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  Kim Harrison
  

How to Create Data Pipelines

Introduction to Data Pipelines

In today’s data-driven world developers and product managers rely on smooth, automated workflows to process, analyze, and leverage big data effectively, particularly as organizations look to launch data-hungry AI programs. Whether you're a data engineer, a software developer, or a business analyst, setting up a well-structured data pipeline is crucial for ensuring data accuracy, scalability, and using real-time data for timely decision-making.

The problem is building a data pipeline the wrong way can lead to inefficiencies, errors, and costly rework. Without a clear guide, you might struggle with slow or unreliable data processing, complex maintenance and debugging, scalability issues as data volume grows, and/or inconsistent or inaccurate data outputs.

This article will review what data pipelines are, why they’re important, how to create one, and common challenges when getting started, without having to get a degree in data science.

What Are Data Pipelines?

At its core, a data pipeline is a series of interconnected steps that automate the flow of data from its source to its destination. These steps typically include:

• Extraction: Gathering data from various sources, such as databases, APIs, and files.
• Transformation: Cleaning, standardizing, and enriching the data to ensure consistency and quality.
• Loading: Storing the processed data into a data warehouse, data lake, or other target system.

>> Click to zoom in on this infographic
Some of the typical components for data pipelines include tooling for collection, processing, storage, consumption, monitoring, security, orchestration, and networking.

Why Create Data Pipelines?

A data pipeline is a series of processes that extra, transform, load data into its destination as transformed data, properly formatted and structured. When designed correctly, it ensures efficiency, scalability, reliability, and real-time processing.

A data pipeline can automate repetitive tasks, saving time and reducing errors. It properly manages the types of data you need to transport and manipulate. It can handle increasing data loads without performance drops. It maintains data integrity from source to destination. And it can enable faster decision-making with up-to-date data.

A poorly built pipeline, on the other hand, can lead to data loss, bottlenecks, and unreliable analytics—costing your business time and money.

Effective data pipelines consist of:

• Data Collection & Ingestion – Collecting data from sources (APIs, databases, logs, etc.).
• Data Processing – Cleaning, transforming, and enriching raw data, either as batch processing or streaming.
• Data Storage – Storing processed data in warehouses (BigQuery, Snowflake) or lakes (S3, Hadoop).
• Data Consumption – Delivering data to data visualization dashboards, ML models, or different data-as-a-service apps per business needs.

Data pipelines are fundamental to an organization's ability to effectively deploy AI across its operations. AI and machine learning models thrive on vast amounts of high-quality data. Inaccurate, inconsistent, or incomplete data leads to flawed models and unreliable results.

Data pipelines ensure that data is cleaned, transformed, and standardized, providing the necessary foundation for accurate AI model training. Pipelines also enable organizations to implement data quality checks and validation processes, ensuring that only reliable data is used for AI initiatives. This is critical for avoiding biased or inaccurate AI outputs, which can have significant consequences in real-world applications.

How to Create a Data Pipeline

In today's data-saturated landscape, turning raw data into actionable insights requires a structured approach. That's where data pipelines come in. Let's explore the process of building your own pipeline, from initial planning to ongoing monitoring.

Step 1: Plan and Set Goals

Before you begin to build, it's crucial to define your pipeline's purpose. Proper planning prevents costly rework and ensures your pipeline delivers the desired outcomes. Some questions to ask could include:

• What problem are you solving? (Real-time analytics? AI training? Reporting? Improve sales forecasting?)
• What data do you need? (Structured databases? Unstructured logs? Website logs? Sales or marketing data? APIs?)
• How frequently should data update? (Batch vs. real-time?)
• What are the key performance indicators (KPIs) you want to track? (e.g. data latency, data quality, processing time)
• Who will use this data? (Data scientists? Business analysts? AI models?)
• What are your scalability needs? (How much data will you handle today? Next year? Will data volume grow?)
• What is your budget? (For tools, cloud services, and personnel)
• What are your security and compliance needs? (GDPR, HIPAA, etc.)

Example goals could include:

• Ingest daily sales data from three sources into a data warehouse for BI dashboards.
• Stream IoT sensor data in real-time for predictive maintenance AI.

Without planning, you risk building an inefficient or unsustainable pipeline.

