What is a Data Pipeline? Types & Benefits in 2024

In today‘s data-driven world, businesses rely on insights extracted from large volumes of data to guide strategic decisions and operations. However, making sense of disconnected, messy data from disparate sources is a major challenge. This is where data pipelines come in – to structure and streamline the flow of data to enable actionable analysis.

In this comprehensive guide, we will explore what data pipelines are, their key components, different types, how they differ from ETL, and the major benefits of implementing data pipelines in 2024.

What is a Data Pipeline?

A data pipeline is the process of moving data from a source to a destination for storage and analysis. It entails sequenced data processing steps that transform raw data into meaningful, analytics-ready information.

Data pipeline process

As depicted in the image above, a data pipeline consists of three core components:

  • Source – Where the data originates e.g. databases, IoT devices, social media APIs, files etc.

  • Processing Steps – The data extracted from sources undergoes a series of transformations to convert it into standardized, analysis-ready formats. Common steps include validation, cleansing, joining data from multiple sources, aggregation, integration, and enrichment.

  • Destination – The final endpoints where processed, refined data is stored. This is usually a data warehouse or data lake where analytics and BI tools can access it.

So in summary, a data pipeline moves data from source systems, processes it, and loads it into destination repositories in a systematic sequence. The processing steps help address raw data quality issues and prepare it for downstream analytics and machine learning models.

Key Data Processing Steps

Let‘s look at some of the key processing steps in more detail:

Data Validation

This involves checking the extracted data to ensure it conforms to expected formats, constraints, data types etc. For instance, validating date fields contain valid dates, geographic coordinates are within range, string lengths are not too long etc. This catches any major data issues early.

Data Cleansing

Raw data from sources often contains incomplete, incorrect, duplicated or irrelevant values. Data cleansing tasks like filling in missing values by lookups or imputation, deduplication, pruning unnecessary columns, etc. help improve data quality.

Data Transformation

Business users may need data formatted in specific ways for their use cases. Tasks like parsing text into columns, aggregating values into KPIs, joining data from multiple sources, encoding categorical values etc. reshape data for downstream needs.

Data Enrichment

Augmenting the source data by merging in supplemental datasets, appending external IDs, adding computed attributes etc. can provide deeper insights. For example, adding customer demographic data from a third-party source to transactional data.

Data Integration

Structuring and aligning data from different sources into a cohesive whole is key. Using standardized schemas, consistent entity matching and mapping Data from multiple sources to provide an integrated view.

With the right orchestration and transformations, disparate datasets can be blended into valuable integrated data assets.


Grouping and summarizing large granular datasets along dimensions like time period, region, product etc. generates aggregated views. Useful for rollups, reporting and high-level analytics.

These steps shape raw extracted data into the refined, trustworthy datasets needed by analysts, data scientists and decision-makers.

Types of Data Pipelines

There are two primary architectural styles for constructing data pipelines:

Batch Processing Pipelines

In batch data pipelines, data is collected from sources and transferred to destinations at regular intervals in batches – hourly, daily, weekly etc.

Processing doesn‘t happen in real-time. The batch is accumulated over the time interval and then processed together before loading to destination.

Batch pipelines are ideal for moving high volumes of data efficiently. They also provide flexibility to handle complex transformations that require aggregating data over time e.g. weekly sales totals.

Batch data pipeline

For instance, an e-commerce company may have a daily batch pipeline that:

  • Extracts user activity event data, product catalog updates, new orders etc. generated that day

  • Cleanses, processes and transforms the data

  • Loads the refined dataset into a data warehouse at the end of the day

This batched approach works well for analytical use cases that don‘t need super low latency.

Stream Processing Pipelines

In stream processing pipelines, data is processed continuously as soon as it arrives from sources. There is no batching interval – the transformations and output happen in real-time.

Streaming pipelines allow low latency data analysis since new data is immediately available at the destination. This enables real-time dashboards, alerts, and dynamic decision making.

They are ideal for rapidly evolving data from sources like user activity events, IoT sensor data, clickstreams etc. Streaming is preferred when fresh data insights are critical.

Stream processing pipeline

For example, a fintech firm may use a streaming pipeline to:

  • Analyze transactions in real-time to detect fraudulent activity
  • Trigger alerts when anomalies are found or thresholds breached
  • Take corrective actions like blocking transactions dynamically

Streaming pipelines enable innovating valuable, timely services and features that leverage instant data.

