#ETL PROCESSES FOR NEW DATASETS SERIES#

Because datasets are being automated using data transformation, this data can be ingested into different reverse ETL tools, giving stakeholders the data they need, where and when they need it.ĭata transformation is fun, but tough work for analytics practitioners. Transformation allows analytics engineers to join different datasets into one data model, providing all the needed data in one dataset. These data sets can be referenced directly within a report or dashboard instead, speeding up compute time.ĭata transformation also activates the reverse ETL process. By automating certain data models, data analysts do not need to repeat the same calculations over and over again within the BI layer. This ensures consistent, accurate, and meaningful datasets are being generated in the data warehouse each day, or on whatever time cadence your business chooses. As you develop consistent metric definitions, your data quality increases, trust in your data work increases, and the ROI of a data team becomes much more apparent.ĭata transformation allows you to automate various data cleaning and metric calculations. dbt supports the creation of metrics and exposing them via the Semantic Layer, ultimately allowing you to create and apply the same metric calculation across different models, datasets, and BI tools, ensuring consistency across your stack. In addition to standardizing raw data sources, metrics can be properly created during the transformation process. Data analysts don’t need to remember which dataset is in which timezone or currency they know the data is high-quality because of the standardization that has taken place. This transformation process should also follow automated testing practices, ultimately creating tables data analysts and end business users can trust.īy transforming your data with tooling that supports standardization, version control, integrated documentation, modularity, and testing, you leave little room for error. In an ideal world, these transformations are version-controlled and peer-reviewed. During the transformation process, raw data is cleaned, casted, converted, joined, and aggregated using SQL and Python to create end datasets that are consumed by business users. Why is data transformation the foundation for modern data analytics? Because it’s the baseline for increasing the data quality of your business and creating meaningful data for your end users.ĭata transformation can increase data quality through the process of standardization, testing, and automation.

#ETL PROCESSES FOR NEW DATASETS SERIES#

Without data transformation, analysts would be writing ad hoc queries against raw data sources, data engineers will be bogged down in maintaining deeply technical pipelines, and business users will not be able to make data-informed decisions in a scalable way.Īs a result, data transformation is at the heart of a business: good transformation creates clear, concise datasets that don’t have to be questioned when used, empowers data analysts to take part in the analytics workflow, and presents a series of challenges that keeps analytics work interesting 😉 Data transformation is a key component of the ETL/ ELT process where the “T” represents the data transformation stage, and is typically performed by the analytics engineer on the team or, depending on the organizational structure and needs, data analysts or data engineers. These end datasets are often exposed in a business intelligence (BI) tool and form the backbone of data-driven business decisions.

Data transformation is the process of taking raw source data and using SQL and Python to clean, join, aggregate, and implement business logic to create important datasets.

Etl processes for new datasets