Data Pipelines Explained

Most organizations use data to their advantage when making high-impact business decisions. But have you ever considered how and where they get this data? 

That’s what the data pipeline does. In brief, it looks like this:

  • Data starts in various forms — sourced from data lakes, databases of different SaaS applications, or as streaming data. Importantly, this data is raw. It requires cleaning and transformation to be useful for business decisions.
  • Data pipelines process and clean data using ETL (Extract, Transform, Load), data replication, and data virtualization.
  • Once prepared, data is ready for analysis and business application.

This guide will take you through a detailed explanation of data pipelines. We’ll also look at the outlook and trends for data pipelines.  

What is a data pipeline?

Data pipeline is a comprehensive term. Calling something a “data pipeline” often includes a variety of processes involved in the flow of given data.

Let’s define it: a data pipeline is the way you move data from one or more places to a destination, while also transforming and optimizing the data for use. Or, more simply, the data pipeline is any mechanism for collecting, transforming and storing the data to support a variety of data projects.

Here's how the pipeline works — starting from a pile of data that originates from raw data sources, the data undergoes transformations to eventually become “data products” that can serve relevant use cases. The pipeline does this in distinct steps — each managed by heterogeneous systems.

Consider the data pipeline a generic term, and depending on your goals, types of data, and budget, you’ll use a variety of technologies and techniques within your pipeline.

(Data pipeline example: setting up a pipeline to detect fraud.)

Challenges with various data sources

Managing a data pipeline means reading and processing from diversified data sources. And all this work moving and processing data can easily become quite complex.

Here are some common challenges that occur with handling data from multiple sources:

More data = more surfaces

Due to the extensive "surface area" of these systems, there are more places where things can go wrong. Data coming in have different formats, structures, and types. Transforming this data to make it compatible with the destination system before integration is complicated and time-consuming.

Beyond this challenge, though, is a more important point: Processing this poor-quality data threatens the entire pipeline and can lead to faulty analytics and corrupt results downstream.

More systems & apps = more complexity

Our systems become more complex. Organizations rely on multiple interdependent components — maintained by different teams with varying skill levels and engineering expertise. Their approach leads to miscommunication and coordination problems when different capabilities are involved. This increases the chances of errors and inefficiencies in the data pipeline.

Defining the data pipeline architecture 

The pipeline enables efficient data movement and processing from its source to the analysis destination. Here's a detailed overview of how this architecture transforms poor data into highly optimized data end-products: 

Stage 1. Data collection and ingestion 

The pipeline begins with data collection from databases, IoT devices, and SaaS platforms. Since this data comes from different sources, its nature varies widely — it could be structured, like in traditional databases, semi-structured, such as JSON or XML files, or unstructured, like text files or multimedia content.

According to a report on automation in the workspace:

Over 40% of workers spend at least a quarter of their work week on manual data collection. And 70% believe that data collection is one of the most time-wasting tasks where automation opportunities should be considered. 

To address these problems, there's a growing trend of real-time data ingestion — an automated approach to process and prepare data immediately as it's received using software like Apache Kafka

(Know the differences: structured, semi- and unstructured data.)

Stage 2. Data transformation

Following data ingestion, data is cleaned by removing inaccuracies and filtering out irrelevant data points. Then, it's standardized to a consistent format and becomes easier to integrate and analyze. (More on this stage in the next section.)

Stage 3. Applying data in business

Once data is transformed into a usable format, the pipeline sends it to the final destination, ready for applications. The two main applications of data are:

  • Business intelligence: Data is used for insights in different areas like recruitment.
  • Machine learning and AI: Large language model algorithms are fed with high-quality data for smarter business decisions.

Depending on the size of your business, you may use a comprehensive data platform that enables this step.

Foundational pillars of a modern data platform includes versatility, intelligence, security and scalability

Data treatment processes used in the pipeline 

There are three different processes used for treating raw data in the pipeline: 

  • ETL (Extract, Transform, Load)
  • Data replication
  • Data virtualization

Extract, transform and load (ETL) process

ETL (Extract, Transform, and Load) processes extract data from diverse sources, such as databases, files, and APIs. This raw data is then transformed through operations like decoupling and denormalizing to become suitable for analytical purposes. Once transformed, this data is loaded into alternative storage systems or data warehouses for further use.

Beyond mere data manipulation, ETL provides contextual historical data tailored to specific applications. It consolidates data from multiple sources and simplifies data management. This process streamlines data processing tasks by automating them, saving valuable time and resources.

Within the ETL pipeline, data processing is done in two distinct manners: 

  • Batch processing allows data to be processed in scheduled batches.
  • Stream ingestion handles real-time data by continuously transforming and loading it as needed, enabling prompt data analysis and decision-making.

Data replication 

Data replication distributes data to users from a variety of repositories. At the center of this process lies a cloud data warehouse — an intermediary — collecting and providing access to data from these diverse sources.

While replicating data from relational databases seems simple, the complexity increases when dealing with custom-built or intricate enterprise applications, such as SAP. This is where replication tools are required: these have to be versatile enough to support various repositories.

The ongoing maintenance of a cloud data warehouse is not just about the initial data population; it also involves ensuring real-time updates. Technologies like Change Data Capture (CDC) help facilitate the continuous syncing of data.

