Data Pipeline- Definition, Architecture, Examples, and Use Cases

Understand what is a data pipeline and learn how to build an end-to-end data pipeline for a business use case.

Data Pipeline- Definition, Architecture, Examples, and Use Cases
 |  BY Daivi

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. This blog will give you an in-depth knowledge of what is a data pipeline and also explore other aspects such as data pipeline architecture, data pipeline tools, use cases, and so much more.


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As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases. Traditional organizations fail to analyze all the generated data due to a lack of automation. However, to keep up with the data generation, organizations are creating end-to-end data pipelines that streamline tasks and transfer data seamlessly between source and target. 

 

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What is a Data Pipeline?

What is a Data Pipeline ?

 

A data pipeline automates the movement and transformation of data between a source system and a target repository by using various data-related tools and processes. To understand the working of a data pipeline, one can consider a pipe that receives input from a source that is carried to give output at the destination. And based on the business use cases, what happens inside the pipeline is decided. A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Whereas online reviews, email content, or image data are classified as unstructured.

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives. While data warehouses contain transformed data, data lakes contain unfiltered and unorganized raw data. However, irrespective of the data source, the complexity of a data pipeline depends on the type of data, the volume of data, and the velocity of data.

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The Importance of a Data Pipeline

Various teams, like marketing, sales, and production, rely on big data to devise strategies for business growth. However, as all departments leverage different tools and operate at different frequencies, it becomes difficult for companies to make sense of the generated data as the information is often redundant and disparate. Consequently, data stored in various databases lead to data silos -- big data at rest. Even if you manually fetch data from different data sources and merge it into Excel sheets, you may be surrounded by complex data errors while performing analysis. It becomes more prominent, especially when you have to perform real-time data analytics since it is nearly impossible to clean and transform data in real-time.

Organizations have built robust data pipelines to mitigate such problems by consolidating data from all distinct data sources into one common destination. However, data generated from one application may feed multiple data pipelines, and those pipelines may have several applications dependent on their outputs. In other words, Data Pipelines mold the incoming data according to the business requirements. This process enables quick data analysis and consistent data quality, crucial for generating quality insights through data analytics or building machine learning models.

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What is an ETL Data Pipeline?

What is an etl data pipeline

 

ETL is the acronym for Extract, Transform, and Load. An ETL pipeline is a series of procedures that comprises extracting and transforming data from a data source. After that, the data is loaded into the target system, such as a database, data warehouse, or data lake, for analysis or other tasks. Data is collected from various data sources during extraction, including business systems, applications, sensors, and databanks. The second step for building etl pipelines is data transformation, which entails converting the raw data into the format required by the end-application. The transformed data is then placed into the destination data warehouse or data lake. It can also be made accessible as an API and distributed to stakeholders.

What is a Big Data Pipeline?

Data pipelines have evolved to manage big data, just like many other elements of data architecture. Big data pipelines are data pipelines designed to support one or more of the three characteristics of big data (volume, variety, and velocity). Building streaming data pipelines for large data is enticing due to the velocity of the data. Data pipelines must be scalable due to the volume of big data, which might fluctuate over time. The big data pipeline must process data in large volumes concurrently because, in reality, multiple big data events are likely to occur at once or relatively close together. Big data pipelines must be able to recognize and process data in various formats, including structured, unstructured, and semi-structured, due to the variety of big data.

Features of a Data Pipeline

1) Real-Time Data Processing and Analysis: Modern data pipelines are expected to automate a sequence of tasks in real-time so businesses can make informed decisions with quality data. Over the years, companies primarily depended on batch processing to gain insights. However, real-time insights can help them make quick decisions and act faster than their competitors, differentiating themselves in the competitive market.

2) Fault-Tolerant: Though data pipelines are highly available, continuous transit of raw data may lead to failure due to unexpected or untidy data. To mitigate impacts on critical processes, data pipelines are designed with a distributed architecture that immediately stimulates alerts for malfunctioning. Such sensitivity is helpful in cases like node failure, application failure, or broken connectivity to offer high reliability.

3) Checkpointing: Organizations face a common issue of data loss and data duplication while running a data pipeline. Especially in large organizations, the complexity of pipelines increases since the need for data across departments and different initiatives increases. Consequently, data engineers implement checkpoints so that no event is missed or processed twice.

4) Scalable: Traditional pipelines struggle to cater to multiple workloads in parallel. It not only consumes more memory but also slackens data transfer. Modern cloud-based data pipelines are agile and elastic to automatically scale compute and storage resources. These services are available on demand, allowing companies to scale seamlessly based on the surge. 

