What is a Data Pipeline? Definition and Best Practices (2022)

A data pipeline is an end-to-end sequence of digital processes used to collect, modify, and deliver data. Organizations use data pipelines to copy or move their data from one source to another so it can be stored, used for analytics, or combined with other data. Data pipelines ingest, process, prepare, transform and enrich structured, unstructured and semi-structured data in a governed manner; this is called data integration.

Ultimately, data pipelines help businesses break down information silos and easily move and obtain value from their data in the form of insights and analytics.

(Video) What is Data Pipeline | How to design Data Pipeline ? - ETL vs Data pipeline
What is a Data Pipeline? Definition and Best Practices (1)

Data Pipeline Architecture

(Video) WHAT IS DATA PIPELINE? | BEST TOOLS FOR OPERATIONS WITH DATA PIPELINES

Data Pipeline Types and Use Cases

Data pipelines are categorized based on how they are used. Batch processing and real-time processing are the two most common types of pipelines.

Batch processing pipelines

A batch process is primarily used for traditional analytics use cases where data is periodically collected, transformed, and moved to a cloud data warehouse for business functions and conventional business intelligence use cases. Users can quickly mobilize high-volume data from siloed sources into a cloud data lake or data warehouse and schedule the jobs for processing it with minimal human intervention. With batch processing, users collect and store data during an event known as a batch window, which helps manage a large amount of data and repetitive tasks efficiently.

Streaming pipelines

Streaming data pipelines enable users to ingest structured and unstructured data from a wide range of streaming sources such as Internet of Things (IoT), connected devices, social media feeds, sensor data, and mobile applications using a high-throughput messaging system making sure that data is captured accurately. Data transformation happens in real time using a streaming processing engine such as Spark streaming to drive real-time analytics for use cases such as fraud detection, predictive maintenance, targeted marketing campaigns, or proactive customer care.

On-Premises vs. Cloud Data Pipelines

Traditionally, organizations have relied on data pipelines built by in-house developers. But, with the rapid pace of change in today’s data technologies, developers often find themselves continually rewriting or creating custom code to keep up. This is time consuming and costly.

Building a resilient cloud-native data pipeline helps organizations rapidly move their data and analytics infrastructure to the cloud and accelerate digital transformation.

Deploying a data pipeline in the cloud helps companies build and manage workloads more efficiently. Control cost by scaling in and scaling out resources depending on the volume of data that is processed. Organizations can improve data quality, connect to diverse data sources, ingest structured and unstructured data into a cloud data lake, data warehouse, or data lakehouse, and manage complex multi-cloud environments. Data scientists and data engineers need reliable data pipelines to access high-quality, trusted data for their cloud analytics and AI/ML initiatives so they can drive innovation and provide a competitive edge for their organizations.

What Is the Difference Between a Data Pipeline and ETL?

A data pipeline can process data in many ways. ETL is one way a data pipeline processes data and the name comes from the three-step process it uses: extract, transform, load. With ETL, data is extracted from a source. It’s then transformed or modified in a temporary destination. Lastly, the data is loaded into the final cloud data lake, data warehouse, application or other repository.

(Video) What are Data Pipelines?

ETL has traditionally been used to transform large amounts of data in batches. Nowadays, real-time or streaming ETL has become more popular as always-on data has become readily available to organizations.

How to Build an Efficient Data Pipeline in 6 Steps

Building an efficient data pipeline is a simple six-step process that includes:

  1. Cataloging and governing data, enabling access to trusted and compliant data at scale across an enterprise.
  2. Efficiently ingesting data from various sources such as on-premises databases or data warehouses, SaaS applications, IoT sources, and streaming applications into a cloud data lake.
  3. Integrating data by cleansing, enriching, and transforming it by creating zones such as a landing zone, enrichment zone, and an enterprise zone.
  4. Applying data quality rules to cleanse and manage data while making it available across the organization to support DataOps.
  5. Preparing data to ensure that refined and cleansed data moves to a cloud data warehouse for enabling self-service analytics and data science use cases.
  6. Stream processing to derive insights from real-time data coming from streaming sources such as Kafka and then moving it to a cloud data warehouse for analytics consumption.