Data transformations and cataloging become easy by centralizing data in the cloud data warehouse. And it enhances user access and interaction with the data — all while minimizing any impact on the performance of the source systems. 

(Manage data better: Learn about database management systems.)

Data virtualization 

Since data resides in sources like Oracle, DB2, Postgres, and MongoDB — across different platforms — including on-premises and the cloud, the data itself comes in diverse forms, including relational, non-relational, and NoSQL databases.

The challenge arises when organizations want to leverage these diverse data sources for analytics without physically moving the data. Data virtualization addresses this challenge by seamlessly connecting multiple sources into one unified location—enabling users to query them as a single entity. This approach reduces costs, simplifies data management, and enhances collaboration.

Data virtualization involves three layers: 

  1. Interacting with data.
  2. Optimizing queries and scalability. 
  3. Providing a user interface (UI).

Data virtualization has many benefits: it speeds up data exploration (query performance can improve by up to 8-10 times), saves costs, reduces data duplication, and enhances storage, recovery, and data management versatility.

Future outlook & data pipeline trends

Data pipelines are being used in many, many parts of your business, so let’s look at the outlook and what to expect.

Automation and AI-driven development in data pipelines 

Automation in data pipelines reduces manual errors and improves efficiency. A well-defined structure within data pipelines ensures a smooth and reliable data flow and lays the groundwork for efficient data management. This way, businesses can extract maximum value from their information reservoirs.

That’s why the acceptance rate of AI-driven development solutions in the data pipeline is predicted to grow substantially in the next few years. 

The graph shows the growing acceptance rates of ML and Data Analytics solutions. The global data pipeline market size is projected to grow from $8.22 billion in 2023 to $33.87 billion by 2030, at a CAGR of 22.4% during the forecast period.

Data pipeline automation now employs more intelligent, efficient, and flexible systems, streamlining data processing workflows and broadening data utilization and management possibilities. It has evolved through three eras: 

  • In the first era, data was directly dumped into warehouses and queried, resulting in high resource and processing costs.
  • The second era introduced materialized views to cut costs, reducing the need for querying raw data but still carrying high rebuilding costs for each data change.
  • The current third era focuses on incremental processing — where only new or changed data is processed—significantly increasing efficiency and reducing operational costs. 

Indeed, its this current era that leverages intelligent controllers in data pipelines, which understand data flows and relationships between datasets and their processing code. These controllers bring several benefits:

  • Save time and cost by detecting and skipping unnecessary data processing steps.
  • Avoid reprocessing inefficiencies by reprocessing only relevant dataset parts when new data arrives.
  • Maintain data integrity and simplify data management by pausing and resuming pipelines at any point.
  • Enable more detailed analysis with real-time updates or selective pausing of pipelines.
  • Identify opportunities to reuse data in different pipelines, reducing redundant processing and storage.

In this era, the distinction between batch and streaming data processing is blurring due to the rise of micro-batch processing. Micro-batch processing processes small data batches quickly — bridging the gap between traditional batch processing and real-time streaming.

(Our prediction? Data analyst and data engineer roles will only become more important.)

Setting up an efficient data pipeline 

Another trend is the concept of data pipeline efficiency and optimization. Obviously, with the issues we’ve discussed here, a pipeline can have many areas of inefficiency.

Here's a guideline to help you establish a great data pipeline. Use this as a checklist whenever you’re starting a new data pipeline.

Define your goals and requirements. Start by clearly identifying the data type (structured, unstructured, streaming, batch) and the frequency of data updates. This sets the foundation for the pipeline's design and functionality.

Choose your tools and technologies. Select appropriate data storage (e.g., databases, data lakes) and processing tools (e.g., Apache Spark, Apache Flink). 

Design the architecture. Plan for a scalable, fault-tolerant architecture that efficiently handles data flow and accommodates increasing data volumes.

Ingest & integrate the data. Establish mechanisms to ingest and integrate data from various sources, ensuring correct formatting and data quality through validation processes. You can use tools like Apache Kafka and AWS Kinesis.

Process and transform the data. Implement your preferred data treatment processes to prepare data, focusing on automating transformation tasks like filtering and aggregation.

Store & manage the data. Opt for scalable storage solutions, such as cloud-based services, and manage data lifecycle effectively with partitioning and indexing strategies.

Monitor and maintain the pipeline. Regularly monitor the pipeline for performance and data integrity and optimize based on evolving requirements.

Testing and deployment. Conduct thorough testing before deployment, adhere to continuous integration and deployment practices, and have a rollback strategy.

Iterate to improve continuously. Regularly review and update the pipeline, incorporating feedback and adapting to new requirements to maintain effectiveness.

More data, more pipelines

Simply put, data pipelines help businesses collect, process, and transform raw data from diverse sources into actionable insights. They do this via methods like ETL (Extract, Transform, Load), data replication, and data virtualization — which convert heterogeneous data into a format suitable for analysis and decision-making.

These pipelines can improve overall business intelligence, driving data-driven strategies and adapting to future trends in automation and AI. 

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This posting does not necessarily represent Splunk's position, strategies or opinion.

Laiba Siddiqui
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Laiba Siddiqui

Laiba Siddiqui is a technical writer who specializes in writing for SaaS companies. You can connect with her on LinkedIn and at contentbylaibams@gmail.com.