Analyzing business data is one of the keystones of modern data-driven commerce. Through data transformation, organizations can alter the structure and format of raw data to run more efficient data analytics operations. Let’s explore the role of data transformation for digital businesses in 2023.
Data transformation is the process of changing the format, structure, or values of data into a usable format to support decision-making processes in an organization.
Businesses usually transform data when different data formats need to be converted to match the target destination system, which can occur at two stages of the data pipeline:
1. Before loading the data (ETL)
Organizations that use on-premises data warehouses generally prefer an ETL process (extract, transform, load) in which data is transformed in the middle step.
2. After loading the data (ELT)
However, today, most organizations use cloud-based data warehouses and data transformation tools that can scale compute and storage with latency measured in seconds in minutes, achieving near real-time insights.
Thanks to the scalability of the cloud, teams can skip preload transformations and load raw data into the cloud data warehouse, such as Google BigQuery, and transform it at query time.
This model is called ELT (extract, load, transform).
Data management processes like data integration, data migration, data warehousing, and data wrangling may all include data transformation.
Businesses can choose among a variety of ETL tools that automate the data transformation process. Data analysts, data engineers, and data scientists, on the other hand, often prefer the ELT model as it allows them to prepare raw data for data analysis using scripting languages like Python or domain-specific languages like SQL.
To create accurate reports and visualize your transformed data, you can use Whatagraph’s data visualization service. Now you can create interactive dashboards using any data available in your BigQuery account.
Creating a new report with Whatagraph has never been easier — just select your BigQuery account and connect your sources.
When your report is created, you can use the filter function to show only a specific subset as well as drag-and-drop individual fields with metrics and dimensions to customize your report.
A solution? Use a data transformation tool with transparent pricing and a cloud-based data warehouse, such as Google BigQuery.
However, if you use a cloud-based warehouse, you can do the transformations after loading because the platform can scale up to meet demand.
You can avoid this using a fully-managed data pipeline where no data is lost or misplaced.
To avoid this, you can use a data transformation tool that supports data virtualization (DV). In this system, data analysts get a virtualized view of different data sets on demand without having to enlist an IT team to load the data into a data warehouse.
The first phase of data transformation includes the conversion of different data types and the flattening of hierarchical data. The aim of these operations is to increase data compatibility with analytics systems while implementing further transformations through added layers of processing.
In the modern ELT processes, data ingestion starts with extracting information from a data source, followed by copying the data to its destination. Initial transformations focus on shaping the format and structure of data to make sure it’s compatible with both the destination systems and data already stored there.
An example of this type of data transformation is parsing fields out of comma-delimited log data for loading into a relational database.
Data mapping and translation of data are among the most basic data transformations. For example, a column containing integers that represent error codes can be mapped to the relevant error descriptions. This way, the column becomes easier to understand and more useful in customer-facing frontends.
Translation converts data from formats used in one system to formats suitable for a different system. Even when parsing is done, web data might arrive as JSON or XML files and needs translating into row-and-column data to be included in a relational database.
In many aspects, data transformation relates to whittling down data and making it more manageable. You can consolidate data by filtering out unnecessary fields, columns, and records.
These might include numerical indexes in data intended for graphs and dashboards or records from business regions that are irrelevant to a particular report.
Business data might also be aggregated or summarized by transforming a time series of customer transactions into hourly or daily sales counts.
Business intelligence tools can also help with filtering and aggregation, but it’s better to complete the transformations before a reporting tool can access the data.
Data from different sources is often merged to create denormalized enriched information. For example, a customer’s transaction can be bundled up into a grand total and added to a customer information table for a quicker reference or the customer’s analytics systems.
These transformations split long or freeform fields into multiple columns, imput missing values, and replace corrupted data.
This technique creates interval labels in continuous data aiming to enhance its efficiency and make it easier to analyze. This process uses decision tree algorithms that transform large datasets into categorical data.
Low-level data attributes are transformed into high-level attributes by applying the concept of hierarchies and creating layers of successive summary data.
Data is sometimes transformed to be ordered logically or to suit a data storage scheme. In the case of relational database management systems, creating indexes can improve performance or the management of relationships between separate tables.
Data containing personally identifiable information or other privacy-sensitive information should be anonymized before propagation. Encryption of private data is already a requirement in many industries, where systems perform encryption at multiple levels, from personal database cells to entire records.
Last but not least, there’s a set of transformations that reshapes data without changing content. This includes casting and converting data types for compatibility, adjusting dates and times, and renaming schemas, tables, and columns for clarity.
Before you can run analytics on your business data, you must transform the data, and to do that, you must load it to a data warehouse that supports big structured and unstructured data analytics. Most organizations today chose a cloud data warehouse to load their data.
Whatagraph allows you to load data from multiple platforms in just three steps.
Book a demo call with our product manager, and you’ll quickly realize why Whatagraph + BigQuery combo is the most optimal solution for moving your data.
Published on Feb 03, 2023
WRITTEN BYNikola Gemes
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