Everything You Need to Know About Data Pipelines

Wondering what data pipelines are and how you can use them to unlock value from growing data volumes? As an innovator relying on analytics to outpace competitors, implementing the right data infrastructure is key.

This comprehensive guide walks through all aspects of data pipelines so you can evaluate if they are right for your organization. I’ll explain in simple terms what pipelines do, their architectures, use cases, tools and steps for getting started.

What Are Data Pipelines?

Data pipelines move data from source systems to destinations like data warehouses and lakes for storage and analysis. They facilitate steps in between to validate, transform and enrich the data as needed.

Here‘s a simple data pipeline diagram showing the flow:

Simple Data Pipeline

Adoption of pipelines is exploding as data volumes and sources increase across industries:

Year Spending Growth
2022 $3.4 billion 15.7%
2023 $4.1 billion 21.3%
2024 $5.0 billion 22.1%

(Source: IDC)

Half of organizations now process over 1 TB of data daily (Source: Dresner). Without automated pipelines, unlocking value and insights from this data is impossible.

So in plain language – data pipelines move data from point A to point B while improving quality to ready it for analysis and use.

Key Benefits of Data Pipelines

Here’s why forward-thinking companies are investing in data pipelines:

1. Centralization – Consolidate data from all systems into a warehouse or lake for access

2. Automation – Eliminate error-prone manual data tasks with code

3. Transformation – Shape and enrich along the way for analysis

4. Quality – Validate, deduplicate, fix issues

5. Scale – Built to handle increasing data as business grows

Simply put, pipes solve roadblocks restricting how data can create value.

Common Data Pipeline Use Cases

Data pipelines support an expanding set of use cases across functions:

Building Analytics Dashboards & Reports

Pipelines allow easily connecting business intelligence tools to data for historical reports and real time dashboards.

Populating Data Warehouses & Lakes

Ingesting from enterprise systems or external sources into cloud data stores.

Supplying Data to ML Models

Quality, trustworthy data at scale trains more accurate machine learning.

Synchronizing Core Business Systems

Keeping platforms like CRM, ERP or financial systems in sync via automated data exchange.

Internet of Things (IoT Data Consolidation)

Ingesting and processing huge volumes of sensor data.

With the right pipelines, data can provide critical insights powering strategic initiatives.

Data Pipeline Architectures

There are two primary types of data pipeline architectures:

ETL Pipelines

ETL (Extract, Transform, Load) tackles data tasks in discrete steps:

  1. Extract from sources
  2. Transform by cleaning or enriching
  3. Load the finished data into destinations

Think assembly line, with each phase adding value.


  • Structure works for simple transformations
  • Separated concerns


  • Inflexible later in pipeline lifecycle
  • Changes require updates across phases

ELT Pipelines

ELT (Extract, Load, Transform) flips the stages:

  1. Extract raw data
  2. Load into target system
  3. Transform as needed by use case

ELT keeps data unchanged and flexible for different end needs.


  • Adaptive to new demands
  • Raw data available


  • Requires more advanced processing power
  • Transformations rerun for each use

There are valid applications for both ETL and ELT pipelines. Choose based on factors like transform complexity, target flexibility and cost.

Hybrid Data Pipelines

Combining ETL and ELT, hybrid pipelines land between the two architectures. An example is performing light preprocessing transformations early, then loading raw data also for flexibility. This balances transform reuse and multifaceted analytical needs.

Batch vs Stream Data Processing

Beyond ETL vs ELT architecture, data pipelines break down by run frequency:

Batch Processing

Batch pipelines collect source data over time intervals (hourly, daily etc) then process it in groups.

  • Simple to build and manage
  • Efficient workloads

Use Cases: overnight jobs, end of week/month reporting

Stream Processing

Streaming (or real-time) pipelines process immediately as data arrives. No batching intervals.

  • Sub-second latency
  • Always current

Use Cases: personalization, recommendations, alerting

Choose batch, streaming or a mix based on business requirements. Streaming has gained popularity providing live data-in-motion to drive instant decisions.