Data Pipeline Architecture

An efficient data pipeline requires dedicated infrastructure; it has several components that help you process large datasets. 

Data Pipeline Architecture

Image Credit: altexsoft.com 

 

Below are some essential components of the data pipeline architecture:

  1. Source: It is a location from where the pipeline extracts raw data. Data sources may include relational databases or data from SaaS (software-as-a-service) tools like Salesforce and HubSpot. In most cases, data is synchronized in real-time at scheduled intervals. Even if data is pulled at regular intervals, you can ingest raw data from multiple sources using an API call or push mechanism.

  2. Transformation: It is an operation that brings necessary changes in data. Data transformation may include data standardization, deduplication, reformating, validation, and cleaning. The ultimate goal when data traverses from the source system to the destination system is to transform the dataset to feed it into centralized storage. However, you can also pull data from centralized data sources like data warehouses to transform data further and build ETL pipelines for training and evaluating AI agents.

  3. Processing: It is a data pipeline component that decides the data flow implementation. Data ingestion methods gather and bring data into a data processing system. There are two data ingestion models -- batch processing, for collecting data periodically, and stream processing, where data is sourced, manipulated, and loaded instantaneously.

  4. Workflow: It involves the sequencing of jobs in the data pipeline and managing their dependency. Workflow dependencies can be technical or business-oriented, deciding when a data pipeline runs.

  5. Monitoring: It is a component that ensures data integrity. Data pipelines must have consistent monitoring to check data accuracy and data loss. As data size grows, pipelines must include mechanisms to alert administrators about speed and efficiency.

How to Build an End-to-End Data Pipeline from Scratch?

Before building a data pipeline from scratch, you must brainstorm various factors, both business and technology, that could influence the data pipeline architecture and other transformations within the pipeline. 

Here are a few broader guiding steps that can be useful when designing a data pipeline architecture:

  1.  Defining Problems: The very first task involves understanding the business goal that can be served by making various pipelines.

  2. Requirements: Once the need for a pipeline for a project is understood, you should prepare a checklist on the type, size, frequency, and source of data your pipeline must support.

  3. Building Pipelines: The next step involves synchronizing pipelines’ output for desired applications like reporting, data science, automation, and more.

  4. Monitor: Finally, monitor the pipeline and initially be critical of the end output to provide feedback and eliminate potential issues.

Data Pipeline Example

Suppose you are running an eCommerce business and want to use data for quick insights or offer personalizations effectively. In that case, you will be required to build numerous pipelines for reporting, business intelligence, sentiment analysis, and recommendation systems.

Step 1: Defining Business Problem or Use Cases

As an online shop manager, you must identify which aspect of business you want to improve with data. For instance, if you build a sentiment analysis of your products’ reviews, you will have to build ETL pipelines for collecting data from a source such as any social media platform or even your website. While social media would help you gain data about what customers or potential customers are saying in public, product reviews from your website can provide buyers’ opinions. Developers must leverage APIs or web scraping tools to gather information from social media platforms. While ‘Beautifulsoup’ and ‘requests’ libraries can be used for screen scraping, Tweepy or Facebook’s Graph API can assist you in pulling information from respective social media platforms. In addition, to extract data from the eCommerce website, you need experts familiar with databases like MongoDB that store reviews of customers.

Step 2: Requirements

In defining the problems or use cases, you must figure out how often you need specific outcomes. In this case, what would be the frequency of checking the sentimental analysis of a product? Based on the popularity of the products, you can estimate the frequency of comments and pull information accordingly. If a product is bought every few seconds, you will have to extract new reviews from the website every few hours. Similarly, you can pull information from social media. This would then take you to finalize the size of your centralized storage or data feed to machine learning models.

Step 3: Building Data Pipelines

While building pipelines, you will focus on automating tasks like removing spam, eliminating unknown values or characters, translating the text into English (if required), and performing other NLP-related tasks like tokenization and lemmatization. In other words, you will write codes to carry out one step at a time and then feed the desired data into machine learning models for training sentimental analysis models or evaluating sentiments of reviews, depending on the use case. 

You can use big-data processing tools like Apache Spark, Kafka, and more to create such pipelines. However, it is not straightforward to create data pipelines. You also have to write codes to handle exceptions to ensure data continuity and prevent data loss.

