While generally following these excellent normalization tips for tabular data, real world situations will sometimes favor the simplicity of a tabular structure even if the table violates second normal form. Even when working with CSV files or spreadsheets it is important to pay attention to First Normal Form which specifies “no repeating groups” and Second Normal Form which demands that “each column must depend on the primary key”. ![]() 3) Lack of ‘normalization’ does not unreasonably increase data volumes.Įxperienced database designers go to great lengths to follow the principles of database normalization. This does not mean that data immediately need to be stored in a relational database to answer relational questions just that some software will have to read all of the data into memory before generating a data subset such as “A where B > C”.Īs a general rule, tabular structure and basic formats like CSV are preferred when data are collected as long time series regardless of what you intend to do with the data later. On the other hand, asking questions about relationships between measurements does not fall out of this structure so easily. Storing data this way also makes it easy to extract data for use in time series and correlation plots by pulling out selected columns. When data are organized like this it is easy to answer the question: “What set of measurements was collected at time … ?” by simply pulling out a single row of data. It is best to think of tabular data as being ‘organized by row’ where each row corresponds to a unique identifier such as the time a measurement was made. Here we see how the anticipated use of data affects how the data should be structured. Missing for a general discussion of missing values.) 2) Typical queries will map a record identifier onto one or more variables. If any row is lacking information for a particular column a missing value must be stored in that cell. Let’s review the basic properties that make a dataset intrinsically tabular: 1) Every record shares the same set of variables.Īnother way of describing this in terms of rows and columns would be: “Every row has the same set of column headers.” Tabular data are inherently rectangular and cannot have “ragged rows”. Even RDBMS (Relation Data Base Management Systems) have the data table as their fundamental unit of organization. Elementary students learn how to organize data into rows and columns at a very early age while high school students master the intricacies of spreadsheets. The data table, arguably the oldest data structure, is both a way of organizing data for processing by machines and of presenting data visually for consumption by humans. Tabular Dataįor most people working with small amounts of data, the data table is the fundamental unit of organization. ![]() And it is always good to expand your knowledge of other tools. Even if most of your work involves data of one particular type it is a valuable exercise to consider how else data can be structured. In this post we will review two of the most popular data structures and describe how they differ and when to choose one over the other. Many datasets, however, are not relational at all and are better stored in tabular or gridded formats. If all you know is SQL, all data look relational. If all you have is a hammer, everything looks like a nail. In this case, the Law of the Instrument applies to data management just as it does to carpentry: Choosing data formats and software tools that match a dataset’s intrinsic structure will allow the data to slide into place with a minimum of hammeringįar too often, those tasked with managing data are familiar with a fairly small set of tools for getting the job done. ![]() But we have all learned - sometimes more than once - that it is much easier if peg and hole have the same shape.ĭata managers also need to carefully consider the shape of their data to determine which data structures best describe their situation. ![]() With enough effort it is possible to fit a square peg into a round hole.
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