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
A new study by the IBM Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation. The AI Projects to Profits study revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation ...
Respondents predict that agentic AI will play an increasingly prominent role in their interactions with technology vendors over the coming years and are positive about the benefits it will bring, according to The Race to an Agentic Future: How Agentic AI Will Transform Customer Experience, a report from Cisco ...
A new wave of tariffs, some exceeding 100%, is sending shockwaves across the technology industry. Enterprises are grappling with sudden, dramatic cost increases that threaten to disrupt carefully planned budgets, sourcing strategies, and deployment plans. For CIOs and CTOs, this isn't just an economic setback; it's a wake-up call. The era of predictable cloud pricing and stable global supply chains is over ...
As artificial intelligence (AI) adoption gains momentum, network readiness is emerging as a critical success factor. AI workloads generate unpredictable bursts of traffic, demanding high-speed connectivity that is low latency and lossless. AI adoption will require upgrades and optimizations in data center networks and wide-area networks (WANs). This is prompting enterprise IT teams to rethink, re-architect, and upgrade their data center and WANs to support AI-driven operations ...
Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...
Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...
Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...
Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...
As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...
Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...