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Data pipelines 101 for software development

Read Time 7 mins | Written by: Cole

Data pipelines 101 for software development

Data is one of your organization’s most valuable assets – it powers everything from new product delivery to business intelligence. That is, if your data is connected, stored, and maintained properly. 

Data pipelines make sure you can actually use your data. They’re responsible for transforming raw data into actionable insights, powering new software development, and driving informed decision-making.

Here’s a comprehensive look at data pipelines.

We’ll quickly cover key components, benefits, challenges, and best practices – along with how they can speed up software development.

What is a data pipeline?

A data pipeline is like a system of interconnected pipes that transport water from one place to another. In the world of data, it's a series of steps or processes that collect, transform, and move data from its source to a destination where it can be analyzed or used.

Data pipelines enable organizations to efficiently manage and use volumes of data from various sources. Your pipelines make sure the data is accurate, consistent, and readily available for all your business processes.

How data pipelines speed up software development

Data pipelines streamline and automate data processing tasks – which significantly speeds up software development. By providing clean and reliable data, your pipelines reduce the time developers spend on data wrangling and troubleshooting. 

Automated data pipeline processing makes sure that data is consistently prepared and delivered to development environments. That empowers your developers to focus on coding and feature development rather than data management. 

Data pipelines also facilitate continuous integration and continuous delivery (CI/CD) processes. They automate data-related tasks and update development and testing environments with current data. 

This leads to faster development cycles, quicker iterations, and ultimately, faster time-to-market for software products.

  • Automation - Reduces manual data handling, allowing developers to focus on coding.
  • Consistency - Makes sure development environments have consistent, up-to-date data.
  • Integration - Facilitates CI/CD processes by automating data tasks.
  • Efficiency - Speeds up development cycles and reduces time-to-market.

Key components of data pipelines

Data pipelines consist of several key components that work together to transfer, process, and store data. These components include data sources, data ingestion, data processing (batch and stream), data storage, data transformation, data orchestration, and data quality controls.

Each component plays a crucial role in ensuring the smooth and efficient operation of the data pipeline, enabling organizations to derive valuable insights from their data.

  1. Data sources - The origins of the data – e.g. databases, APIs, or external data providers.
  2. Data ingestion - The process of importing and transferring data from various sources into the pipeline. Common tools include: Apache Kafka, Apache Flume, and AWS Kinesis.
  3. Data processing
    • Batch processing - Handles large volumes of data in chunks at scheduled times (e.g., Apache Hadoop, Apache Spark).
    • Stream processing - Handles data in real-time as it arrives (e.g., Apache Storm, Apache Flink).
  4. Data storage - Stores the processed data in databases, data warehouses, or data lakes (e.g., Amazon S3, Google BigQuery, Snowflake, Databricks).
  5. Data transformation - Converts raw data into a more useful format (ETL/ELT processes). Common tools include Talend, Informatica, and Apache Nifi.
  6. Data orchestration - Manages and schedules the workflow of data pipelines (e.g., Apache Airflow, Prefect).
  7. Data quality and governance - Enforces data accuracy, consistency, and compliance with regulations. Useful tools include Great Expectations and Collibra.

Benefits of data pipelines

Data pipelines come with many benefits to enterprises – including scalability, automation, real-time analytics, and data integration. They enable your organization to handle large volumes of data efficiently, reduce manual intervention and errors, provide immediate insights for decision-making, and combine data from multiple sources for comprehensive analysis. 

By leveraging data pipelines, you can enhance your data management capabilities and drive better business outcomes.

  • Scalability - Efficiently handle large volumes of data.
  • Automation - Reduce manual intervention and errors.
  • Real-time analytics - Provide immediate insights for decision-making.
  • Data integration - Combine data from multiple sources for comprehensive analysis.

Data pipeline challenges

Building and maintaining data pipelines can be complex and comes with many challenges. These include the complexity of the pipeline architecture, ensuring data quality, and protecting sensitive data. 

Your DataOps team needs to address these challenges to guarantee the reliability and security of data pipelines.

  • Complexity of development - Building and maintaining data pipelines is complex and requires specialized skills.
  • Data quality - Ensure the accuracy and consistency of data across various sources.
  • Security - Protecting sensitive data throughout the pipeline.
  • Data lineage - Understanding where data comes from, how it has been transformed, and where it is going is critical for debugging, compliance, and trust in the data.
  • Data provenance - Keeping track of the origin of data sets helps in maintaining data quality and integrity.
  • Metadata tracking - Keeping track of metadata, which includes information about the data’s content, context, and structure, enhances data discoverability and usability.
  • Pipeline orchestration - Efficiently orchestrating and managing the various components and workflows of the data pipeline makes for smooth operation and timely data delivery.
  • Failure recovery - Implementing robust failure recovery mechanisms – like retry logic, checkpointing, and data backups, makes sure that the pipeline can recover quickly from errors.
  • Automated testing - Incorporating automated tests for data pipelines helps catch errors early and uphold the reliability of the pipeline.

Best practices for building data pipelines

To maximize the efficiency and reliability of company data, your org should follow data pipeline best practices. That includes designing modular pipelines, implementing robust monitoring and alerting systems, maintaining thorough documentation, and applying strong security measures. 

These practices help simplify maintenance and scalability, detect and address issues promptly, provide clear understanding and management of the pipeline, and safeguard data throughout the process.

  • Modular design - Design pipelines in modular components to simplify maintenance and scalability.
  • Monitoring and alerting - Implement robust monitoring to detect and address issues promptly.
  • Documentation - Maintain thorough documentation to help understand and manage the pipeline.
  • Security measures - Implement strong encryption and access controls to safeguard data.

Data pipeline tools

Various tools and technologies support different aspects of data pipelines – from ingestion and processing to storage, orchestration, and transformation. 

Selecting the right tools and technologies is crucial for building efficient and effective data pipelines.

  • Ingestion - Apache Kafka, AWS Kinesis, Google Pub/Sub
  • Processing - Apache Spark, Apache Flink, AWS Lambda
  • Storage - Amazon S3, Google BigQuery, Snowflake, Databricks
  • Orchestration - Apache Airflow, Prefect, Dagster
  • Transformation - dbt (data build tool), Talend, Informatica

How do I hire a team to build data pipelines?

More and more enterprises and venture-funded companies hire modern application development (MAD) service providers to build their data pipelines. That’s why Codingscape exists.

No need to wait 6-18 months before you start building your DataOps initiatives. We can assemble a senior data pipeline team in 4-6 weeks.

Zappos, Twilio, and Veho are just a few companies that trust us to build their software and systems with a remote-first approach.

You can schedule a time to talk with us here. No hassle, no expectations, just answers.

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Cole is Codingscape's Content Marketing Strategist & Copywriter.