Organizations must deliver personalized experiences across channels, products, and services at scale. The only way to do so is to leverage data. But simply having the data isn’t enough. Organizations need to build data pipelines that bring the data from where it’s created to where it can be used, in a format that’s actually useful.
But what exactly are data pipelines and why do they matter? Data pipelines are a way to collect, process, and move data between systems. They are important because they allow you to get your data sets out of source systems so they can be used for applications like business intelligence and data visualization.
In this post, we will define what a data pipeline is, discuss the benefits of using them, and compare data pipelines vs ETL (Extract-Transform-Load). We will also look at different types of data pipelines. By the end of this post, you should have a good understanding of what a data pipeline is and why you might want to use one.
What is a data pipeline?
A data pipeline is a set of continuous processes that extract data from various sources, transform it into the desired format, and load it into a destination database or data warehouse. Data pipelines can be used to move data between on-premises systems and cloud-based systems, or between different cloud-based systems.
Benefits of a data pipeline
Data pipelines are an essential element of an organization’s data strategy. Pipelines and data lineage describe how data flows from where it is created to where it is stored to where it is used. Often, data pipelines are automated and incorporate data governance policies and measures, creating a seamless, secure, and scalable means to consolidate data from multiple sources into a single repository like a cloud data warehouse where it can be exposed to business users through self-service analytics. In doing so, organizations can tap into insights that would otherwise be unavailable.
There are many benefits of using a data pipeline, including:
Improved data quality
Data pipelines can help to cleanse and standardize data as it is extracted from different sources. This can improve the overall quality of the data set, and make it more useful for analytics and decision-making.
Increased efficiency
Data pipelines can automate many of the processes involved in data consolidation, which can save time and resources.
Greater flexibility
Data pipelines offer a high degree of flexibility, allowing businesses to easily modify or add new processes as needed.
Improved scalability
Data pipelines can be easily scaled to accommodate increasing data volumes.
Data pipeline vs ETL
There is often confusion between modern data pipelines and traditional ETL pipelines. Both involve moving data from one place to another, but there are some key differences.
ETL stands for extract, transform, load. This type of pipeline process usually happens in batch mode, which means that it happens at regular intervals, such as once a day or once a week. The data is first extracted from the source system, then transformed into the desired format, and finally loaded into the destination system.
A modern data pipeline, on the other hand, creates a continuous process of moving data from one system to another. Data pipelines are typically used for streaming data, or data that is constantly changing, such as social media data or stock prices. In a data pipeline, the data is typically extracted in real-time or near real-time, then transformed, and loaded into the destination system.
There are several benefits of using a modern data pipeline over an ETL pipeline.
Near real-time: Modern data pipelines can enable near-real-time insights through real-time analytics because the data is processed in an always on manner. This is opposed to ETL processes, which can take several hours or even days to process the data.
Reliability: Modern data pipelines are often more reliable than ETL processes because they are designed to handle failures gracefully. In an ETL process, if one part of the process fails, the entire process fails. In a data pipeline, however, if one part of the process fails, the other parts of the process can continue to run. This makes data pipelines more resilient to failures.
There are some trade-offs to using a data pipeline over an ETL pipeline. First, data pipelines can be more complex to set up and maintain than ETL pipelines. This is because data pipelines typically involve more moving parts, such as message queues and stream processing systems. Second, data pipelines can be more expensive to run than ETL processes because they require more computing resources.
Data pipeline architecture examples
There are many different types of data pipeline architectures that can be used to move data from one place to another. Some of the most common include:
ETL (Extract, Transform, Load) data pipeline
ETLs are used to move data from a variety of sources into a centralized repository, such as a data warehouse. The data is extracted from the sources, transformed into a consistent format, and then loaded into the target repository.
Modern data stack ETLs: Matillion, Rudderstack, and Supermetrics
ELT (Extract, Load, Transform) data pipeline
ELT pipelines are similar to ETL pipelines, but the data transformation step is performed after the data is loaded into the target repository. This allows for more flexibility in how the data is transformed, but can also lead to increased complexity.
Modern data stack ELTs: Airbyte, Fivetran, and Talend
Data replication pipelines
Data replication pipelines are used to copy data from one repository to another, typically in real-time. This is often used for disaster recovery or to create a read-only copy of data for reporting and analytics purposes.
Modern data stack database replication tools: Hevo Data, Rubrik, and Veeam
Data streaming pipelines
Data streaming pipelines are used to move data in real-time from one system to another. This is often used for applications that need to process data as it is generated, such as financial trading systems or social media analytics.
Modern data stack data streaming tools: Apache Kafka, Redpanda, and Confluent
Choosing the right type of data pipeline architecture depends on your needs. For instance, a BI architecture integrates different technologies and BI tools to optimize existing analytical processes. On the other hand, a data modeling warehouse will use an architecture that allows users to set up appropriate databases and schemas so that the data can be transformed and then stored more easily. That's why, before making a decision, it is essential to consider different factors, including the size and complexity of your data, the frequency with which it needs to be moved, and the resources available to build and maintain the pipeline.
Get more out of your data
Data pipelines are an essential component of data architecture for modern organizations who want to make data-driven decisions. They allow you to collect, process, and store data. Data pipelines come in different shapes and sizes, but all serve the same purpose of getting your data from Point A to Point B (and sometimes C). At the end of the day, however, organizations still need to explore this data, find insights, and take action on them.
ThoughtSpot is a great tool for self-service business intelligence that can extend your data pipelines all the way to the point where decisions are made and impact is created. With ThoughtSpot, you can easily connect to any cloud data platform, combine multiple data sets, and visualize your data in seconds. And best of all, you can try it for free with our 30 day trial. So why wait? Sign up today and see how easy it is to get started with interactive data visualization and analysis.