Step 4: Monitor 

To visualize your pipelines, you can use Airflow, an open-source tool, to schedule and automate workflows. Proper feedback is the key to obtaining quality insights or building robust machine learning models, as incorrect data might lead to assumptions afflicting business operations. Sometimes, it could lead to irreplaceable damage and customer churn.

Effective data pipelines enable engineers to save time and effort by eliminating bottlenecks in implementing data-driven initiatives, providing stable analytics data, or building machine learning models.

Data Pipeline Tools

Here are a few significant data pipeline tools that every data engineer must know about.

You can easily extract and load your data for analytics using the fully managed extract, transform, and load (ETL) service AWS Glue. To organize your data pipelines and workflows, build data lakes or data warehouses, and enable output streams, AWS Glue uses other big data tools and AWS services.  AWS Glue performs API operations to modify your data, provide runtime logs, and send you notifications so you can monitor the status of your processes. The AWS Glue console integrates these services into a managed application, allowing you to focus on creating and managing your data pipeline activities.

AWS Glue Projects For Practice

If you want to gain a fundamental understanding of AWS Glue, here is an interesting project idea you should work on.

  • Orchestrate Redshift ETL using AWS Glue and Step Functions

You will learn how to create an ETL Big Data pipeline to extract actionable business insights from the data using AWS Glue, other big data tools, and custom apps. For this project, you will work with the Amazon Customer Reviews dataset, which includes product reviews submitted by Amazon customers between 1995 and 2015.

Source Code: Orchestrate Redshift ETL using AWS Glue and Step Functions

Apache Airflow data pipeline

 

You can easily schedule and operate your complex data or ETL pipelines using the workflow engine Apache Airflow. Doing this ensures that each activity in your data pipeline is executed on time and using the proper tools. The three main uses of Airflow are for scheduling, orchestrating, and monitoring workflows. Using Airflow is comparable to using a Python package. It is well-written, simple to grasp, and fully customizable. Thus, it enables data engineers to build a data pipeline with any level of complexity. Airflow also allows you to utilize any BI tool, connect to any data warehouse, and work with unlimited data sources.

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Apache Airflow Projects For Practice

If you want to understand Apache Airflow, here is an interesting project idea you should work on.

  • Build a Data Pipeline using Airflow, Kinesis, and AWS Snowflake

In this Airflow project, you will build a data pipeline from the EC2 logs to storage in Snowflake and S3 after data processing and transformation with Airflow DAGs. Through Airflow DAG processing and transformation, two data streams (customer and order data) will be added to the Snowflake and S3 processed stage. Working on this project will introduce you to Amazon Managed Workflows for Apache Airflow (MWAA), a managed orchestration solution for Apache Airflow1 that greatly simplifies the setup and operation of end-to-end data pipelines at scale in the cloud.

Source Code: Build a Data Pipeline using Airflow, Kinesis, and AWS Snowflake

The primary feature of Apache Kafka, an open-source distributed event streaming platform, is a message broker (also known as a distributed log). Over the past couple of years, the community has released various excellent products, such as Apache Kafka Streams for creating stream-processing applications on top of Apache Kafka or Apache Kafka Connect for integrating Kafka with external data systems. Building real-time data pipelines is much easier with the help of Kafka, Kafka Connect, and Kafka Streams.

Apache Kafka Projects For Practice

Here is an innovative project using Apache Kafka to help you understand the use of this tool in building efficient data pipelines.

  • Building Real-Time Data Pipelines with Kafka Connect

Working on this big data pipeline project will teach you how the Kafka Streaming API is used for transformation while the Kafka Connect APIs are utilized for data loading and ingestion.

Source Code: Building Real-Time Data Pipelines with Kafka Connect

Talend ETL data pipeline

 

Talend is a popular open-source data integration and big data pipeline tool with more than 6500 customers worldwide. It offers a range of services for big data experts, such as cloud services, business application integration, data management, data integration, etc. Talend offers strong data integration capabilities for carrying out data pipeline tasks. Talend Open Studio (TOS) is one of the most important data pipeline tools available. You can easily control each step of the data pipeline process using TOS, from the original ETL (Extract, Transform, and Load) design to the execution of the ETL data load. Using the graphical user interface that Talend Open Studio provides, you can easily map structured and unstructured data from multiple sources to the target systems. 

Talend Projects For Practice:

Learn more about the working of the Talend ETL tool by working on this unique project idea.

