Today, businesses all around the world are driven by data. This has led to companies exploiting every available online application, service, and even social platform to extract data to gain a deeper understanding of the changing market trends. Now, this data requires numerous complex transformations to get ready for Data Analytics. Moreover, companies require technologies that can transfer and manage huge quantities of data in real-time to the desired destinations. Data Pipelines and ETL (Extract Transform & Load) Pipelines are 2 such technologies that are in high demand among businesses to manage their ever-increasing data.
This blog will introduce both Data and ETL Pipelines and provide their simple applications. It will further provide you with the key difference between these 2 tools that can help you understand the Data Pipeline vs ETL Pipeline discussion. Furthermore, it will explain the ideal use cases of both of these technologies. Read along to learn more about Data Pipeline vs ETL Pipeline comparison and choose which is best for you!
Table of Contents
- What is a Data Pipeline?
- Examples of Data Pipeline Applications
- What is an ETL Pipeline?
- Examples of ETL Pipeline Applications
- Data Pipeline vs ETL Pipeline: Key Differences
- When to Use Data Pipeline vs ETL pipeline
What is a Data Pipeline?
Data Pipeline acts as the medium using which you can transfer data from the source system or application to the data repository of your choice. The architecture of a Data Pipeline is made up of software tools that collaborate to automate the data transfer process. A Data Pipeline can contain multiple sub-processes including data extraction, transformation, aggregation, validation, etc. It is an umbrella term for all the data-related processes that can take place during data’s journey from source to destination.
The term “Data Pipeline” can refer to any combination of processes that ultimately transfer data from one location to another. This implies a Data Pipeline doesn’t need to transform the data during its transfer. This separates a Data Pipeline from an ETL Pipeline which is also a specific type of Data Pipeline. Generally, any of the subprocesses like Data Replication, Filtering, Transformation, Migrations, etc can be present in any sequence as part of a Data Pipeline.
Examples of Data Pipeline Applications
Data Pipelines are beneficial for your business teams as they save time effort and resources. Moreover, Data Pipelines automate their data transfer task and offer them a real-time flow of data to any required destination. The following examples are of use cases that a Data Pipeline can support but are not achievable via any ETL Piepleines:
- Providing you with real-time reporting services.
- Providing you with the facility to analyze data in real-time.
- Allowing you to trigger various other systems to operate different business-related processes.
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What is an ETL Pipeline?
Your organization generates and collects vast data quantities every day. Now, to gather any actionable insight from this enormous sea of data, you will need to:
- Extract and collect data spread across numerous sources that are in some way relevant to your business.
- Perform Data Cleaning and Transformations on the extracted data to make it suitable for Data Analysis.
- Load the transformed datasets into your chosen repository such as a Data Lake or a Data Warehouse and build a single source of truth.
Now, these processes work in a sequence to optimize your raw data into a form that is fit for analysis. However, if you are carrying out the above processes manually, various errors can occur. Your process code may throw sudden errors, certain data values may go missing, data inconsistencies can occur, and many other similar bottlenecks are possible when working with a manual ETL approach.
Businesses rely on an ETL pipeline to automate the three-step task and securely transform their data. An ETL Pipeline is made up of a group of tools that work to extract raw data from different sources, transform & aggregate the raw data and finally load it to your destination storage. A good ETL Pipeline also offers you end-to-end management coupled with an error handling mechanism.
Examples of ETL Pipeline Applications
ETL Pipeline finds applications in various fields depending on their subtasks of Extracting, Transforming, and Loading data. For example, if a company requires data present in different Web Services, Customer Relationship Managers (CRMs), Social Media Platforms, etc., it will deploy an ETL Pipeline and focus on the extraction part.
Similarly, the Data Transformation aspect of an ETL Pipeline is essential for applications that need to modify their data into a reporting-friendly format. Furthermore, Data Loading finds applications in tasks that require you to load vast datasets into a single repository that is accessible to every stakeholder. This implies, that an ETL Pipeline can have a multitude of applications based on its various sub-processes.
Data Pipeline vs ETL Pipeline: Key Differences
The term Data Pipeline and ETL Pipelines are almost used synonymously among Data Professionals. However, these 2 pipelines differ in terms of their usage, methodology, and importance. You can get a better understanding of the Data Pipeline vs ETL Pipeline discussion by comparing the following differences:
- Data Pipeline vs ETL Pipeline: Basic Concepts
- Data Pipeline vs ETL Pipeline: Importance
- Data Pipeline vs ETL Pipeline: Working Methodology
Data Pipeline vs ETL Pipeline: Basic Concepts
The first step in the Data Pipeline vs ETL Pipeline discussion is to understand their basic objectives and structure.