Step 2: Select Data Sources

Your data pipeline's effectiveness depends on its ability to connect to diverse data sources. Common sources include:

• Databases (Relational databases like PostgreSQL or MySQL vs. non-relational databases like MongoDB or Cassandra): Connected with SQL queries, JDBC/ODBC connectors
• APIs (REST, SOAP, GraphQL): Connected with Python (requests), API clients
• Cloud Storage (Amazon S3, Google Cloud Storage, Azure Blob Storage): Connected with SDKs (Boto3, Google Cloud Storage, Bloblang)
• Streaming Data (Kafka, IoT): Connected with Kafka consumers, WebSocket listeners
• Flat Files (CSV, JSON, XML, Parquet): Connected with Pandas (read_csv), custom parsers

Don’t forget, it's a good practice to check for rate limits (APIs) and data formats (JSON vs. Avro).

Step 3: Create a Data Processing Plan

Data processing involves cleaning, transforming, and enriching your data. Tools for this step include:

• Python: Libraries like Pandas, NumPy, and scikit-learn provide powerful data manipulation capabilities.
• ETL Tools: Tools like Talend, Apache NiFi, and Informatica offer visual interfaces for building data transformations.
• Cloud Data Processing Services: Services like AWS Glue, Google Cloud Dataflow, and Azure Data Factory provide scalable and managed data processing.

A detailed processing plan should outline data cleaning steps, like removing duplicates and handling missing values. It should include data transformation logic such as data type conversions, aggregations, and joins. The plan should also include data enrichment processes, such as adding external data sources. And it should have data validation rules, like ensuring data consistency and accuracy.

Step 4: Determine your data output storage solution

Choosing the right storage solution is critical for data accessibility and performance. Some options are:

• Cloud Storage: Cost-effective for storing large volumes of data.
• Data Warehouses: Optimized for analytical queries and business intelligence. (Snowflake, BigQuery, Redshift)
• Data Lakes: Store raw, unstructured data for data exploration and machine learning. (Hadoop, S3, Azure Data Lake Storage)
• Flat Files: Simple and portable, but not suitable for large-scale data.

Step 5: Determine How Your Data Will Flow

A flowchart diagram helps visualize the data flow and identify potential bottlenecks. Data orchestration tools, such as Apache Airflow, Prefect, or Dagster, automate the execution of pipeline tasks and ensure proper workflow. These tools enable:

• Task scheduling and dependencies
• Error handling and retries
• Pipeline monitoring and logging

Step 6: Build the Connections and Test Code

Building connections involves writing code or configuring ETL tools to connect to your data sources and storage solutions. Testing is crucial to ensure data accuracy, integrity, and organization into correct schema.

Connection Examples:

• Python: psycopg2 for PostgreSQL, pymongo for MongoDB, requests for APIs.
• ETL Tools: Drag-and-drop interfaces to configure data source and target connections.

Testing:

• Unit Tests: Verify individual data transformation functions.
• Integration Tests: Test the entire pipeline workflow.
• Data Quality Checks: Validate data accuracy and completeness.

Step 7: Monitor Movement of Data

Continuous monitoring is essential for identifying and resolving issues promptly. Monitoring tools can track:

• Data Latency: The time it takes for data to flow through the pipeline.
• Data Quality: The accuracy and completeness of the data.
• Pipeline Performance: Processing time, resource utilization, and error rates.
• Alerts: Setup alerts to notify you of any anomalies.

Integrations with Data Pipelines

Integrations are the lifeblood of effective data pipelines. Without seamless connections between various tools and components, data pipelines become fragmented and inefficient, hindering an organization's ability to extract valuable insights.

Integrations automate the movement of data between different systems, eliminating manual data transfers and reducing the risk of errors. They ensure data compatibility, promoting data consistency. Real-time integrations enable the continuous flow of data, supporting real-time analytics and decision-making. They enable centralized monitoring and management of the entire data pipeline, simplifying troubleshooting and maintenance.

Integrations streamline data workflows, freeing up data engineers and analysts to focus on higher-value tasks. And they allow organizations to scale their data pipelines as data volumes grow and adapt to changing business requirements.

Here's a breakdown of the importance of integrations, focusing on key data pipeline tools.

Data Warehouses

Data warehouses (e.g. Snowflake, BigQuery, Amazon Redshift) serve as central repositories for structured data, optimized for analytical queries. Integrations include:

• ETL/ELT Tools: Seamless integration with ETL/ELT tools (e.g. dbt, Fivetran, Airbyte) is crucial for loading and transforming data into the warehouse.
• Business Intelligence (BI) Tools: Integration with BI tools (e.g. Tableau, Power BI) enables users to visualize and analyze data stored in the warehouse.
• Data Orchestration Tools: Integration with orchestration tools ensures that data loading and transformation processes are executed in a timely and reliable manner.
• API Integrations: API integrations allow for the real-time pushing of data into the warehouse, or the requesting of data from the warehouse for use in other applications.

