Data transformation is the process of modifying, filtering, aggregating, or enriching data according to predefined rules and logic. It is an essential step in data analysis, as it can help you prepare your data for further exploration, visualization, or modeling. In this article, you will learn how to transform data with rules and logic using some common data transformation tools and techniques.
Data transformation can help you achieve a variety of objectives, such as cleaning and standardizing your data to get rid of errors, outliers, duplicates, or missing values. It can also reshape and restructure your data to make it easier to work with, like pivoting, merging, splitting, or transposing data. Additionally, data transformation can be used to enhance and enrich your data by adding more information, such as calculating new metrics, creating categories, or joining external data sources. Moreover, you can use data transformation to filter and aggregate your data to focus on specific aspects like selecting subsets or applying summary statistics.
Quando trabalhamos com dados, o mais crucial é garantir que eles estejam corretos e capturados de maneira consistente em todas as partes. Organiza??o é chave!
When ever working with data, the most important aspect is ensuring the integrity of the data, and spending the time to ensure that the data has been captured in a way that is standard across the subsets. Ensuring a strong structure and organization of the data is incredibly important.
Transforming data with rules and logic is like being a digital detective, sorting and organizing information to make it more useful. Think of it as giving your data a set of instructions – if certain conditions are met, specific changes happen. It's a bit like telling your data, "Hey, if this happens, do that." This helps clean up messy data, making it more organized and easy to understand. Whether it's fixing errors or rearranging information, applying rules and logic is the secret sauce that turns raw data into a clear, structured story. It's like decoding a puzzle to reveal the insights hidden within your data.
Data transformation tools come in many forms, depending on your needs, preferences, and skills. Excel is a widely used and easy to learn spreadsheet application that allows you to manipulate data with formulas, functions, and macros. However, it can be limited in scalability and automation. SQL is a powerful and flexible query language that requires some technical knowledge and access to a database system. Python is a versatile and expressive programming language that requires coding skills and a suitable environment. R is a specialized and efficient programming language that also requires coding skills and a suitable environment. All of these tools can help you transform data with rules and logic.
The exact steps and syntax for applying rules and logic to transform data will vary depending on the tool you use, but the general principles remain the same. For example, to remove duplicates from a data set, you can use the Remove Duplicates feature in Excel, the DISTINCT keyword in SQL, the drop_duplicates() function in Python, or the distinct() function in R. Similarly, if you want to split a column into multiple columns based on a delimiter, you can use the Text to Columns feature in Excel, the SUBSTRING and CHARINDEX functions in SQL, the str.split() method in Python, or the separate() function in R. Lastly, if you need to calculate the average of a numeric column grouped by another column, you can use the AVERAGEIF function in Excel, the AVG and GROUP BY clauses in SQL, the groupby() and mean() methods in Python, or the group_by() and summarise() functions in R.
SQL - For near duplicate records - RANK is a powerful function that can be implemented in SQL during normalisation of data. This will help utilise the right data record in your KPI calculations. For completely duplicate records - use group by on your required table keys having count >1. Power BI - there is a inbuilt function that could be used via tools ribbon in power query and delete all duplicate records from your underlying data model.
Para registros quase duplicados, a fun??o RANK no SQL pode ajudar a selecionar o registro de dados correto para cálculos de KPI. Para registros completamente duplicados, a cláusula GROUP BY pode ser usada com contagem para identificá-los. No Power BI, uma fun??o integrada permite excluir registros duplicados do modelo de dados.
Once you have transformed data with rules and logic, it's imperative to test and validate the results to guarantee accuracy and consistency. You can compare the original and transformed data sets for any discrepancies, errors, or unexpected changes. Additionally, descriptive statistics, charts, or tables can be used to summarize and visualize the data to identify any patterns, trends, or outliers. Quality checks should also be applied, such as counting the number of rows and columns, checking the data types and formats, or verifying calculations and formulas.
Dê uma olhada nos resultados para ver se algo n?o bate usando o teste de completude. Use um truque simples com SQL para ver a diferen?a entre os dados originais e os transformados. Contabilize e veja quais dados faltam no conjunto transformado.
Compare results for any discrepancies using completeness test. A simple sql query could be written to show the difference of data records at the source level and after transformation. Use count and view your data records missing from transformed set.
Finally, when you transform data with rules and logic, it is important to document and communicate your process and outcomes to yourself and others. Doing so can help keep track of your data transformation steps and logic, explain decisions and assumptions, and share results and insights. There are several ways to document and communicate your data transformation such as using comments, notes or annotations within the tool or code, metadata, logs or reports outside the tool or code, as well as presentations, dashboards or stories to showcase your data transformation results and insights to your audience.