The key objective of a Data Pipeline is to simply transfer the data from the sources such as online applications & business processes to its storage destination like Data Warehouses. Even if no transformation is applied to the data during this transfer process, then also the Data Pipeline’s work is complete. Moreover, a Data Pipeline does not have to always end with the Data Loading step. Instead, a Data Pipeline can trigger webhooks to initiate new processes which can use the data present in such a pipeline.
In contrast, an ETL Pipeline must always extract data, transform it, and then load it into the desired target system. Such a pipeline needs to operate in this fixed sequence and following all the three steps of extraction, transformation, and loading is critical to its success. Furthermore, an ETL Pipeline always ends with the Data Loading step as it needs to store the data in a Data Warehouse where Business Intelligence tools can access it seamlessly.
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Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.
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- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Data Pipeline vs ETL Pipeline: Importance
Since Data Pipelines can be formed by the collection of any type of transformative or real-time process working in any sequence, their applications are wide and are present in many verticals of the data-driven industry. Moreover, Data Professionals who require fast analysis and real-time streams of data, rely on simple Data Pipelines which do not require any complex Data Transformations.
ETL Pipelines on the other hand hold great importance for BI professionals and Data Analysts who need to gather a vast sea of scattered data in its raw form and deliver it together as a single source of truth. This process of centralizing the relevant data can simplify the work of your Business Teams as they can access data in their required format and create detailed Data Visualisations easily. An ETL Pipeline can also free up your Development Team to work on other high-priority tasks instead of getting stuck looking for insights into fragmented customer data.
Therefore, the Data Pipeline vs ETL Pipeline discussion does not weigh one tool in terms of importance. Instead, both of these pipelines have their importance depending upon your use case.
Data Pipeline vs ETL Pipeline: Working Methodology
Depending on the subprocesses involved, your Data Pipeline can consume data either in batches or as real-time streams which involves managing events as they occur. This gives it an edge over ETL Pipelines in the Data Pipeline vs ETL Pipeline discussion. In the case of real-time data streaming, data is processed at a speed that is manageable by the destination storage or application. Moreover, you can experience live data updates using a simple Data Pipeline. For example, companies that work with sensor-based data implement Data Pipelines to transfer regular data traffic to their Data Warehouses or any other system.
Contrarily, your ETL Pipeline can generally work only with data batches. Each of these batches can be of equal sizes and the ETL processes are applied to each batch individually. The key thing to remember is that all the batches are prepared over some time and are then processed together according to the pipeline’s capacity. For example, if a company’s ETL Pipeline runs once every twelve hours, it can collect data throughout the day and then perform the transformation and loading processes at once.
When to Use Data Pipeline vs ETL Pipeline
The Data Pipeline vs ETL Pipeline discussion is the ideal use case for both of these pipelines. This section elaborates on the situations in which using these pipelines is favorable.
Data Pipelines in general are beneficial for companies that need to transfer their data in the fastest possible manner without much hassle. Such pipelines can provide simple and low latency consuming way of transferring consistent & well-structured chunks of data for analysis. Furthermore, Data Engineers can easily consolidate data from multiple sources and transfer it systematically to desired locations. For instance, an AWS Data Pipeline empowers you to transfer data freely between AWS on-premises data and alternative storage resources.
ETL Pipelines are ideal for companies that need to gather the right data, transform it into a reporting-friendly form, and store it as a single source of truth. This combined data is then beneficial for Data Analysis and Data Visualization. Developers that work with ETL tools can automate the mundane and complex task of Data Transformation and focus on development work. This allows Development Teams to channel their resources to take a company forward instead of getting stuck in managing huge chunks of data every day.
That’s it! You now have an understanding of the Data Pipeline vs ETL Pipeline comparison and can choose the pipeline that suits your work the most!
The article introduced you to Data Pipelines and ETL Pipelines and explained their simple application. It also provided 3 key differences to help you conclude the Data Pipeline vs ETL Pipeline discussion. Moreover, the article elaborated on the ideal use cases of both pipelines to further elaborate on the Data Pipeline vs ETL Pipeline in the current context. This article attempted to explain the difference between Data Pipelines and ETL Pipelines, terms which are often used interchangeably.Visit our Website to Explore Hevo
Now, to run queries or perform Data Analytics on your raw data, you first need to export this data to a Data Warehouse. This will require you to custom code complex scripts to develop the ETL processes. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ sources to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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