ETL Pipeline Design for Beginners: Architecture Patterns & Design Sample

ETL Pipeline Design for Beginners: Architecture Patterns & Design Sample

Cloud migration has become essential for businesses to achieve updated and real-time efficiency and performance. Therefore, many businesses are shifting to cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud. 

However, precise planning, careful analysis, and proper execution are required to ensure the compatibility of your business requirements with the cloud solution.

Since the process can be rather time-consuming, this is where ETL comes in to automate it all.

ETL is short for Extract, Transform, and Load. As cloud technologies dominate the IT industry, businesses have started migrating their data to cloud environments using ETL tools.

Introduction to ETL

What is ETL? It is simply an abbreviation for “Extract, Transform, and Load”. A suitable definition for ETL would be extracting data from the source and transforming it by combining, deduplication, and ensuring quality to Load the data into the desired target database.

Essentially, ETL tools are means to enable data integration strategies. This is done by supporting businesses to acquire data from multiple sources and then consolidate it into a centralized location. That is to say, ETL tools enable different data types to work together.

A common example of an ETL process is collecting and refining various data types and delivering them to a data warehouse or data lakes, such as Amazon Redshift, Azure Data Lake Storage Gen2, and Google BigQuery.

It is a fully automated process that performs data extraction from legacy sources for analysis by using connectors. The transformation bit is carried out using calculations such as aggregation, business transformation, filters, ranking, etc. There are almost no complications involved and it’s a three-step process as the name suggests.

With that said and done what is an ETL pipeline?

ETL Pipeline: Overview and Importance

An ETL pipeline comprises programs or tools that perform data extraction and transform it per business needs. Ultimately, loading it to the target destination. For instance, data warehouses, databases, data lakes, etc., for further processing.

So, overlapping the two definitions, ETL is a process and ETL pipeline is the flow in which it is carried out. Here’s a schematic diagram to give you an idea:

Now moving forward, the significance of an ETL Pipeline is as follows:

1. ETL pipeline offers more control. In addition, the ability to monitor and schedule jobs.
2. ETL pipeline links the tools or processes of ETL and automates the entire procedure. In other words, everything is taken care of with minimal manual labor involved.
3. You can also get recovery management and restart ability in case of job failures through the ETL pipeline.
4. Finally, ETL pipeline tools such as Google Cloud Data Flow, Airflow, and AWS Step Function, all offer an incredibly user-friendly interface to manage the flows.

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Data Pipeline Architecture

A data pipeline architecture is a wide-ranging system of pipelines. These pipelines are responsible for connecting various data sources. In addition, data processing systems, storage layers, applications, and analytics tools.

However, the data pipeline architecture definition might differ based on context. For instance:

• Frameworks and specific set of tools – These are used in only particular scenarios for a specific role.
• Logical architecture – that creates an outline of the processes and transformation procedures a dataset undergoes. This covers the entire scope from extraction to serving.

Here’s a schematic diagram to differentiate between the two:

Architecture Patterns: ETL Pipelines Centered on an EDW (Enterprise Data Warehouse)

There is no limit to the number of ways you can design a data architecture. However, we’ll focus on one of the most common architecture patterns for data pipelines. In this case, “ETL pipelines centered on an EDW (Enterprise Data Warehouse)”.

This architecture pattern is the “legacy” way of handling massive data volumes. The business may work around a single data warehouse. This may or may not be supplemented with some domain-specific data marts.

Two teams play a key role and they are data engineers and central IT teams. They have the full responsibility of moving data from the extraction point to the target destination i.e., warehouses in a schema as well as bringing together the different datasets together. Joining the disparate datasets enables deeper analytics.

Moreover, addition IT experts will work with data analysts who use SQL to query the data warehouses. However, operational and analytical consumption must be supported while data is kept available and the production environment is kept disruption-free.

The main advantage of this architecture pattern is that it keeps the data highly reliable and consistent. Moreover, it ensures that the company is committed to a single source. Nevertheless, it can be quite a brittle architecture that may lead to technical debt at each step.

An additional drawback of the ETL architecture pattern is that the data warehouse is specifically built for batch-oriented and structured data. Whereas, the majority of the world deals with streaming and complex datasets.

1. ETL Pipeline Architecture

As you have learned that an ETL pipeline architecture portrays how the ETL data pipeline processes will run at each step from start to end. Moreover, you are already familiar with how a source and target destination is involved in this process.

Here’s a clearer design sample to give you a better idea of the architecture of ETL. 

Now let’s break down each stage of the ETL data pipeline to fully understand how it works.

1.1 Extract Architecture Design

The first bit is “extract” and when designing the architecture for data extraction, there are three possible approaches to choose from for core solution implementation:

1.1.1 Full Extraction

In full extraction, each extraction will collect all the data from the source point and move it down the data pipeline.

1.1.2 Incremental Extraction

Slightly differing from the first one, incremental extraction works with cycles. To clarify, every time the ETL pipeline runs, only the new, as well as modified data, is extracted from the source. A good example would be data collection using an API.

1.1.3 Source-Driven Extraction

In this approach, the source sends a notification to the ETL system that the data has been modified. This runs the ETL pipeline to extract the new/modified data. An example of source-driven extraction is data collection using webhooks.

