![]() Some of these may not be problems when you first start out on your data journey, but as you begin to scale your data platform, the system will be less forgiving and problems will begin to increase exponentially. ![]() While there's no single strategy that will lead to success, there are certain things to consider before developing your strategy. (See what time-series forecasting can do.) Challenges with data managementĪs data volume and velocity grow, it’s important to adopt a data management strategy to help manage costs and appropriately scale the environment. The oldest time-series data that falls outside of non-searchable retention periods will be destroyed and lost forever. Older time-series data that falls outside of searchable retention periods will be archived to slower, cheaper storage for possible use later. ![]() Data will be correlated and transformed so that it can be used cohesively to identify insights and help solve problems efficiently. An agent may be sitting on that remote server, watching that path and collecting the data that will be indexed by a logging platform. This creates a new time-series event within a log file. Actions are executed on remote servers which log the activity of that action. Below is a diagram showing the different phases, along with a short description of each. As data ages, it will traverse each phase until it is ultimately destroyed. New data will fall into the first phase as it’s created but before it’s indexed. The data lifecycle is a continuous process made up of multiple phases that represent how data is created, fed into the system and pushed out of the system.
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