Data-Management

A data pipeline is the backbone of the digital world. Therefore, building a data pipeline is essential for any business that wants to make data-driven decisions. The term "data pipeline" is also popular terminology for consolidating and managing consistent data quality.

The importance of data pipelines cannot be overstated. Without them, data transmission would depend on manual processes full of human errors. This can become expensive, inefficient, and can negatively impact a business' success.

Now let's understand what a data pipeline is, its components, and how they work together to provide insights into data.

What Is a Data Pipeline?

Data pipelines enable moving data from point A to point B using an automated data pipeline tool. Between points A and B, you might get points like C, D, and E, which are the data pipelines. Data pipelines allow companies to transfer information from one system to another and send it to other teams, such as data analysts using automated data pipeline tools.

It's important to note that data produced from a single data source might feed multiple data pipelines. This is because data pipelines are designed using various tools, depending on the needs of the business.

data pipeline

A Data Pipeline Example?

A bookstore is an excellent example of a data pipeline.

The library systems in a bookstore maintain a database of books that are available for purchase by customers. The database is held in a relational database management system (RDBMS). The RDBMS provides an interface to query and update data from the database, allowing the bookstore to manage its inventory and customer purchases efficiently.

The RDBMS manages the data in a way that makes it easy for different applications within the business to access it. For example, applications such as Salesforce or QuickBooks can access the data to generate sales or other reports that you may use to make informed decisions.

what is rdbms

What Is a Big Data Pipeline?

A big data pipeline is a system for processing large data sets. As organizations increasingly rely more on data to guide their decisions, the need for efficient and reliable big data pipelines has never been greater.

When data volume, variety, and velocity increase, a big data pipeline that handles multiple data events concurrently is necessary. A big data pipeline should typically process data in semi-structured, structured, and unstructured formats.

Big data pipeline allows data extraction from any source, high volume data transformation, and stores the data in various repositories. For example, big data pipeline sources may include log files, IoT (Internet of Things) devices, messaging systems, and databases.

The features of big data pipelines provide secure real-time data analytics and data processing, automated self-service management, and scalable cloud-based architecture.

Data Pipeline vs. ETL

There is a great misconception surrounding the terms ETL (Extract, Transform, Load) and data pipeline. Unfortunately, people often use the terms interchangeably.

ETL process

Is ETL the Same as a Data Pipeline?

ETL is not the same as a data pipeline. ETL stands for Extract, Transform, Load. It specifically refers to a type of data pipeline (sub-process) that extracts raw data, transforms, and loads in data warehouses, data lakes, or data marts.

ETL is most common in batch workloads, while data pipelines ingest in real-time. Data loading to a destination is the ETL's last step. In data pipelines, the process doesn't stop after data loading; instead, it can act as the source data for other processes.

It can be extracted from one or more sources and loaded into one or more data warehouses or data lakes. In short, an ETL is a three-step process, unlike in data pipelines, which follows various steps before storing the data.

Data Pipeline Types

Different data pipelines are well-suited for different data types, depending on organizational needs. Their classification depends on how often the pipeline executes commands. Here are the most common data pipeline types:

Batch

Organizations typically use it for batch data pipelines for ETL purposes, where execution is manually or recurring. These pipelines are ideal for processing large amounts of data that don't need real-time processing. This means they extract all the data from a source, transform it, and load it into a data warehouse.

batch processing

Real-Time

These pipelines are used for streaming data applications. They can process data as it comes in, which is essential for applications that need to react to events in real-time. These data pipelines enable you to handle a million events.The advantage of using a real-time pipeline is that it allows you to see changes immediately and decide based on that information.

real time processing

Open-Source

Open-source data pipelines are most common in on-premise environments. They give organizations more control over their data since they are easily customizable. You can inspect your code and tailor it to fit your specific use case. The good thing about open-source pipelines is lower upfront costs. However, you'll need the expertise to operate the system or have data engineers.

Cloud

These pipelines are becoming more popular as organizations move to the cloud. Cloud data pipelines are hosted on a cloud platform and can be scaled up or down as needed. They use cloud-based data and rely on the provider's hosting technology.

cloud processing

Data Pipeline Components

These are the building blocks that make up your data pipeline. A modern data pipeline comprises of the following components:

  • Data source: A source may include internal databases, external databases, or cloud platforms. Data pipelines extract data using a push mechanism, an API call, or any other method an organization chooses.
  • Processing steps: These are steps in the analysis process where the data is processed further and analyzed using a set of rules and decision criteria to produce a report or chart (e.g., filtering out invalid records).
  • Dataflow: This shows data flow from point A to point B. This includes the operations and the data stores it goes through.
  • Workflow: The sequence of operations and data dependency management through the data pipeline.
  • Storage: Refers to a place where data is held until there are available tools to process it and take it to the next stage.
  • Monitoring: Helps ensure maximum performance of all processes in a data pipeline. Monitoring helps detect and diagnose bottlenecks that may affect its efficiency.
  • Destination: A repository for the final output of the analysis process. From here, the information can be sent to a client or shared with colleagues through an internal portal or app.
  • Technology: Infrastructure and tools enable data flow from origin to destination.

Data Pipeline Architecture

Data pipeline architecture describes the structure of items that extract, control, and send data to the appropriate system to gain insightful information. Here are examples of data pipeline architectures:

ETL Data Architecture

The most common type is the ETL architecture. ETL pipelines are used to move data from one database to another. They typically involve extracting data from a source database, transforming it, and loading it into a destination database.

ELT Data Pipeline

An ELT Data Pipeline architecture is a data processing architecture designed to handle extract, load, and transform (ELT) operations. ELT is a data processing method typically used to migrate data from one system to another.

The ELT data pipeline architecture is designed to extract data from a source system, load it into a target system, and then transform it into the desired format.

The ELT data pipeline architecture is scalable, fault-tolerant, and highly available for data processing. It's designed to handle large volumes of data and can be used to process data in real-time. In addition, the ELT data pipeline architecture can take data from multiple data sources and process data in different formats.

Batch Pipeline

A batch processing typically performs traditional analytics. The process begins with an execution plan that specifies the steps needed to run each query in the pipeline. Then, each query is executed against the database using SQL. After completing all the queries, an aggregate report is created and sent back to the end user for viewing.

Streaming Data Pipeline

The streaming data pipeline for real-time analytics is a powerful tool that can be used to analyze large amounts of data in real-time.

It uses the power of Hadoop and other open source technologies to process massive data payments quickly and make them available for analysis. You can use the information to analyze customer behavior, detect fraud, and improve customer satisfaction.

Big Data Pipeline

A big data pipeline is a collection of large amounts of information that can be difficult to process using the standard data pipeline. The common way to deal with it is by using distributed computing. This allows the data to be processed in real-time and stored in a distributed manner, which makes it easier for data scientists to access and analyze.

Data Pipeline Case Uses

  • Banks can use data pipelines to help them in the data integration from multiple sources to get business insights to gain a competitive advantage.
  • Companies can collect data from organizational data silos to enhance productivity.
  • Use the clinical data pipeline in the healthcare industry to access electronic health records for patients to be analyzed by different doctors.
  • Online stores can use the eCommerce data pipeline to analyze and use it for sales forecasting.

lambda archquitecture

Conclusion

Modern data pipelines enable organizations to gain real-time insights that help make excellent decisions. There are different data pipeline types and architectures to choose from. This is why it is crucial to choose one that fits your organization's needs. A data pipeline helps centralize your data so you can use it to enhance the consumer experience, increasing the company's productivity and revenue margins. Data analysts can advise if you want to improve every aspect of data in your business.