Fast-paced businesses need to see through a forest of data to make decisions. The problem is that there are myriad platforms and teams that collect and manage data. Each team uses different tools, which leads to siloed data, and often teams spend massive amounts of time trying to stay on top of things instead of making progress.
Extracting timely insights from your company data is crucial yet challenging. This is where data pipelines come in. With modern data pipelines, companies can move faster, maximize the potential of their data, and make better decisions as they scale.
What is a data pipeline?
Think of a physical pipeline, like the one below. See how it’s made up of different sections that are connected together to facilitate the movement of the material inside? That’s exactly how a data pipeline works.
A data pipeline is a series of actions that move data from multiple sources to a preferred destination. It automates multi-step processes including aggregating, cleaning, formatting, processing, visualizing, and analyzing data as it moves from source to destination. Once this data flow is loaded into a data warehouse or data lake, multiple teams benefit from it.
Some use cases are as follows:
- Your marketing team manages an upcoming personalized campaign deployment by creating a data pipeline for all their required assets.
- When a prospect engages with a sales representative, you move them from your marketing qualified lead (MQL) pipeline to your sales qualified lead (SQL) pipeline. Once that’s done, you automatically trigger an email campaign to deliver resources and helpful guides that increase the likelihood of the prospect converting into a paying customer.
A modern data pipeline replaces repeatable tasks that are typically performed manually or with the help of computerized processes that require human intervention. In essence, the data pipeline automates the steps involved in data processing, data migration, and data transformation. This includes loading raw data from a data source into interim storage and then transforming it into useful formatted data before finally moving it into a destination.
Conceptually, data pipelines are easy to grasp. They consolidate data from disparate sources, eliminate data silos, and boost efficiency—while improving results. Now let’s dive into the factors that determine the effectiveness of a data pipeline.
Characteristics of a modern data pipeline
A modern data pipeline helps you streamline your data transformation processes without overloading your IT team (and in a cost-effective manner). Aside from ease of use, there are other factors that you should consider while building your data pipelines.
We’ve listed some of the most important characteristics of a modern data pipeline that you should look for:
1. Real-time data processing and analytics
Traditionally, organizations used batch processing for ETL (extract, transform, load) operations, which resulted in a stale data stream that was hours or days old. This lag in data stream processing could lead to missed opportunities, including less effective marketing campaigns, lower conversions, and lost potential sales.
Contrarily, a modern data pipeline sources, collects, manages, and analyzes data in (near) real time so teams can quickly access insights from across their tools for better decision making. Your data pipelines should be able to react to events with webhooks, like a file being delivered or an action being taken on a SaaS platform you use. This feature further reduces the latency, providing up-to-date data to your systems and users.
2. Streamlined data pipeline development
Modern data pipelines are built using tools that offer great accessibility to users so they can build customizable data pipelines to meet their needs. To use your data to its fullest potential, you want to build data pipelines in a way that works for anyone in the business touching data—regardless of their experience with coding. Low-code or no-code solutions are usually going to be the most effective way to start driving value with data pipelines immediately.
3. Self-service management
Legacy data pipeline architecture used to require a lot of ongoing maintenance. Setting up configurations and integrating with new platforms and applications was a time-intensive process.
However, modern data pipelines are designed so users can independently build workflows for their specific needs without depending on an IT team. They can easily connect different tools including software as a service (SaaS), data integration platforms, cloud data warehouses, and data lakes to create and manage pipelines in a self-service manner.
4. Robust monitoring functionality
Having a robust, fault-tolerant architecture in your data pipeline is critical as you move data, as it enables you to track failures and errors quickly. Modern data pipelines significantly reduce the risk of human error and data inconsistencies and keep your data secure.
They are designed with built-in monitoring functionality that automatically triggers alerts and sends notifications in the event of any pipeline failure, trigger failure, or other error in linked services (data sources, data warehouses, data lakes, etc.).
5. Scalable cloud-based architecture
Modern data pipelines offer instant agility and cloud elasticity to allow users to automatically scale, compute, and store resources as needed. They dedicate independent computer resources to ETL workloads, which enables them to handle complex, large volumes of data transformations while maintaining high quality and performance.
Given that modern data pipelines can provide immediate provisioning when data sets expand, businesses can easily handle spikes and growth. For instance, you can add more data processing and storage capacity within minutes during a seasonal sales spike or an aggressive marketing campaign.
Benefits of a data pipeline
The overarching benefit of data pipelines is a streamlined, automated flow of data from a source to a destination. This translates to consistent data quality and faster data analysis for critical decisions.
For example, your development team can create a continuous delivery pipeline to keep track of all product updates. Or they can run, save, or schedule API requests that can help you import CRM data or run a series of requests.
Advantages of data pipelines:
- Eliminating tedious, repeatable tasks that are done either manually or with some form of human intervention
- Bringing data from fragmented sources into a centralized repository
- Offering increased visibility, thereby reducing search time across files, platforms, and other data source systems
- Providing data in a unified way and removing the need for familiarity with dozens of tools
- Identifying, tracking, and managing critical metrics (and separating the strategic insights from the operational noise)
- Greatly reducing unnecessary communication and helping to implement standardization across teams
- Quickly improving overall productivity by automating processes
Build a data pipeline with Shipyard
The first step to building a data pipeline is to identify the existing challenges or bottlenecks in your business operations. Once that’s established, you need a reliable and powerful tool that enables data ingestion and management.
Shipyard offers pre-built automated data connectors that support dozens of data sources, including Amazon Redshift, dbt Core, Airtable, Google BigQuery, and Snowflake. Building a data pipeline in Shipyard is quick and easy—users can modify pipelines directly in the drag-and-drop interface. The code-agnostic platform allows advanced users such as data engineers and data scientists to deploy code faster in whatever language suits their requirements.
Run thousands of pipelines at once—Shipyard dynamically scales to meet your demands. Low-code templates take care of the basics so you can manage more critical work. You can also diagnose issues and address root causes in real time so unexpected errors don’t interrupt your business functions or impact your customers.
Get started building data pipelines with Shipyard for free.
