What is a Data Pipeline? Usage, Types, and Applications

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27th Nov, 2022
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What is a Data Pipeline? Usage, Types, and Applications

Every business these days is looking for ways to integrate data from multiple sources to gain business insights for a competitive advantage or to form an image in the current market. 

Organizations and individuals can achieve a few of their goals based on outcomes that are generated with the help of a data pipeline. Suppose you want daily sales data from a point-of-sale system from a retail outlet so that you can find the total sales of a day and the data get extracted from a series of processes which is usually done via Data Pipeline. 

What is Data Pipeline?

Data Pipeline is a flow of process/mechanism used for moving data from one source to destination through some intermediatory steps. Filtering and features that offer resilience against failure may also be included in a pipeline. 

In simple terms, let us go with one analogy, consider a pipe that accepts input from a source and transports it to supply output at the destination. The data pipeline use cases change with business requirements. 

Data Pipeline Usage

A data pipeline is a crucial instrument for gathering data for enterprises. To assess user behavior and other information, this raw data may be gathered. The data is effectively kept at a location for current or future analysis with the use of a data pipeline. 

  • Batch Processing Pipeline

When data is routinely gathered, converted, and sent to a cloud data warehouse for business operations and traditional business intelligence use cases, a batch process is typically used. Users may easily plan the jobs for processing massive volumes of data with little to no human involvement from siloed sources into a cloud data lake or data warehouse. 

  • Streaming Pipeline

Using a high-throughput messaging system, streaming data pipelines allow users to ingest structured and unstructured data from a variety of streaming sources, such as the Internet of Things (IoT), connected devices, social media feeds, sensor data, and mobile applications, while ensuring that the data is accurately recorded. 

Let us examine this distinction with the aid of a household analogy. Consider this as water supply infrastructure where we have water resources (large amount of water) then it is moved to treatment plant for water treatment. The treated water is then moved in storage (warehouse). The stored water is then sent to houses for their daily use.
Same is in the case of Data Pipeline, the enormous amount of data is collected first and then moved for data quality where the useful amount of data is extracted. The extracted data is then sent to various businesses for their research purposes. 

This water supply infrastructure example serves as a nice analogy for explaining how the data pipeline works and related to it: 

  • Water Resources = Data Sources 
  • Process = Via Pipeline 
  • Treatment Plant = Checking Data Quality 
  • Storage = Data Warehouse 

Data Pipeline Components

  • Origin: Data from all sources in the pipeline enter at the origin. Most pipelines originate from storage systems like Data Warehouses, Data Lakes, etc. or transactional processing applications, application APIs, IoT device sensors, etc. 
  • Dataflow: This relates to the transfer of data from its origin to its destination, as well as the modifications made to it. The Dataflow is based on the subset of Data Pipeline that we will discuss in the later section which is ETL (Extract, Transform and Load). 
  • Destination: This is the last location where data is sent. The destination is figured out by the use case of the business. The destination is often a data lake, data warehouse or data analysis tool. 
  • Storage: Storage refers to all systems used to maintain data at various stages as it moves through the pipeline.  
  • Processing: The process of absorbing data from sources, storing it, altering it, and feeding it into the destination is referred to as processing. Although data processing is related to the data flow, this step emphasis is on the data flow implementation. 
  • Workflow: A series of processes are defined by a workflow, along with how they relate to one another in a pipeline. 
  • Monitoring: Monitoring is done to make sure that the Pipeline and all its stages are functioning properly and carrying out the necessary tasks.

Data Pipeline Architecture

The design and organization of software and systems that copy, purge, or convert data as necessary and then route it to target systems like data warehouses and data lakes is known as Data pipeline architecture. Data pipelines consist of three essential elements which define its architecture: 

  • Data Sources 
  • Data Preparation  
  • Destination 

1. Data Sources

Data are gathered from sources. A few relational database management systems like MySQL, Customer relationship management tools like HubSpot and Salesforce, Enterprise Resource Planning systems like Oracle or SAP, some search engine tools, and even IoT device sensors which includes speedometers as well are examples of common sources of gathering the data. 

2. Preparation

Data is typically taken from sources, altered, and changed following business requirements, and then placed at its destination. Transformation, augmentation, filtering, grouping, and aggregation are typical processing processes. 

3. Destination

When data processing is complete, it goes to a destination, usually a data warehouse or data lake for analysis. 