  • Talend Real-Time Project for ETL Process Automation

This Talend big data project will teach you how to create an ETL pipeline in Talend Open Studio and automate file loading and processing. You must first create a connection to the MySQL database to use Talend to extract data. You can learn all the fundamentals of the Talend tool with the help of this project.

Source Code: Talend Real-Time Project for ETL Process Automation

AWS Data Pipeline

AWS Data Pipeline, a managed ETL (Extract, Transform, and Load) service, enables you to specify data flows and transformations for a range of AWS services and resources on-premises. With the help of the AWS Data Pipeline, you can establish the interrelated processes that build your pipeline, which comprises the data nodes that store data, the sequentially running EMR tasks or SQL queries, and the business logic activities. You can create data-driven workflows with AWS Data Pipeline so that tasks can depend on the execution of earlier tasks. AWS Data Pipeline implements the logic you have defined based on the parameters you specify for your data transformations.

AWS Data Pipeline

Source: Aws.amazon.com

 

AWS Data Pipeline manages-

- The logic for scheduling, executing and rerunning your jobs.

- Keeping track of the interactions between your business logic, data sources, and prior processing workflows to ensure that none of your logic is executed until all of its prerequisites are fulfilled.

- Sending any appropriate failure notices.

- Creating and overseeing any computing resources that your jobs might need.

Azure Data Pipeline

This section will give you an idea of Azure Data Factory Pipeline and Azure Databricks Pipeline,

Azure Data Factory Data Pipeline

Azure Data Factory data pipeline

 

With the help of the cloud-based data integration platform Azure Data Factory, you can design data-driven workflows for orchestrating and managing data flow and transformation. ADF does not store any data on its own. Instead, you can design data-driven workflows to coordinate data transfer between supported data stores or in an on-premise environment. You can also use programmatic and UI tools to monitor and optimize workflows.

With the help of the Data Factory service, you can build data or ETL pipelines that move and transform data and then schedule their execution (hourly, daily, weekly, etc.). As a result, time-sliced data is used and generated by workflows. You can set the pipeline mode as scheduled (once per day) or one-time.

The typical workflow for an Azure Data Factory data pipeline involves three steps.

  1. Connecting and gathering- Establish connections to all necessary data and processing sources, including SaaS services, file shares, FTP, and web services. Use the Copy Activity in the data pipeline to transport data from both on-premise and cloud data store to a centralized data store for further processing. 

  2. Transforming and enhancing- Data is transformed utilizing compute services like HDInsight Hadoop, Spark, Azure Data Lake Analytics, and Machine Learning once it is accessible in a centralized data repository in the cloud.

  3. Publish- Transform data in the cloud and send it to on-premises sources like SQL Server or store it in your cloud storage sources for BI and data analytics tools and other apps to use.

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Databricks Pipeline

Let's look at how data engineers can successfully use Databricks' Delta Live Tables to construct data pipelines for automated ETL.

  • Step 1- Automating the Lakehouse's data intake.

Databricks data pipeline

 

Transferring different data types, such as structured, unstructured, or semi-structured data, into the lakehouse on schedule is the biggest challenge data engineers encounter. With Databricks, businesses can easily move data into the lakehouse in batch or streaming modes at low cost and latency without additional settings, such as triggers or manual scheduling, using Auto Loader. Auto Loader uses the simple cloudFiles syntax to identify and process new files as they come in automatically.

 

  • Step 2- Internal Data transformation at LakeHouse.

Data transformation in Databricks data pipeline

 

To transform unstructured data into structured data suitable for data analytics, data science, or machine learning, data engineers must apply data transformations or business logic to data streams as it enters the lakehouse. Before putting raw data into tables or views, DLT gives users access to the full power of SQL or Python. Data transformation can take many forms, such as merging data from different data sets, aggregating data, sorting data, generating additional columns, changing data formats, or implementing validation procedures.

  • Step 3- Ensuring the accuracy and reliability of data within Lakehouse.

Reliability check in Databricks data pipeline

 

The consistency of the data throughout the lakehouse depends on the quality and integrity of the data. Data engineers can design data quality and integrity controls in the data pipeline using DLT by explicitly defining Delta Expectations, such as by implementing column value checks.

 

Data quality metrics in Databricks data pipeline

 

The data pipeline event log contains a record of all the data quality parameters, allowing users to monitor and report on data quality across the entire data pipeline. You can create reports using data visualization tools to show the quality of the data set and the number of rows that passed or failed the data quality checks.