Data Pipeline Best Practices

When implementing a data pipeline, organizations should consider several best practices early in the design phase to ensure that data processing and transformation are robust, efficient, and easy to maintain. The data pipeline should be up-to-date with the latest data and should handle data volume and data quality to address DataOps and MLOps practices for delivering faster results. To support next-gen analytics and AI/ML use cases, your data pipeline should be able to:

  1. Seamlessly deploy and process any data on any cloud ecosystem, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and Snowflake for both batch & real-time processing.
  2. Efficiently ingest data from any source, such as legacy on-premises systems, databases, CDC sources, applications, or IoT sources into any target, such as cloud data warehouses and data lakes
  3. Detect schema drift in RDBMS schema in the source database or a modification to a table, such as adding a column or modifying a column size and automatically replicating the target changes in real time for data synchronization and real-time analytics use cases
  4. Provide a simple wizard-based interface with no hand coding for a unified experience
  5. Incorporate automation and intelligence capabilities such as auto-tuning, auto-provisioning, and auto-scaling to design time and runtime
  6. Deploy in a fully managed advanced serverless environment for improving productivity and operational efficiency
  7. Apply data quality rules to perform cleansing and standardization operations to solve common data quality problems

Data Pipeline Examples in Action: Modernizing Data Processing

Data pipelines in technology: SparkCognition

SparkCognition partnered with Informatica to offer the AI-powered data science automation platform Darwin, which uses pre-built Informatica Cloud Connectors to allow customers to connect it to most common data sources with just a few clicks. Customers can seamlessly discover data, pull data from virtually anywhere using Informatica's cloud-native data ingestion capabilities, then input their data into the Darwin platform. Through cloud-native integration, users streamline workflows and speed up the model-building process to quickly deliver business value. Read the full story.

What is a Data Pipeline? Definition and Best Practices (2)

Data pipelines in healthcare: Intermountain Healthcare

Informatica helped Intermountain Healthcare to locate, understand, and provision all patient-related data across a complex data landscape spanning on-premises and cloud sources. Informatica data integration and data engineering solutions helped segregate datasets and establish access controls and permissions for different users, strengthening data security and compliance. Intermountain began converting approximately 5,000 batch jobs to use Informatica Cloud Data Integration. Data is fed into a homegrown, Oracle-based enterprise data warehouse that draws from approximately 600 different data sources, including Cerner EMR, Oracle PeopleSoft, and Strata cost accounting software, as well as laboratory systems. Affiliate providers and other partners often send data in CSV files via secure FTP, which Informatica Intelligent Cloud Services loads into a staging table before handing off to Informatica PowerCenter for the heavy logic. Read the full story.

Data Pipelines Support Digital Transformation

As organizations are rapidly moving to the cloud, they need to build intelligent and automated data management pipelines. This is essential to get the maximum benefit of modernizing analytics in the cloud and unleash the full potential of cloud data warehouses and data lakes across a multi-cloud environment.

Resources for Data Pipelines for Cloud Analytics

Now that you’ve had a solid introduction to data pipelines, level up your knowledge with the latest data processing, data pipelines and cloud modernization resources.

Blog: Data Processing Pipeline Patterns

Blog: How AI-Powered Enterprise Data Preparation Empowers DataOps Teams

(Video) What Are Data Pipelines

Cloud Analytics Hub: Get More Out of Your Cloud

(Video) Data Pipelines Explained

FAQs

What is data pipeline Meaning? ›

A data pipeline is a set of tools and processes used to automate the movement and transformation of data between a source system and a target repository.

What is data pipeline and its functions? ›

Data pipelines enable the flow of data from an application to a data warehouse, from a data lake to an analytics database, or into a payment processing system, for example. Data pipelines also may have the same source and sink, such that the pipeline is purely about modifying the data set.

What is a data pipeline explain with diagram? ›

A data pipeline is a means of moving data from one place (the source) to a destination (such as a data warehouse). Along the way, data is transformed and optimized, arriving in a state that can be analyzed and used to develop business insights.