Data Pipeline vs ETL Pipeline

In many terms, you can say that data pipeline is super set of ETL and that there is no comparison between them. ETL stands for Extract, Transform, and Load which is a subset of Data Pipeline. ETL refers to a collection of operations that take data from one system, transform it, and load it into another. A data pipeline is a broader phrase that refers to any set of procedures that transports data from one system to another, whether it is transformed or not. 

Data from sources including business processes, event tracking systems, and data banks are sent into a data warehouse for business intelligence and analytics via a data pipeline. In contrast, an ETL pipeline loads the data into the target system after it has been extracted, converted, and loaded. The order is crucial; after obtaining data from the source, you must incorporate it into a data model that has been created following your business intelligence needs. This is carried out by gathering, purifying, and then altering the data. The last step is to import the output data into your ETL data warehouse. 

Despite being synonymous with each other, ETL and data pipelines are two distinct concepts. Data pipeline tools may or may not have data transformation, while ETL tools are used for data extraction, transformation, and loading. 

Types of Data in Data Pipeline

1. Structured vs. Unstructured Data

Structured data is information that adheres to a predefined manner or model. This type of data can be quickly analyzed. Unstructured data is gathered information that is not organized in a predefined way or model. This type of unstructured data is very text heavy as it holds facts, dates, and numbers. With its irregularities, this information is difficult to understand compared to data in a fielded database. 

2. Raw Data

Raw data is information that has not been processed for any particular purpose. This data is also known as primary data, and it can include figures, numbers, and readings. The raw data is collected from various sources and moved to a location for analysis or storage. 

3. Processed Data

Processed data comes from collected raw data. System processes convert the raw data into a format for easier visualization or analysis. These processes can also clean and transform the processed data into the desired location. 

4. Cooked Data

Cooked data is another type of raw data that has gone through the processing system. During processing, the raw data has been extracted and organized. In some cases, it has been analyzed and stored for future use.

Evolution of Data Pipelines

The environment for gathering and analyzing data has recently undergone tremendous change. The main goal of creating data pipelines was to transfer data from one layer (transactional or event sources) to data lakes or warehouses where insights might be extracted. 

Data pipeline's origins may be traced back to the days when we manually updated tables using data input operators. There were many human errors in it, making regular data uploads without human interaction necessary, especially for sensitive data from institutions like company which manufactured products, banks, and insurance firms.  

To guarantee that the data was available the next day, transactional data used to be posted every evening. This method was more practical, so gradually we transitioned to the intervals that could be set for these data transfers. Even for urgent purchases, consumers still had to wait until the next day. 

Application of Data Pipelines

Data must be efficiently transferred as soon as feasible from one location to another and transformed into usable information for data-driven organizations. Sadly, there are several barriers to clear data corruption, data flow, including bottlenecks (which cause delay), and various data sources that provide duplicate or contradictory data. 

The human procedures needed to address those issues are all eliminated by data pipelines, which transform the procedure into an efficient, automated workflow. 

Data Pipeline Tools and Technologies

Although there are many distinct types of data pipelining tools and solutions, they all must meet the same three criteria: 

  1. Extrapolate information from several relevant data sources 
  2. Clean up, change, and enhance the data to make it analysis ready 
  3. Data should be loaded into a single information source, often a data lake or a data warehouse 

Types of Data Pipeline Solutions

  • Batch Data is called on-premises data as well. For non-time-sensitive applications, batch processing is a tried-and-true method of working with large datasets. One common Tool used for Batch Data is SAP BODS which is usually run by Master Data Management.

  • Real Time Data: It comes from Satellites and IOT sensors. Real-time data has many tools, one common tool is Apache Kafka. Apache Kafka is a free and open-source data storage device designed for ingesting and processing real-time streaming data. Kafka is scalable because it distributes data over different servers, and is quick because it decouples data streams, resulting in minimal latency.  

Kafka may also distribute and replicate partitions over several servers, preventing server failure. Companies can use real-time analytics to receive up-to-date information about operations and respond quickly, or to supply solutions for smart monitoring of infrastructure performance.

  • Cloud: The Cloud bucket data has been tailored for use with cloud-based data. These solutions enable a business to save money on resources and infrastructure since they may be hosted in the cloud. The business depends on the competence of the cloud provider to host data pipeline and gather the data. To get complete certification course, do check out the Cloud Computing Certification.
  • Open-Source: An open-source is a low-cost data pipeline substitute. These tools are less expensive than those commercial solutions, however using the system requires some knowledge. The technology can be altered by other users because it is freely accessible to the public.

