The exact types of information included in a data catalog can vary, but they typically include items such as: Its purpose is to make it easy for stakeholders within and outside your organization to understand the context of the data so that they can work with it quickly. Create a data catalogĪ data catalog is a source of information about the data that exists within your data lake. Ideally, this process will be automated as much as possible by scanning the data lake for signs of data quality issues, such as incomplete or unreadable data. Likewise, your data team should create a process for identifying data quality problems within the data lake. Files that are too large can make your data difficult to work with. For example, in order to standardize file sizes, it's typically wise to set a file size limit for data within the data lake. Including data governance within your data lake architecture requires establishing the right processes, standards, and metrics from the start. Without effective data governance, you lack a systematic and predictable approach to managing data. Data governance also helps to enable effective data quality and data security. Establish governanceĭata governance refers to the processes, standards, and metrics that organizations use to ensure that data can fulfill its intended purpose. 4 data lake architecture best practicesīuilding the right features into your data lake architecture from the start is critical for ensuring that you can work with the data in the ways you need. Even though data lakes are unstructured, it is still important to ensure that they offer the functionality and design features that your organization requires in order to easily interact with the data that they house. The architecture of a data lake refers to the features that are included within a data lake to make it easier to work with that data. The data inside a data lake can take varying forms, and it is not arranged in any particular way. What is data lake architecture?Ī data lake is a repository of information in its "raw" format, meaning the format in which it existed when it was first collected and added to the storage pool. No matter how much data you have within your data lake, it will be of little use if you lack the architectural features to govern the data effectively, keep track of it, and keep it secure. Implementing the right data lake architecture is crucial for turning data into value. 5 Data Lakes Best Practices That Actually Work.Building a Governed Data Lake in the Cloud.Stitch Fully-managed data pipeline for analytics.Talend Data Fabric The unified platform for reliable, accessible data.
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