petrella dives into core features Snowflake users of the platform can expect to enact. For instance, the Kensu data observability platform aggregates and visually links information from dbt, Snowpark, Snowplow, and more using the Kensu Explorer. Users can seamlessly browse through the lineage, allowing for easier discovery of data incident sources. You can access their full conversation here: https://lnkd.in/g8euXfPQ

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  • View organization page for Kensu, graphic

    2,193 followers

    A popular Kensu feature amongst data teams is the Circuit Breaker, which stops data pipelines when a data incident has been detected. The Kensu Circuit Breaker is a component that data teams can seamlessly import into an Azure Data Factory environment and incorporate into any desired pipeline. Once in place, the Kensu interface allows activating or deactivating the rules that govern the circuit breaker's operation in a few clicks. Learn more about the Kensu + Azure Data Factory integration with the “How Kensu empowers Microsoft users with proactive Data Observability” webinar. This webinar is taking place this week! Don’t miss this, register below: 🇺🇸 US: https://lnkd.in/gE6JDhfi 🇬🇧 UK: https://lnkd.in/gHQxSgVR

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  • View organization page for Kensu, graphic

    2,193 followers

    In one week, join host Emanuele for the final Kensu webinar of the year. In “How Kensu empowers Microsoft users with proactive Data Observability,“ he’ll cover how Microsoft users are taking advantage of the easy-to-implement Kensu platform. Through this integration, data teams can fetch data observations for all their Azure Data Factory pipelines and prevent data incidents, eliminating frustrations that arise when faced with broken data. As one of the few Data Observability providers available to support customers on-premise, multi-cloud, or hybrid environments, Kensu is broadening access to Data Observability for Microsoft users. Register: 🇺🇸 US: https://lnkd.in/gE6JDhfi 🇬🇧 UK: https://lnkd.in/gHQxSgVR

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Funding

Kensu 4 total rounds

Last Round

Seed

US$ 4.2M

See more info on crunchbase