Data Pipeline Examples

Data Quality Pipeline

Data quality pipelines supply features like frequent standardization of all new client names. Real-time customer address validation during the acceptance of a credit application would be regarded as a part of a data quality pipeline. 

Master Data Management Pipeline

Data matching and merging are key components of master data management (MDM). In this pipeline, data is gathered and processed from many sources, duplicate records are found, and the results are combined into a single golden record. 

Business to Business Data Exchange Pipeline

Complex structured or unstructured documents, such as EDI and NACHA documents, SWIFT transactions, and HIPAA transactions, can be sent and received by enterprises from other businesses. B2B data exchange pipelines are used by businesses to send documents like purchase orders or shipment statuses. 

AWS (Amazon Web Services) Data Pipeline

Cloud-based data pipeline solution, i.e., AWS Data Pipeline enables you to process and transfer data between various AWS services and on-premises data sources. You can use the web service AWS Data Pipeline to automate the transfer and transformation of data. Also, it is possible to create data-driven workflows with AWS Data Pipeline so that tasks are dependent on the accomplishment of earlier actions. Your data transformations' parameters are defined by you, and AWS Data Pipeline upholds the logic you have set up. 

To set up AWS Data Pipeline in detail one must have some knowledge of AWS solution architect. One can get training on AWS Solution architect associate do refer to AWS Solution Architect Curriculum.

Implementation Options for Data Pipelines

Data Preparation Tools

To better see and deal with data, users rely on conventional data preparation tools like spreadsheets. Unfortunately, this also requires users to manually manage each new dataset or develop intricate macros. Thank goodness, there exist technologies for business data preparation that can transform manual data preparation procedures into automated data pipelines. 

Design Tools

With the help of an intuitive interface, you may use tools created to construct data processing pipelines using the digital equivalent of toy building blocks. 

Coding

Users use SQL, Spark, Kafka, MapReduce, and other languages and frameworks for data processing. AWS Glue and Databricks Spark are two examples of proprietary frameworks that you may also use. Users must have programming knowledge to apply this strategy. 

Finally, you must decide which data pipelining design pattern best suits your requirements and put it into practice. They consist of: 

Raw Data Load

This straightforward implementation transfers massive, unchanged data between databases. 

Extract-Transform-Load

Before being loaded into the target database, this approach pulls data from a data store and helps clean, standardize, and integrate. 

Extract-Load-Transform

This concept is like ETL; however, the processes have been altered to reduce latency and save time. The target database is where the data is transformed. 

Data Virtualization

Virtualization offers the data as views without physically keeping a separate copy, in contrast to typical processes that make physical copies of stored data. 

Data Stream Processing

This method continuously streams event data in a chronological order. The method separates events, separating each different occurrence into a separate record, allowing assessment for a later use.

Decoding Data Pipelines in Terms of AWS

The following stages make up the essential process: sources of data and their generation: 

  1. Collection from polling services that pull real-time data, such as EC2, S3, etc. 
  2. Enormous amounts of data are sometimes saved in S3 or Amazon RDS using many engines or EC2. 
  3. ETL, or extract, transform, and load, is a procedure that gets more difficult when data volume quickly doubles. 
  4. Data today comes from a wide range of sources with varying types and structures. ETL is essential for supporting the security and privacy of data. Similar functionalities are offered by EMR, Lambda, Connexis, and other services, but AWS glue automates this ETL. 
  5. Analyze - Next phase is consuming this data to understand, make use of the supplied information, and extract insights, which were the main objectives for this procedure. 