  • Step 4- Deploy and Operationalize ETL automatically

Automated ETL in Databricks data pipeline

 

DLT builds a graph that encompasses the semantics of the tables and views defined by the data pipeline when deployed. Additionally, DLT automatically joins tables or views defined by the data pipeline and checks for inconsistencies, missing dependencies, and syntax issues.

There is no need to manually manage data pipeline operations or write check-pointing code because DLT automatically pauses and begins the pipeline in the event of system failures. DLT automatically handles every complexity necessary to restart, backfill, execute the data pipeline from scratch, or release a new pipeline version.

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  • Step 5- Data Pipeline Scheduling.

 

Scheduling in Databricks data pipeline

 

Data engineers also need to coordinate ETL workloads. DLT pipelines can be scheduled with Databricks Jobs to deploy end-to-end production-ready ETL pipelines automatically. Data engineers can create a recurring schedule for their ETL workloads in Databricks Jobs' scheduler and set notifications for when the job is successful or encounters a problem.

Airflow Data Pipeline

Apache Airflow is an open-source tool for designing, scheduling, and managing batch-oriented workflows. The Airflow framework may be readily extended to connect with new technologies and contains operators to integrate with various technologies. Your workflows can be set up as an Airflow DAG if they have a defined start and end and run at regular intervals. The user interface for Airflow offers detailed views of data pipelines and individual jobs and a timeline view of pipelines. You can perform tasks, such as retrying a task in case of failure and examining logs from the UI.

You can run pipelines regularly by utilizing Airflow's robust scheduling semantics. Additionally, you can use Apache Airflow to create data pipelines that use incremental processing to minimize unnecessary, expensive reevaluations. Backfilling and other airflow features make it simple to reprocess existing data. You can use Apache Airflow to create data pipelines that can recompute derived data sets even after you update your code.

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Learn to Create a Data Pipeline

Here are a few data pipeline projects you must explore to understand how to build efficient data pipelines.

Data pipeline using Azure Synapse Analytics

 

You will create a data pipeline on Azure for this project employing Azure Synapse Analytics, Azure Storage, and an Azure Synapse SQL pool. Using this data pipeline, you will analyze the 2021 Olympics dataset. You will first create an account for Azure Storage and upload data files in a container. The next step is to set up an analytics workspace and an SQL pool in Azure Synapse. Then, build a data pipeline to import data from Azure Storage into SQL pool tables. After loading data from SQL pool tables into Power BI, create and publish a Power BI dashboard on the Azure synapse workspace.

Source: Building Data Pipelines with Azure Synapse Analytics

In this AWS project, you will create a data pipeline using a range of AWS services and Apache tools, including Amazon OpenSearch, Logstash, Kibana, Apache NiFi, and Apache Spark. You will first use Apache NiFi to retrieve data from an API, transform it, and load it into an AWS S3 bucket. You will feed data into Amazon OpenSearch using Logstash by ingesting it from an AWS S3 bucket. You will feed data into Kibana from Amazon OpenSearch to perform data visualization on the data. Additionally, you will use PySpark to conduct your data analysis.

Source: Build an AWS Data Pipeline using NiFi, Spark, and ELK Stack

In this project, you will use Apache Spark, HBase, and Apache Phoenix to create a Real-Time Streaming Data Pipeline for an application that analyzes oil wells. Create and run an AWS EC2 instance to begin working on this project. Next, use a docker-compose file to create docker images on an EC2 machine via ssh. Download the dataset and store it in HDFS. Using Spark, read data from HDFS storage and write it to an HBase database. Create a Phoenix view on top of the HBase database to use SQL queries to analyze the data.

Source: Build a Streaming Data Pipeline using Spark, HBase, and Phoenix

Organizations need help to perform big data processes that seek continuous monitoring and supervision at various stages. However, enterprises can create robust data pipelines to automate several data processes and drive business efficiency. As the demand for big data and machine learning processes further increases, advancements in data pipelines will help data engineers and data scientists deliver scalable solutions while reducing costs. To learn more about building efficient data pipelines, explore some real-world Data Science and Big Data projects in the ProjectPro repository.

FAQs on Data Pipeline

A data pipeline is a method of sending data from a source to a target (such as a data warehouse). Data is modified and optimized along the way to get to the point where it can be analyzed and used to generate business insights.

Data pipeline is not an ETL, as the term ‘data pipeline’ refers to any process that transfers data from one system to another while potentially transforming it. On the other hand, ETL refers to a specific set of processes that extract data from a source, transform it, and then load it into a target system (a data warehouse or a data lake).

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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