Data Monitoring Tools

Data monitoring tools (e.g. Datadog, Prometheus, Monte Carlo) track data quality, pipeline performance, and system health. Integration needs include:

• Data Sources: Integration with data sources is essential for monitoring data quality and detecting anomalies.
• Data Processing Tools: Integration with data processing tools allows for tracking pipeline performance and resource utilization.
• Orchestration Tools: Integration with orchestration tools enables monitoring pipeline execution and identifying potential bottlenecks.
• Alerting Systems: Integration with alerting systems (e.g. Slack, email) is crucial for notifying users of critical issues.

Data Orchestration Tools

Data orchestration tools (e.g. Apache Airflow, Prefect, Dagster) automate and manage complex data workflows. Integration needs include:

• Data Sources and Storage: Integration with various data sources and storage systems is essential for extracting, loading, and transforming data.
• Data Processing Tools: Integration with data processing tools allows for executing data transformations and analysis tasks.
• Monitoring and Alerting: Integration with monitoring and alerting systems ensures pipeline reliability and timely issue resolution.
• Cloud Services: Integration with cloud services (e.g. AWS, GCP, Azure) enables seamless deployment and management of data pipelines.

Data Processing Tools

Data processing tools (e.g. Apache Spark, Pandas, dbt) transform and enrich raw data into a usable format. Integration needs include:

• Data Sources: Integration with data sources is crucial for extracting raw data.
• Data Warehouses and Data Lakes: Integration with data warehouses and data lakes is essential for loading processed data.
• Orchestration Tools: Integration with orchestration tools allows for automating data processing tasks.
• API Integrations: API integrations allow for the enrichment of data with outside sources.

Common Data Pipeline Challenges for Startups

For startups, data pipelines often begin as simple scripts and then quickly evolve into mission-critical infrastructure. Startups face unique challenges when building data pipelines due to their rapid growth, limited resources, and evolving needs.

Scalability Issues

Startups often experience exponential data growth as they acquire more users and expand their operations. Pipelines designed for initial data volumes may quickly become overwhelmed, leading to performance bottlenecks and system failures. Predicting future data volume is difficult, so over-engineering is just as bad as under-engineering.

When building scalable pipelines some important elements to consider for your strategy include:

• Cloud-Native Architecture: Leverage cloud-based data services (e.g. AWS Glue, Google Cloud Dataflow, Azure Data Factory) that offer elastic scalability and pay-as-you-go pricing.
• Distributed Processing: Use distributed computing frameworks like Apache Spark to process large datasets in parallel.
• Horizontal Scaling: Design pipelines that can be horizontally scaled by adding more computing resources as needed.
• Microservices Architecture: Break down pipelines into smaller, independent microservices that can be scaled independently.
• Data Partitioning: Divide large datasets into smaller partitions to improve query performance and processing speed.
• Asynchronous Processing: Use message queues (e.g. Kafka, RabbitMQ) to decouple pipeline components and enable asynchronous processing.

When aligning your strategy to a budget, carefully monitor cloud service usage and optimize resource allocation to minimize costs vs. the cheaper price, but dramatically limited scale of 100% on-premises hosted data. Consider serverless computing options (e.g. AWS Lambda, Google Cloud Functions) for event-driven pipelines to reduce costs. And choose appropriate storage solutions (e.g. object storage, data warehouses) based on data access patterns and cost considerations.

Some common bottlenecks that teams come across include problems around data ingestion, transformation, storage, and orchestration. If there is inefficient data ingestion from various sources, consider implementing efficient data connectors, using message queues for streaming data, and optimizing data serialization formats. If you experience slow and inefficient data transformation processes, use distributed processing frameworks, optimize data transformation logic, and leverage cloud-based data transformation services. In the case of inadequate storage capacity or slow query performance, consider choosing scalable storage solutions, optimizing database indexes, and implementing data partitioning. If there is inefficient scheduling and management of pipeline tasks, use data orchestration tools like Airflow, Prefect, or Dagster to automate and manage pipeline workflows.

Technical Debt Management

Startups often prioritize rapid development over long-term maintainability, leading to technical debt in their data pipelines. This debt can manifest as complex and poorly documented code, inconsistent formats, and brittle data pipeline architecture.

Some strategies to consider to avoid technical debt include modular design. Design pipelines with modular components that can be easily modified and replaced. Using IaC tools, such as Terraform or CloudFormation to automate infrastructure provisioning and data management at scale. And also consider implementing automated unit and integration tests to ensure code quality and prevent regressions.