In terms of data pipeline there are several terms that can match the requirements of Data Science. Let us look at some of these terms below: 

  • Data Engineering: Data engineering is the process of creating systems that make it possible to collect and use data. Typically, this data is utilized to support further analysis and data science, which frequently uses machine learning.
    Data Analyst: A data analyst is a person with the expertise and abilities to transform raw data into information and insight that can be used to business choices.
    Data set: A grouping of connected pieces of data that can be handled as a whole by a computer yet is made up of individual components. 
  • Data Mining: Data mining is the process of identifying patterns and extracting information from big data sets using techniques that combine machine learning, statistics, and database systems. 
  • Data Modeling: In software engineering, data modelling refers to the process of developing a formal data model for an information system.  
  • Big Data: Big data is defined as data that is more varied, coming at a faster rate and in larger volumes. Simply said, big data refers to larger, more complicated data collections, particularly from new data sources. This is sometimes referred to as the "three Vs."  
  • Unstructured Data: Unstructured data is information that is either not arranged in a predefined way or does not have a predefined data model. Unstructured data can also include facts like dates, numbers, and figures but is often text-heavy. 
  • IOT Device: The term "Internet of things" refers to physical items equipped with sensors, computing power, software, and other technologies that link to other systems and devices over the Internet or other communications networks and exchange data with them. 
  • Data Wrapping: Data wrapping employs analytics to increase the perceived value of your items to customers. But getting it wrong might result in additional expenditures and little gain. 
  • Data Collection: The act of acquiring and analysing data on certain variables in an established system allows one to analyse results and respond to pertinent inquiries. All study subjects, including the physical and social sciences, humanities, and business, require data collecting as a component of their research. 
  • AWS: The most complete and widely used cloud platform in the world, Amazon Web Services (AWS), provides over 200 fully functional services from data centres across the world. 
  • GCP: Google Cloud Platform (GCP) is a collection of cloud computing services that Google offers. It employs the same internal infrastructure as Google does for its consumer products including Google Search, Gmail, Drive, and YouTube.  
  • Big Query: With built-in capabilities like machine learning, geographic analysis, and business intelligence, BigQuery is a fully managed corporate data warehouse that assists you in managing and analysing your data. 
  • Kafka: A distributed publish-subscribe messaging system called Apache Kafka collects data from many source systems and makes it instantly available to target systems. Kafka is a Scala and Java application that is frequently used for big data real-time event stream processing. 
  • Hadoop: A network of numerous computers may be used to address issues requiring enormous volumes of data and processing thanks to the open-source software tools in Apache Hadoop. It provides a software framework for the MapReduce programming model for distributed large data processing and storage. 

Data Science is a vastly intricate and sophisticated discipline. These are only a few of the terms you will often hear while discussing Data Science, and they only serve as a high-level overview of the subject.

Moving Data Pipelines

There are several data pipelines that use standard procedures like: 

  • Data is ingested from a variety of sources (including databases, SaaS apps, the Internet of Things, etc.) and placed on a cloud data lake for storage 
  • Integration is the processing and transformation of data 
  • Applying and cleaning up data quality standards 
  • Data from a data lake to a data warehouse is copied 

Movement will be done by: 

  1. Extracting information from several sources 
  2. Preprocessing adjustments should be used, such as masking confidential data 
  3. Putting information in a repository 
  4. Adapting data transformations to business needs

Conclusion

We hope that this article helped you understand Data Pipeline and aided you in navigating broader in the field of Data. Your comprehension of data pipelines has been thoroughly explained in this article. If you want to learn more about Data Pipeline and other technical courses including Cloud Computing or Data Science, check out KnowledgeHut Computing Certification course where you will learn everything needed to become a professional in any technology.

Frequently Asked Questions (FAQs)

1. What is meant by data pipeline?

A data pipeline automates data transformation and transfer between a source system and a destination repository by using several data-related technologies and methods. 

2. What is a data pipeline example?

Assume you are the owner of an eCommerce company and want to successfully personalize your offerings or use data for rapid insights. For jobs like reporting, business intelligence, sentiment analysis, and recommendation systems, you will therefore need to develop a lot of pipelines. 

3. What are the steps in data pipeline?

Steps to follow for data pipeline are: 

  • Step 1- Collection: Data is collected from various sources. 
  • Step 2- Preparation: Data is then processed for preparing quality data. 
  • Step 3- Ingestion: Collecting data from sources like IOT devices, databases and storing it on data lake. 
  • Step 4- Computation: The outcomes of running the pipeline for analysis is computation process. 
  • Step 5- Presentation: The data is presented in a way that is formulated via charts and graphs or by further analytical tools. 
Profile

Joydip Kumar

Solution Architect

Joydip is passionate about building cloud-based applications and has been providing solutions to various multinational clients. Being a java programmer and an AWS certified cloud architect, he loves to design, develop, and integrate solutions. Amidst his busy work schedule, Joydip loves to spend time on writing blogs and contributing to the opensource community.

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