Why do we need a data pipeline? ›

Yet modern data pipelines enable your business to quickly and efficiently unlock the data within your organisation. They allow you to extract information from its source, transform it into a usable form, and load it into your systems where you can use it to make insightful decisions.

What makes a good data pipeline? ›

Just make sure your data pipeline provides continuous data processing; is elastic and agile; uses isolated, independent processing resources; increases data access; and is easy to set up and maintain.

What is the first step of a data pipeline? ›

The first step in a Pipeline involves extracting data from the source as input. The output generated at each step acts as the input for the next step. This process continues until the pipeline is completely executed. In addition, some independent steps might run in parallel as well in some cases.

What is the difference between data pipeline and ETL? ›

ETL refers to a set of processes extracting data from one system, transforming it, and loading it into a target system. A data pipeline is a more generic term; it refers to any set of processing that moves data from one system to another and may or may not transform it.

What are the different types of data pipelines? ›

The most common types of data pipelines include:
  • Batch. When companies need to move a large amount of data regularly, they often choose a batch processing system. ...
  • Real-Time. In a real-time data pipeline, the data is processed almost instantly. ...
  • Cloud. ...
  • Open-Source. ...
  • Structured vs. ...
  • Raw Data. ...
  • Processed Data. ...
  • Cooked Data.
1 Jan 2022

What is data pipeline tool? ›

Often, a Data Pipeline tool is used to automate this process end-to-end in an efficient, reliable, and secure manner. Data Pipeline software guarantees consistent and effortless migration from various data sources to a destination, often a Data Lake or Data Warehouse.

How do you manage data pipeline? ›

  1. 15 Essential Steps To Build Reliable Data Pipelines. ...
  2. Differentiate between initial data ingestion and a regular data ingestion. ...
  3. Parametrize your data pipelines. ...
  4. Make it retriable (aka idempotent) ...
  5. Make single components small — even better, make them atomic. ...
  6. Cache intermediate results. ...
  7. Logging, logging, logging.
1 Dec 2020

What is ETL data pipeline? ›

An ETL pipeline is a set of processes to extract data from one system, transform it, and load it into a target repository. ETL is an acronym for “Extract, Transform, and Load” and describes the three stages of the process.

What is a data pipeline SQL? ›

Data pipelines are processes that extract data, transform the data, and then write the dataset to a destination. In contrast with ETL, data pipelines are typically used to describe processes in the context of data engineering and big data.

How do you simplify pipeline data? ›

To simplify your data pipeline you need speed, security, flexibility and self-service in one package. If you miss the catalog, the data virtualization, security and the query performance, then you are once again adding friction resulting in continued complex pipelines and missed opportunities.

What is a 5 stage pipeline? ›

Those stages are, Fetch, Decode, Execute, Memory, and Write. The simplicity of operations performed allows every instruction to be completed in one processor cycle.

What are data pipeline components? ›

Pipeline components specify the data sources, activities, schedule, and preconditions of the workflow. They can inherit properties from parent components. Relationships among components are defined by reference. Pipeline components define the rules of data management.

How does a data pipeline connect to data source? ›

A data pipeline is a series of processes that migrate data from a source to a destination database. An example of a technical dependency may be that after assimilating data from sources, the data is held in a central queue before subjecting it to further validations and then finally dumping into a destination.

Who creates a data pipeline? ›

That's why data analysts and data engineers turn to data pipelining. This article gives you everything you need to know about data pipelining, including what it means, how it's put together, data pipeline tools, why we need them, and how to design one.

What is a real time data pipeline? ›

Streaming data pipelines, by extension, is a data pipeline architecture that handle millions of events at scale, in real time. As a result, you can collect, analyze, and store large amounts of information. That capability allows for applications, analytics, and reporting in real time.

What is end to end data pipeline? ›

A data pipeline is an end-to-end sequence of digital processes used to collect, modify, and deliver data. Organizations use data pipelines to copy or move their data from one source to another so it can be stored, used for analytics, or combined with other data.