2.1 Transform Architecture Design

Similar to extraction, there are many things to consider while designing transform architecture:

2.1.1 Operations Order

As the name suggests, the results are influenced by the order in which you apply the transform rules to incoming data. For example, consider two transform scripts.

Your first one will process the data to compute a consecutive number of sales made to a customer. Whereas, the second process will drop sales information from the data pipeline unless there is a billing address.

Now, in that scenario, if the row for the customer missing a billing address is dropped, the result will be different.

2.1.2 Business Logic

The transformation of the ETL process implements business logic. This could be the calculation of a client’s lifetime value or the number of purchases they made.

Therefore, the architecture needs to be made in a manner that can handle corrupt or missing data and transform the purchases. Hence, supporting the business logic implementation part.

2.1.3 Algorithmic Efficiency

Since “transform” combs through extracted data, therefore, heavy loads are inevitable. Algorithmic efficiency in architecture design can make a significant difference in terms of the time taken for a data transform to execute.

Here’s an example, implementing a phone directory solution for 2M rows transformation vs a “for loop” results in different outcomes.

2.1.4 Quality Assurance

Normally, transformation is the place where the source data is validated against a prescribed criterion and monitored to ensure the quality of data.

At this phase,  numerous ETL processes are designed with notifications to alert the developers regarding any errors and rules that are hindering the data from being passed on the pipelines.

3.1 Load Architecture Design

Once again, there are different possible architecture designs for data being loaded to a target destination. They are as follows:

3.1.1 Full Load

The main mechanism of full load is – dumping all the data into the destination database in one go.

3.1.2 Incremental Batch Load

Incremental batch inserts data into the target database at prescribed intervals. It is similar to what you have learned about incremental extraction.

3.1.3 Incremental Stream Load

Similarly, incremental stream load inserts transformed data into the target database whenever new data may emerge or old source data is modified.

How to build an ETL Pipeline: For Beginners

As promised, here’s a step-by-step guide to building your end-to-end ETL data pipeline using batch processing:

Step 1: Set up Reference Data

Create your dataset that may define the set of permissible values within the data. For instance, in a phone number data field, specify the list of country codes that you allowed.

Step 2: Extract and Standardize Data from Source Using Connectors

Correct data extraction is the basis for the success of ETL steps. Extract data from various sources, such as non/relational databases, APIs, JSON, XML, and CSV files. Convert them into a single format for standardized processing.

Step 3: Validate Your Data

Now, keep your data that has the prescribed values and reject any data that does not. For instance, if you only want the dates from the year 2022, reject any values older than 12 months.

Work on an analysis of the rejected records to identify any issues, correct where needed, and change the extraction process to ensure future batches run smoothly.

Step 4: Data Transformation

Start by cleaning – remove any duplicate records, check the data integrity, create aggregates where necessary, and implement business rules. For instance, if you only want to analyze the business profits, you can summarize the USD amount of invoices into a monthly, weekly, or daily total.

However, you must program numerous functions for automatic data transformation.

Step 5: Data Staging

After data transformation, the data will be stored in this layer. We do not recommend that you load data directly into the target database or warehouse.

To clarify, the staging layer lets you roll back data easily if something goes wrong. Moreover, it also helps in generating audit reports for proper diagnosis, analysis, and even regulatory compliance.

Step 6: Loading Data to Warehouse

This is almost the end of the journey. The data that moved passed the staging layer has now arrived at the data warehouse. You may now append or overwrite any existing information each time the ETL pipeline loads a new batch.

Step 7: Setting up Schedules

This is an additional step but it is the most critical step in automating the ETL pipeline. Based on your preference, you can select the frequency at which the ETL pipeline may run.

You may include timestamps to identify the loading date.

How to build an ETL Pipeline with Stream Processing?

While you’re reading, here’s a concise outline for building an ETL pipeline with stream processing. For this guide, we’ll be using Apache Kafka.

Step 1: Extract Your Data into Apache Kafka

The very first step is to extract the source data into Kafka. In this guide, you can use the Confluent JDBC connector. If you are feeling confident, go with custom codes that can pull records from the source and write them into Kafka topics.

Kafka can automatically pull up the data whenever new records are added to the source. It then pushes them to the topic as a new message. This enables a real-time data stream.

Step 2: Pull the Data from Kafka Topics

The ETL application will extract your source data from Kafka topics either in AVRO or JSON format. These are then deserialized to perform transformations through KStreams.

Step 3: Transform the Data into KStream Objects

After you have pulled the data from Kafka topics, it can now be transformed on KStream objects. You can use Java, Python, Spark, or any other compatible programming language.

The Kafka streams will process one record at a time. This will result in either one or multiple outcomes depending on the built transformation.

Step 4: Load Data to Other Systems

Finally, once the transformation completes, the ETL app will load the streams into the target destinations i.e., data lakes or data warehouses.

Takeaway

In this article, you have learned through a structured approach – what is ETL, data pipelines, ETL pipeline designs, and how you can build an ETL pipeline. Moreover, you also have an in-depth understanding of ETL pipeline architecture design and every step involved. This knowledge will certainly come in handy when developing your customized ETL pipeline.

However, as a data engineer, there are some basics you need to cover beforehand. If you truly want to master the craft and land a high-paying cloud job, have a look at the Google Cloud Professional Data Engineer Course. In this concise course, you will learn how to design data processing systems and how to build and operationalize data processing systems.