Hybrid Pipelines

Hybrid pipelines combine attributes of batch and streaming to balance throughput, latency and complexity. An example is micro-batching which processes incoming data in small, frequent batches instead of a single large batch.

Other examples include:

  • Using batch and stream processing in different stages of the pipeline
  • Having separate stream and batch pipelines with a common destination
  • Starting with batch pipelines and adding streaming capacity later

The right mix of batch and streaming depends on the data volumes, analysis needs and infrastructure. A pragmatic approach is best for most real-world systems.

Data Pipelines vs ETL Pipelines

Extract, Transform and Load (ETL) workflows are an essential part of data pipelines. However, ETL refers to just one subset of the data pipeline process.

  • Extract – Ingesting data from different sources
  • Transform – Cleaning, validating, enriching, reshaping the extracted data
  • Load – Loading the transformed data into target databases such as data warehouses, data lakes etc.

So in summary:

  • ETL is focused on the extract, transform and load steps that prepare data for consumption
  • Data pipeline is the end to end process that includes data extraction, processing, and storage to destination.

ETL is a key component of data pipelines. But data pipelines also encompass broader data flows, integration of different sources and destinations, orchestration, monitoring and other data management functions beyond just ETL.

ETL vs Data Pipeline

As shown in the diagram, ETL is concerned with the mechanics of getting data into the warehouse or lake, while the data pipeline deals with end-to-end data flow and processing.

Benefits of Data Pipelines

Implementing well-architected data pipelines provides the following business benefits:

Centralized Data

Data pipelines consolidate data from disparate sources like databases, SaaS apps, APIs etc. into a centralized data repository like a data warehouse or data lake.

This provides:

  • Single source of truth – Consistent data for reporting and analytics instead of fragmented data stores. Reduces mismatches and conflicts.

  • Improved collaboration – Different teams can work with the same data assets instead of their own copies. Promotes transparency and alignment.

According to a 2020 Dataiku survey, 96% of organizations reported their data pipelines and processes enabled better collaboration between teams.

Data pipeline centralizing data

Standardized Data

The processing steps in a data pipeline cleanse, validate and transform raw data from sources into standardized schemas. This makes data consistent, complete and accurate for downstream analytics.

Steps like data profiling, parsing, standardization, deduplication etc. help shape quality data for business users.

According to an Experian survey, poor data costs organizations an average of $15 million annually. Implementing data standardization and quality best practices within pipelines is crucial.

Real-time Analytics

For streaming pipelines, data becomes available for analysis right away as its generated without any delays. This enables:

  • Faster insights – Quickly analyze emerging trends and patterns
  • Dynamic dashboards – Power real-time reports and visualizations
  • Rapid decision making – Take timely actions based on latest data

A Forrester study found real-time data usage directly correlates with improved customer experiences and business performance. Real-time data pipeline capabilities will be a key competitive differentiator going forward.


With modern data platforms, it’s easier to modify pipelines to incorporate new sources, transformations, destinations etc. New data can be quickly integrated and leveraged without engineering overhead.

This makes businesses more adaptable to changing data needs. Users can readily experiment with new data instead of waiting on IT delivery queues.

According to Research and Markets, increased enterprise agility is a key driver expected to fuel 14% CAGR growth in data pipelines over 2020-2025.

Maintainable and Reusable

Following best practices like dividing pipelines into modular stages, using orchestration, configuration over coding etc. improves maintainability. Changes can be isolated and tested incrementally instead of rewriting entire pipelines.

Logic can also be reused across pipelines, avoiding redundant work. similarly, common libraries for tasks like parsing, cleansing can be shared for consistency.

These development approaches reduce overall lifecycle costs associated with building and operating pipelines at scale.

In summary, well-designed data pipelines are fundamental for extracting value from data. They provide the plumbing for powering analytics and data-driven processes in modern digital businesses. Investing in robust data pipelines yields better quality analytics at reduced costs.

To learn more about optimizing your business‘s data and analytics capabilities through managed services, check out our guide on selecting the best analytics consulting firms.

You can also connect with top data analytics experts specialized in designing and implementing data pipelines tailored to your business needs.