What is the priority of building a data pipeline? ›

Final Thoughts. As a data engineer, data integrity should be considered a top priority when building a pipeline. Not only does it give you confidence that your code is working as expected, but it also gives the user confidence that their data is accurate.

What is data pipeline architecture? ›

A data pipeline architecture is a system that captures, organizes, and routes data so that it can be used to gain insights. Raw data contains too many data points that may not be relevant. Data pipeline architecture organizes data events to make reporting, analysis, and using data easier.

What is pipeline analysis? ›

Pipeline analysis is understanding that your entire recruiting process is a funnel and that you can apply some key methods for analyzing what's performing well and what in the process may need some work.

What questions do you ask before designing data pipelines? ›

So before building any data pipeline it's important to consider a few things.
...
⏰ How Often Will You Pull Data?
  • When the data is needed?
  • How much data being pulled?
  • How often the data changes?

How do you create an automated data pipeline? ›

The platform offers various data integration methods for building a fully automated data pipeline. These include: Extract, Transform, Load (ETL): Integrate.io extracts data from sources such as e-commerce systems, transforms data into the proper form for analytics, and loads it to a data warehouse.

Is ETL part of data pipeline? ›

ETL Pipeline vs.

A data pipeline refers to the entire set of processes applied to data as it moves from one system to another. As the term “ETL pipeline” refers to the processes of extraction, transforming, and loading of data into a database such as a data warehouse, ETL pipelines qualify as a type of data pipeline.

What is difference between pipeline and data flow? ›

Data moves from one component to the next via a series of pipes. Data flows through each pipe from left to right. A "pipeline" is a series of pipes that connect components together so they form a protocol.

How long does it take to build a data pipeline? ›

Building data pipelines is not a small feat. Generally, it takes somewhere between one to three weeks [Exact time depends on the source and the format in which it provides data] for a developing team to set up a single rudimentary pipeline.

What is a pipeline system? ›

Pipeline Systems means all parts of those physical facilities through which gas or oil moves in transportation, including but not limited to pipes, valves, and other appurtenances attached to pipes such as compressor units, metering stations, regulator stations, delivery stations, holders, or other related facilities.

What is ETL process? ›

ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.

What are the main steps of an analytical pipeline? ›

The short description here is not definitive, but processes often have some or all of the following steps:
  • The current statistics production process.
  • Using open source.
  • Version control on GitHub.
  • Quality assurance.
  • Automated testing.
  • Test history.
  • Code coverage.
27 Mar 2017

What is data pipeline in machine learning? ›

A machine learning pipeline is the end-to-end construct that orchestrates the flow of data into, and output from, a machine learning model (or set of multiple models). It includes raw data input, features, outputs, the machine learning model and model parameters, and prediction outputs.

What is data pipeline in Azure? ›

A pipeline is a logical grouping of activities that performs a unit of work. Together, the activities in a pipeline perform a task. For example, a pipeline can contain a group of activities that ingests data from an Azure blob, and then runs a Hive query on an HDInsight cluster to partition the data.

What is data pipeline monitoring? ›

Data Pipeline Observability enables your Data Engineers to monitor their Data Pipelines and optimize their performance by altering the parameters and resources like computation units, storage requirements, network resources, and many more.

Can you build a data pipeline in SQL? ›

You build your pipelines using SQL commands to describe the business logic and use connectors to define both ingestion sources (such as streams, files, or databases via CDC) and output destinations (a data lake table, your cloud data warehouse, or perhaps back into another event stream).

Which data pipeline tools have you been using lately? ›

7 Best Data Pipeline Tools 2022
  • Free and open-source software (FOSS)
  • Keboola.
  • Stitch.
  • Segment.
  • Fivetran.
  • Integrate.io (formerly Xplenty)
  • Etleap.

What is a pipeline in cloud? ›

On any Software Engineering team, a pipeline is a set of automated processes that allow developers and DevOps professionals to reliably and efficiently compile, build, and deploy their code to their production compute platforms.

What is ETL data pipeline? ›

An ETL pipeline is a set of processes to extract data from one system, transform it, and load it into a target repository. ETL is an acronym for “Extract, Transform, and Load” and describes the three stages of the process.

