Data monitoring

Latest Articles

Observability vs monitoring for data pipelines

Data teams don’t have it easy. The Modern Data Stack is evolving at pace, but with each addition comes spiraling complexity and responsibility. Staying on top of the number of failure points as well as maintaining organizational trust in data is the Mount Everest of jobs. Trudging on, like the badly equipped sherpas of the […]

How to protect your data pipeline against the next cloud outage

Every year, businesses grow increasingly dependent on cloud infrastructure to power their data stack. This year, cloud services are already set to process an incredible 94% of workloads. This massive growth is not surprising. Cloud computing offers many advantages such as greater flexibility, increased speed and agility, lower latency, cost optimization and more. Yet although […]

Monitor and manage data quality in real time

Many customers and community members come to Snowplow specifically because they want accurate and complete data collection. One of our core features is being a loss-less data pipeline, which means all data can be accounted for, even if it doesn’t make it all the way through to a data storage environment. More specifically, events coming […]

What is data latency and how to measure it

Data latency has turned into a key metric for data teams. It is becoming increasingly important as companies aim to execute on use cases that require real-time or near real-time data access. However, measuring and reporting on latency can be more challenging than most businesses anticipate, yet critical for data teams that need to deliver […]

Monitoring Bad Rows on GCP Using BigQuery and Data Studio

One of the key features to Snowplow pipeline is that it’s architected to ensure data quality up front – rather than spending a lot of time cleaning and making sense of the data before using it, schemas are defined up front and used to validate all data types as they come through the pipeline. Another […]

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