Monte Carlo announced the launch of the Monte Carlo Data Observability Platform, an end-to-end solution to prevent broken data pipelines.
Monte Carlo’s solution delivers the power of data observability, giving data engineering and analytics teams the ability to solve the costly problem of data downtime.
The Data Observability platform is an end-to-end solution for your data stack that monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact, and notify those who need to know. By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster.
“The fastest thing that can destroy an executive’s trust in data is for it to be wrong -- we make sure that doesn’t happen,” said Barr Moses, CEO and co-founder of Monte Carlo. “Over the last few years, businesses have moved from hoarding data to putting it to work for them. In my conversations with hundreds of data professionals I was struck by the fact that organizations were investing millions of dollars and strategic energy in data, but the people at the front lines couldn’t use it or didn’t trust it. With Monte Carlo’s Data Observability Platform, data teams can unlock the potential of their data and finally trust it to deliver value for their companies.”
The Monte Carlo Data Observability platform delivers:
- End-to-end observability into all of your data assets. Monte Carlo connects to your existing data stack, providing visibility into the health of your cloud warehouses, lakes, ETL, and business intelligence tools.
- ML-powered incident monitoring and resolution. Monte Carlo automatically learns about data environments using historical patterns and intelligently monitors for abnormal behavior, triggering alerts when pipelines break or anomalies emerge. No configuration or threshold setting required.
- Security-first architecture that scales with your stack. Designed by security industry veterans, the platform intelligently maps your company’s data assets while at-rest without requiring the extraction of data from your environment and scalability to any data size. Monte Carlo never stores or processes your data - full stop.
- Automated data catalog and metadata management. Real-time lineage and centralized data cataloguing provide a single pane-of-glass view that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements.
- No-code onboarding. Code-free implementation for out-of-the-box coverage with your existing data stack and seamless collaboration with your teammates.
The Monte Carlo Data Observability Platform is currently available for qualified organizations.
The Latest
In the heat of the holiday online shopping rush, retailers face persistent challenges such as increased web traffic or cyber threats that can lead to high-impact outages. With profit margins under high pressure, retailers are prioritizing strategic investments to help drive business value while improving the customer experience ...
In a fast-paced industry where customer service is a priority, the opportunity to use AI to personalize products and services, revolutionize delivery channels, and effectively manage peaks in demand such as Black Friday and Cyber Monday are vast. By leveraging AI to streamline demand forecasting, optimize inventory, personalize customer interactions, and adjust pricing, retailers can have a better handle on these stress points, and deliver a seamless digital experience ...
Broad proliferation of cloud infrastructure combined with continued support for remote workers is driving increased complexity and visibility challenges for network operations teams, according to new research conducted by Dimensional Research and sponsored by Broadcom ...
New research from ServiceNow and ThoughtLab reveals that less than 30% of banks feel their transformation efforts are meeting evolving customer digital needs. Additionally, 52% say they must revamp their strategy to counter competition from outside the sector. Adapting to these challenges isn't just about staying competitive — it's about staying in business ...
Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey ...
SLOs have long been a staple for DevOps teams to monitor the health of their applications and infrastructure ... Now, as digital trends have shifted, more and more teams are looking to adapt this model for the mobile environment. This, however, is not without its challenges ...
Modernizing IT infrastructure has become essential for organizations striving to remain competitive. This modernization extends beyond merely upgrading hardware or software; it involves strategically leveraging new technologies like AI and cloud computing to enhance operational efficiency, increase data accessibility, and improve the end-user experience ...
AI sure grew fast in popularity, but are AI apps any good? ... If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment ...
If you were lucky, you found out about the massive CrowdStrike/Microsoft outage last July by reading about it over coffee. Those less fortunate were awoken hours earlier by frantic calls from work ... Whether you were directly affected or not, there's an important lesson: all organizations should be conducting in-depth reviews of testing and change management ...
In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...