What is data pipeline in machine learning? ›

A machine learning pipeline is the end-to-end construct that orchestrates the flow of data into, and output from, a machine learning model (or set of multiple models). It includes raw data input, features, outputs, the machine learning model and model parameters, and prediction outputs.

What is a data pipeline in Python? ›

Pipeline are a sequence of data processing mechanisms. Pandas pipeline feature allows us to string together various user-defined Python functions in order to build a pipeline of data processing.

What is the difference between ETL and data pipeline? ›

ETL refers to a set of processes extracting data from one system, transforming it, and loading it into a target system. A data pipeline is a more generic term; it refers to any set of processing that moves data from one system to another and may or may not transform it.

What is ETL pipeline example? ›

ETL pipeline implies that the pipeline works in batches. For example- pipe is run once every 12 hours. Data Pipeline can also be run as a streaming evaluation (i.e., every event is handled as it occurs). Type of data pipeline is an ELT pipeline (loading the entire data to the data warehouse and transform it later).

Is ETL part of data pipeline? ›

ETL Pipeline vs.

A data pipeline refers to the entire set of processes applied to data as it moves from one system to another. As the term “ETL pipeline” refers to the processes of extraction, transforming, and loading of data into a database such as a data warehouse, ETL pipelines qualify as a type of data pipeline.

How do you manage data pipeline? ›

  1. 15 Essential Steps To Build Reliable Data Pipelines. ...
  2. Differentiate between initial data ingestion and a regular data ingestion. ...
  3. Parametrize your data pipelines. ...
  4. Make it retriable (aka idempotent) ...
  5. Make single components small — even better, make them atomic. ...
  6. Cache intermediate results. ...
  7. Logging, logging, logging.
1 Dec 2020

How do you create and maintain data pipeline? ›

Data pipelining tools and solutions come in many forms, but they all have the same three requirements: Extract data from multiple relevant data sources. Clean, alter, and enrich the data so it can be ready for analysis. Load the data to a single source of information, usually a data lake or a data warehouse.

What is end to end data pipelines? ›

A data pipeline is an end-to-end sequence of digital processes used to collect, modify, and deliver data. Organizations use data pipelines to copy or move their data from one source to another so it can be stored, used for analytics, or combined with other data.

What are pipelines in programming? ›

On any Software Engineering team, a pipeline is a set of automated processes that allow developers and DevOps professionals to reliably and efficiently compile, build, and deploy their code to their production compute platforms.

Which is a good pipeline solution for ML project? ›

1. DVC. DVC, or Data Version Control, is an open-source version control system for machine learning projects. It's an experimentation tool that helps you define your pipeline regardless of the language you use.

How do you do a machine learning pipeline? ›

A typical machine learning pipeline would consist of the following processes:
  1. Data collection.
  2. Data cleaning.
  3. Feature extraction (labelling and dimensionality reduction)
  4. Model validation.
  5. Visualisation.

What are the three steps to create a data pipeline? ›

This process continues until the pipeline is completely executed. In addition, some independent steps might run in parallel as well in some cases. It usually consists of three main elements, i.e., a data source, processing steps, and a final destination or sink.

What is a 5 stage pipeline? ›

Those stages are, Fetch, Decode, Execute, Memory, and Write. The simplicity of operations performed allows every instruction to be completed in one processor cycle.

What is a data pipeline SQL? ›

Data pipelines are processes that extract data, transform the data, and then write the dataset to a destination. In contrast with ETL, data pipelines are typically used to describe processes in the context of data engineering and big data.

Videos

1. Best Practices for Building a Cloud Data Pipeline
(Alooma)
2. Scale and Optimize Data Engineering Pipelines with Best Practices: Modularity and Automated Testing
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3. What are some common data pipeline design patterns? What is a DAG ? | ETL vs ELT vs CDC (2022)
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4. Best Practices for Building and Deploying Data Pipelines in Apache Spark - Vicky Avison
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5. Best Practices for Building a Fast and Reliable IoT Data Pipeline
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6. What To Consider When Building Data Pipelines - Intro To Data Infrastructure Part 2
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