![]() ![]() ETL processes can be simple, or when used to combine, or fuse, multiple data sets together, can become more complex. One of the most fundamental processes that occurs to data before it is ultimately ingested into a data lake is the Extract-Transform-Load (“ETL”) process. The optimal step in the data lifecycle for data observability monitoring is not always obvious. We focus first on monitoring the data from the point it originates, through its flow to the data lake via data pipelines.Ī Good Place to Start – Monitor Before “E” In this article, we describe how Zectonal’s software can benchmark the quality of data supply chains, and how you can maintain a more pristine, higher quality data lake as a result. Raw Materials -> Fleet Transportation -> Factories -> Manufacturing Equipment -> Finished Goods We defined the 5 data supply chain components as:ĭata -> Data Pipelines -> Data Lake -> Data Analytic Software -> AI-driven InsightsĪnd compared them to the 5 Industrial Age components: In a previous blog Don’t Underestimate the Importance of Characterizing Your Data Supply Chain, we defined the 5 components the data supply chain while also making a comparison to an Industrial Age physical supply chain. Zectonal offers 4 ways to prevent your Data Lake from becoming polluted resulting in more impactful AI driven outcomes. ![]() ![]() Monitoring for established data quality standards and protecting from emerging AI Security threats are critical for ensuring the optimal use of your Data Lake. Polluted Data Lakes and Data Warehouses are becoming an increasingly significant problem resulting in AI Bias and Data & AI Poisoning security vulnerabilities.Ĭounterfeit data, in both non-deceptive and deceptive forms is entering the global data supply chain at an alarming rate. ![]()
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