It happens: dashboards and reporting fall victim to data pipeline breaks. Data teams quickly need to diagnose what’s wrong, fix where things may be broken, and provide up-to-date numbers to their end business users. But when these breaks happen (and they surely do) how can teams quickly identify the root cause of the problem?
For me, it’s every time I am about to turn onto a one lane road that is clear for miles and make great time, a slow Honda CRV goes by and I am stuck behind them instead. Every time.
I work as an art handler in a major city. Their collection consisted of antiquities, things that would usually be reserved for museums. Think 2nd century BCE vases, figurines, etc.
On the desk of the client, there was a small Athenian chalice painted in the iconic orange-figure-black-background style, developed around 500 BCE.
Inside of it was paper clips, spare change, and chewed gum.
If data teams have some form of data lineage in place, they can more easily identify the root cause of the broken pipeline or data quality issue. By backing out into the data models, sources, and pipelines powering a dashboard a report, data teams can understand all the upstream elements impacting that work and see where the issues lie.
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It happens: dashboards and reporting fall victim to data pipeline breaks. Data teams quickly need to diagnose what’s wrong, fix where things may be broken, and provide up-to-date numbers to their end business users. But when these breaks happen (and they surely do) how can teams quickly identify the root cause of the problem?
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For me, it’s every time I am about to turn onto a one lane road that is clear for miles and make great time, a slow Honda CRV goes by and I am stuck behind them instead. Every time.
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I work as an art handler in a major city. Their collection consisted of antiquities, things that would usually be reserved for museums. Think 2nd century BCE vases, figurines, etc.
On the desk of the client, there was a small Athenian chalice painted in the iconic orange-figure-black-background style, developed around 500 BCE.
Inside of it was paper clips, spare change, and chewed gum.
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how 'bout now?
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I don't care
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hello?
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Is this really anonymous?
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If data teams have some form of data lineage in place, they can more easily identify the root cause of the broken pipeline or data quality issue. By backing out into the data models, sources, and pipelines powering a dashboard a report, data teams can understand all the upstream elements impacting that work and see where the issues lie.
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This is a weird but cool, don't you think?
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