Documentation and knowledge transfer practices should also be a part of your strategy. This includes both comprehensive documentation as well as comments. It's important to document pipeline architecture, data flow, data transformations, and code. And it's just as important to include clear and concise comments that explain functionality.

Keeping information in a central location (e.g. Confluence, Notion), and making a proactive effort to share is also important. Create thorough onboarding processes for new team members to familiarize them with the pipeline. Conduct regular knowledge sharing sessions to ensure that all team members understand the pipeline. And some teams are even using tools that automatically generate documentation from code.

By addressing these challenges proactively, startups can build robust and scalable data pipelines that support their growth and enable them to extract valuable insights from their data.

Cost Optimization

Resource Planning

Cost optimization is a critical consideration when designing and managing data pipelines, especially for startups and organizations with budget constraints.

In some cases this means choosing between server less and dedicated infrastructure. Serverless provides a pay-as-you-go pricing structure that auto-scales and generally reduces operational overhead. Some of the downsides include potential cold start delays, vendor lock-in, and less control over infrastructure. Common use cases for server less include event-driven pipelines, variable workloads, prototyping.

On the other hand, dedicated infrastructure provides predictable costs, greater control over infrastructure, and consistent performance. Some of the downsides include higher upfront costs, manual scaling, increased operational overhead. Ideal use cases for dedicated infrastructure include predictable and consistent workloads, performance-critical pipelines, compliance requirements.

Some teams use a hybrid approach, combining serverless and dedicated infrastructure to optimize costs and performance. For example, use serverless for event-driven tasks and dedicated instances for resource-intensive data transformations.

When handling variable workloads, options include:

• Auto-Scaling: Implement auto-scaling for compute and storage resources to adapt to fluctuating workloads.
• Serverless Computing: Leverage serverless functions or container services that scale automatically based on demand.
• Queuing Systems: Use message queues (e.g. Kafka, RabbitMQ) to decouple pipeline components and handle spikes in data volume.
• Scheduling: Schedule pipeline tasks during off-peak hours to reduce costs.
• Elastic Data Warehouses: Utilize data warehouses that scale compute and storage independently.

Build vs. Buy Decision Framework

As you build your strategy, there may be elements that you consider building, whereas others you consider buying. It's important to consider the total cost of ownership, features and functionality (does the tool meet your requirements?), scalability and performance, integration (can it seamlessly integrate with your existing systems?), maintenance and support, time to market, and whether anyone on your team has some level of expertise to build and maintain.

While commercial solutions come at a literal cost, including license fees, subscription costs, support fees, integration costs--development costs can amount to more. Custom builds include development costs, infrastructure costs, maintenance costs, personnel costs. And don't forget to factor in long term costs, not just the initial start up investment.

When calculating the ROI for building vs buying, some important metrics to consider include:

• Cost Savings: Calculate the cost savings achieved by automating data workflows and improving data quality.
• Revenue Growth: Measure the increase in revenue generated by improved data-driven decision-making.
• Time Savings: Quantify the time saved by data analysts and engineers due to automated data pipelines.
• Improved Efficiency: Measure the increase in operational efficiency resulting from streamlined data workflows.
• Reduced Risk: Calculate the reduction in risk associated with data errors and inconsistencies.
• Qualitative benefits: Improved customer satisfaction, and better business insights. Use metrics that are relevant to your business goals.

By carefully considering these factors, organizations can optimize their data pipeline costs and maximize their return on investment.

Data Pipelines in 2025

Data pipelines are absolutely essential in today's data-driven world. They automate the process of extracting data from various sources, transforming it into a usable format, and loading it into a destination for analysis. This is fundamental for any data-driven operation. They ensure that data is clean, consistent, and reliable, which is crucial for accurate analysis and decision-making.

By automating data workflows, pipelines save time and resources, reducing the need for manual data handling. This allows organizations to focus on analyzing data and deriving insights, rather than spending time on data preparation.

Data pipelines can handle large volumes of data, making them essential for organizations that need to process and analyze massive datasets. They can be scaled up or down as needed, ensuring that organizations can adapt to changing data needs.

Furthermore, data pipelines are the foundation upon which successful AI initiatives are built. They provide the necessary infrastructure to ensure that AI models have access to the high-quality data they need to perform effectively.

More Resources:

• Article: The Data Pipeline is the New Secret Sauce
• Article: AI Inference: A Guide for Founders and Developers
• Article: Data Privacy, Security, and Identity in the Age of GenAI