Data Engineering: A Workflow with Six Steps for ELT

Six phases make up the data transformation process: exploration, extraction, extraction and Bentley Microstation loading, transformation, transformation, testing, documentation, and deployment.

The foundation of CDE Solution provider all analytics work is data transformation, which is the process of taking raw data and giving it meaning. Data transformation is also a key way that data professionals add real value to their organizations.

The six generally specified phases of CDE solution the data transformation process are: extraction and loading, exploration, transformation, testing, documentation, and deployment. Following the completion of these procedures, the raw data assumes a new, useful shape that aids the company's business intelligence initiatives.

On this page, we'll go through how data is transformed in a typical ELT procedure.

Extract and load is step one.

The extraction and loading procedures must be finished before beginning the transformation effort if your team uses an ELT process that requires the raw data sources to be extracted and loaded into a data warehouse before the data is converted.

Extraction

Data from several sources that are pertinent to your organization are extracted throughout the extraction process. The team intends to use a large portion of the retrieved data for their analytics work. Back-end application databases, marketing platforms, email and sales CRMs, etc. are a few examples of data sources.

By engaging with Application Programming Interfaces (APIs) through customized scripts or by relying on the utilization of open source or Software-as-a-Service (SaaS) ETL solutions to reduce some of the technical burden, this data is frequently pulled from their systems.

Loading

The extracted data is imported into the target data warehouse during the load phase. The current data warehouses Snowflake, Amazon Redshift, and Google BigQuery are a few instances of. Data lakes from Databricks are an example of another type of data storage platform. The majority of SaaS apps that extract data also load that data into a target data warehouse. Data engineering and technical expertise are often needed for custom or internal extraction and loading methods.

Data exploration in step two

It's time to comprehend how the raw data looks once it has been placed in the data warehouse. You will now be under the following circumstances:

ERDs (Entity Relationship Diagrams) and join keys are examined to comprehend how the data is joined.

Identify the columns that could contain missing values or incorrect column names.

Write standard queries to carry out a quick analysis or summary statistics on the data, such as how many rows there are. In how many rows? Is the main key distinctive? Exists a main key, if so?

Recognize the variations in data types, time zones, and currencies across different data sources.

There is no ideal way to examine unprocessed data; instead, follow the instructions provided by the data source. When compared to doubting the integrity of the data (which may be your natural inclination if you are a data practitioner), this stage may become easier if you have a high level of trust in the accuracy and integrity of the raw data.

Data Conversion in Step 3

It's now time to begin your modeling process in the actual transformation stage now that the raw data has been imported into the data warehouse, you are familiar with its structure, and you have a general idea of what to do with it. You might have noticed a few things about this data when you initially looked at it during the data exploration phase.

Column names could or might not be obvious.

No table is connected to another table.

The timestamps can be in the wrong reporting time zone.

Due to their richness, JSON resources may need to be un-nested.

Primary key may not be present in the table. Consequently, a shift is needed! When the transformation really takes place, the data from the data source is typically:

Lightly modified means that fields were successfully translated, time zones for fields with time stamps were synchronized, tables and fields were given the proper names, etc. This typically occurs in the staging model used in dbt to produce a clear, homogeneous foundation for the data.

Numerous transformations, including the addition of business logic, the creation of suitable materializations, the linking of data, the creation of aggregates and metrics, etc. This often happens in dbt's market and intermediate models, resulting in tables that are eventually made available to end users and business intelligence (BI) tools.

Writing modular and version-controlled transformations in SQL and Python using contemporary tools like dbt are common methods for converting data. Other options include using stored procedures or creating unique SQL and Python scripts that are automatically performed by a scheduler.

Step 4: Testing the data

Now that your data has been modeled, it seems to be generally accurate, but how can you be sure? How do you make sure that the important metrics and data you reveal to consumers farther down are reputable and trustworthy?

Perform data testing that complies with your organization's requirements at this point. This could resemble:

checking the uniqueness and non-nullability of primary keys

ensuring that column values are within anticipated ranges

checking the lineups of model relationships, etc.

You may build a system where you can quickly and often test transformations against the criteria you specify by using a tool like dbt, which allows you to design code-based tests to execute against data transformations.

Step 5: Documenting the Data

Your data will be turned into useful business entities, evaluated in accordance with your standards, and made accessible to end users in the open. How do you think they will be seen by people who are not directly involved in the change process? What kind of documentation are you producing to spell out the transformation's business logic and outline its primary metrics and columns?

Although the transition is finished at this point, the job has only just begun. Reliable documentation must be created and kept up to date in order for a data transformation to have meaning and impact for end users.

We advise that you record the following before starting the conversion process:

The transformation or data model's main use - What was the main impetus for this transformation? What crucial reporting assistance does it provide your BI tool?

Important columns with unclear column names or business logic implementation

Measurement and Aggregation

Instead than concentrating on how to execute the transformation for more technical users, documentation for the data transformation process should typically be produced in a way that is intelligible to those who were not engaged in the original process. An vital method to guarantee that your end-users are at ease and empowered to work with the data that your team has worked so hard to produce is to ensure that the conversion documentation is written in a way that encourages the engagement of business users in the analysis.

Step 6: Automate and Plan the Deployment

It's time to launch your data transformations when they have been developed, tested, and documented. A data engineer, data analyst, or analytics engineer now pushes these transformations into production, running them in the data warehouse's production environment. These production-ready tables are what the BI tools will read and what the analysts will query for analysis.

These data conversions must be refreshed or updated at a speed that matches the demands of the company using some kind of planning procedure or business process orchestration application. With the help of tools like dbt Cloud, you can write data transformations and tests in a group-based integrated development environment (IDE) and then schedule them to execute on a regular basis. Utilizing unique cron schedulers or external business process orchestrators are further methods that frequently call for more technological improvements.

Starting now!

There will always be more data transformation work to do for each new data source, business challenge, or entity required. After constructing the fundamental data transformation, your attention may transition to optimization, governance, and democratization activities. Therefore, a successful data transformation process is both strict and adaptable; it provides enough boundaries to keep the analytics work worthwhile and orderly, as well as enough room for it to be engaging, difficult, and tailored to your company's needs.