Not all data ends up in the database or the data warehouse, some of it ends up getting dumped in the data lake, a low storage cost uncharted place typically populated with unstructured data. Similarly, a data warehouse is a central repository of integrated data from more than one source (usually transactional systems, different databases and these days also from machine logs) where we can use data mining tools to perform analysis and reporting functions. A real-world warehouse is a place of designated functional work by operatives where many items are brought together from different suppliers and places. What is a data lakehouse?įirst, there’s data, then there are databases and then there are data warehouses - the latter being a facilitating technology for Business Intelligence (BI). Grail is promised to revolutionize data analytics and data management by unifying observability and security data from cloud-native and multi-cloud environments, retaining its context and delivering instant, precise and cost-efficient AI-powered answers and automation. The company has now come forward with a scalable data lakehouse solution known as Grail, which represents a new core technology of the Dynatrace Software Intelligence Platform. Software intelligence company Dynatrace thinks it can provide some (if not many, or perhaps all) of the answers here. The answer might be to move out of the weeds and into the lakehouse. So much so that it can look at itself and see how the operations layers it builds are performing - even if that means knowing there are murky waters beneath. If this all sounds like an insurmountable challenge, then, it is, but the reason we’re able to detail this data meltdown scenario is that the IT industry is usually pretty good at being introspective. If you automate processes on bad data, you can’t expect things to keep running smoothly. That means any answers organizations can hope to get from data analytics are often incomplete, imprecise, or even – dare we say it, downright wrong – limiting the value. That means they only get pieces of the puzzle to base their decisions on, rather than a complete picture.Īs well as having an incomplete dataset, most of the data organisations do keep is stored and analyzed in silos – using a host of different monitoring and analytics tools (one for a bit of infrastructure here, one for a bit of infrastructure there etc.), so lacks a crucial ingredient that ties it all together – context. Nobody has the time or inclination to do that (‘lengthy and expensive retrieval processes’ doesn’t exactly scream ‘real-time actionable insights’), so for most organizations, that means they just make do with whatever they have in their observability and log analytics tools at any one time.
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