Data Pipeline and Orchestration

After completing the Design and Planning phase in the data engineering process, we can transition into the implementation and orchestration of our data pipeline. For this step, it is important to have a clear understanding on what is the implementation and orchestration effort as well as what are the programming languages and tooling that are available to enable us to complete those efforts.

It is also important to understand some of the operational requirements, so we can choose the correct platform that should help us deliver on those requirements. Additionally, this is the time to leverage the cloud resources we have provisioned to support an operational pipeline, but before we get deep into those concepts, let’s review some background information about what is exactly a pipeline, how can it be implemented and executed with orchestration?

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Data Pipelines

A data pipeline refers to a series of connected tasks that handles the extract, transform and load (ETL) as well as the extract, load and transform (ELT) operations and integration from a source to a target storage like a data lake or data warehouse. Properly designed pipelines ensure data integrity, quality, and consistency throughout the system.

The use of ETL or ELT depends on the design. For some solutions, a flow task may transform the data prior to loading it into storage. This approach tends to increase the amount of python code and hardware resources used by the hosting environment. For the ELT process, the transformation may be done using SQL code and the data warehouse resources, which often tend to perform great for big data scenarios.

Pipeline Implementation

The implementation of a pipeline refers to the building and/or coding of each task in the pipeline. A task can be implemented using a programming languages like Python or SQL. It can also be implemented using a no-code or low-code tool, which provides a visual interface that allows the engineer to connect to Web services, databases, data lakes and other sources that provide access via API. The use of which technology to use depends on the skill set of the engineering team and cost analysis of the tools that should be used. Let’s compare some of these options in more detail:

The choice between Python, SQL, or low-code tools depends on specific project requirements, team skills, and the complexity of the data processing tasks. Python offers flexibility and control, SQL excels in structured data scenarios, while low-code tools provide rapid development and simplicity.

Pipeline Orchestration

Pipeline orchestration refers to the automation, management and coordination of the data pipeline tasks. It involves the scheduling, workflows, monitoring and recovery of those tasks. The orchestration ensures the execution of those tasks, and it takes care of error handling, retry and the alerting of problems in the pipeline.

Similar to the implementation effort, there are several options for the orchestration approach. There are code-centric, low-code and no-code platforms. Let’s take a look at some of those options.

Orchestration Tooling

When it comes to orchestrating data pipelines, there are several options available.

When comparing these options, it’s essential to consider factors like ease of use, scalability, extensibility, integration with other tools and systems.

Orchestration Operations

In addition to the technical skill set requirements, there is an operational requirement that should be highly considered. Important aspects include automation and monitoring:

Automation and monitoring contribute to compliance and governance requirements. By tracking and documenting data lineage, monitoring data quality, and implementing data governance policies, engineers can ensure regulatory compliance and maintain data integrity and security.

Cloud Resources

When it comes to cloud resources, there are often two components that play a significant role in this process: a Virtual Machine (VM) and the Data Lake.

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Together, a VM and Data Lake form the backbone of the data pipeline infrastructure. They enable efficient data processing, facilitate data integration, and lay the foundation for seamless data analysis and visualization. By leveraging these components, we can stage the data flow into other resources like a data warehouse, which in turn enables the analytical process.


A data pipeline is basically a workflow of tasks that can be executed in Docker containers. The execution, scheduling, managing and monitoring of the pipeline is referred as orchestration. In order to support the operations of the pipeline and its orchestration, we need to provision a VM and data lake cloud resources, which we can also automate with Terraform. By selecting the appropriate programming language and orchestration tools, we can construct resilient pipelines capable of scaling and meeting evolving data demands effectively.

Exercise - Data Pipeline and Orchestration

With a firm grasp of pipeline concepts and orchestration, let’s let’s tackle a hands-on exercise. We’ll navigate the practical world of implementing a pipeline to seamlessly extract CSV data from a source and deliver it in our data lake.