Critical Capabilities of Enterprise Data Pipelines

Beyond the basics of moving data, robust pipelines should provide:

Cloud scale

Ability to handle exponentially growing data needs

Metadata management

Catalog business meaning and lineages for governance


Retry, dedicate errors, lineage context


Observability into throughput, uptime, usage


Encryption, access controls and data masking


Coordination of complex systems


Any business logic required while in motion

API expandability

Access for adding custom functionality

Think through these areas when evaluating tools.

Leading Data Pipeline Tools and Platforms

The pipeline marketplace provides solutions across needs, architectures and price points:


The long-time data integration leader with end-to-end capabilities

AWS Data Pipeline

Fully managed ETL service natively using other AWS data systems

Azure Data Factory

Microsoft’s integration platform for multi-cloud pipelines


Modern SaaS focusing on pre-build warehouse connectors


Specializes in real-time, streaming data pipeline creation

I suggest creating an RFI checklist including requirements across integration needs, security, support SLAs, skill sets and toolchain interoperability.

Most vendors provide trial access to POC tools hands-on before committing.

How Much Do Data Pipelines Cost?

With cloud elasticity, pipeline costs scale directly with usage. Here are pricing levers:

1. Amount of data – $ per GB passing through

2. Compute used – Transformation processing power

3. Number of pipelines – Base costs per pipeline

Also factor one-time implementation services or FTE technical skills needed.

Consider starting costs against painful status quo expenses like manual engineering. Includes opportunity cost of delayed insights!

Steps to Implement a Data Pipeline

Though tools exist to simplify, core steps are required for any data pipeline:

1. Identify Triggers

Events activating pipeline execution

2. Map Source Data

Understand structures and metrics

3. Model Business Logic

Joins, aggregates, transforms

4. Choose Destination

Warehouse, lake etc.

5. Extract & Validate

Pull safely, confirm schema

6. Transform

Shape, enrich per logic above

7. Load & Test

Insert securely and check accuracy

8. Monitor & Support

Optimize usage, avoid failures

Embed knowledge of the full business process into each pipeline to maximize value.

Data Pipeline Security Best Practices

With data traversed and stored across systems, pipelines introduce potential risk requiring mitigation:

  • Encrypt data end-to-end

  • Limit data access authorize users

  • Follow least privilege principles

  • Mask sensitive elements like PII

  • Validate integrity checking for tampering

  • Monitor pipelines with anomaly detection

">Document security processes around pipeline DevOps, access controls and edge protection. Instruments safeguards early in design.

Real World Data Pipeline Success Stories

Data pipelines provide competitive advantage across industries:

Ecommerce – Fashion retailer stitches Shopify, shipping, marketing data into Snowflake to optimize promotions and predict inventory needs by location.

Logistics – Global parcel company centralized operational data into a Azure Synapse Analytics, building a batch pipeline from IoT sensors, device logs and fulfillment systems. This powers predictive maintenance across planes and trucks, avoiding delays.

Banking – Digital bank extracts raw transactional data from core systems into a datalake. Compliant ML pipelines then process this to provide real-time personalized promotions to customers.

Energy – Smart meter pioneer uses Apache Kafka to ingest and distribute real-time power consumption data, enabling grid optimization and advanced consumer packages.

Maturing Data Pipeline Capabilities

As foundational infrastructure, pipelines capabilities continue rapid evolution:

  • Further democratization and self-service
  • Increasing embedded data quality
  • Expanding SaaS source/destination ecosystem
  • ML-augmentation like auto-mapping
  • Scaling to handle new data speeds and types

In Closing – Evaluating Your Data Pipeline Readiness

  1. Document your primary data pipeline motivation – which use cases provide value?

  2. Assess existing infrastructure gaps adding friction

  3. Explore leading tools aligned to fill gaps and speed time-to-insight

Today’s pace of business requires tapping data pipelines to enable decision automation based on trusted information. I hope this guide has demystified their function and path to value realization.

What resonated or sparked further questions? Feel free to email me directly to continue the conversation!