What is a data pipeline?
A data pipeline is the structure that facilitates moving data from the applications or systems where it is stored. The pipeline loads it into a data storage facility, for example a warehouse or the cloud, where it can be transformed using a BI platform like Sisense. This process necessitates cleaning and preparing the data so that analysts, data scientists, business users, developers, and even customers can access it and derive meaningful intelligence from it.
Data is one of your company’s most important assets, and your future depends on how well you use it. That future will be built on countless datasets from apps, warehouses, the cloud, and all the other places that data is collected and stored. It’s the job of data engineers to build stable data pipelines that deliver the information to BI tools where analysts, users, and customers can extract the most value from it.
The data pipeline’s role is to bring together data from all the sources your company uses. This could be product data, user data, in-house data from various departments, and possibly even third-party data used to provide context to your own information. Data from certain cloud sources might incur charges for each access, according to your pricing structure; thus your pipeline should only query certain sources when necessary to control costs. Your data pipeline must be flexible enough to accommodate all these origins, but sturdy and reliable enough to seamlessly fuse your insights into one consolidated data source.
What is a big data pipeline?
The advent of big data brings the need for big data pipelines, which have the capacity to transfer enormous amounts of data. IDC’s Global DataSphere report estimates that the amount of data created over the next three years will be more than the data created over the past 30. Big data, in addition to its great size, also usually contains greater complexity. The big data pipeline must be agile enough to handle immense volumes of complex data, usually moving very quickly (think the three V’s: volume, velocity, and variety). This means that the code you used to create the pipeline should be able to scale with minimal, if any, changes as the data it facilitates scales. A big data pipeline may use batches, stream processing, or other methods to process your data.
Processing data through a big data pipeline traditionally starts with the collection of data from various sources, such as apps, Internet of Things devices, and website traffic data. Next, the ETL (extract, transform, load) process takes place to crunch the data and load it into a relational database, data warehouse, lake, or other holding tank, where it can be prepped for further analysis. Then, a BI platform like Sisense takes over and shapes the data into reports, dashboards, and other visualizations where intelligence can be derived and surfaced to users, either infused into workflows or into apps, products, and experiences.
Data pipeline vs. ETL
An ETL process is one kind of transport operation that can be performed by a data pipeline. This refers to the process of extracting information from a source, transforming it, and loading it into a data warehouse or other type of database.
There are a few key differences between ETL and data pipelines: ETL processes are typically set up to run in batches, while pipelines can stream in a continuous flow. This is especially useful for data that needs constant updating or to display in real time. ETL processes aren’t generally set up to handle the volumes of data that an analytics app will need to crunch in order to create value from it.
Also, while “transform” is a key step in the ETL process, it doesn’t necessarily have to be included in a data pipeline operation. The pipeline can just be set up to carry the data from one source to a destination, without transforming it along the way.
One way that the two processes are similar is that they can both handle structured or unstructured data. This gives them the flexibility to use a variety of data sources and storage facilities, and makes it easier to scale for larger projects.
Types of data pipeline solutions
There are as many data pipeline setups as there are data sources and types of storage solutions. But there are a few basic decisions to make in the initial phase that will drive the ways you manage and optimize your data.
First, if your data is in the cloud, you’ll want to shop for a cloud-native pipeline, optimized for cloud-based data. Using the cloud solution’s infrastructure and staff resources for your pipeline means you’ll be saving money over hosting it in-house.
Then, you’ll have to decide if you want to process your data in real time or set up batch processing. Batch is more appropriate if you have large volumes of data and you don’t need real-time results.
Finally, you can choose an open-source tool or a plug-in solution from a commercial vendor. Open source is cheaper, of course, but will require your organization to devote the development expertise to set up your pipeline as well as manage and maintain it.
Sisense makes pipeline management simpler for you, with an easy-to-use UI built for nontechnical staff. You can focus on pulling in data to build analytics apps, widgets, and dashboards that give your colleagues and customers what they need.
Sisense customer MindMax went with the commercial option, using Fivetran to plug in a fully managed, turnkey data pipeline solution that would organize the data from each of its web-based applications into a data warehouse that it could then analyze using Sisense.
MindMax partners with higher education institutions to increase enrollment, particularly among continuing education and adult learners. MindMax knew that the growth of its business depended on moving away from manual data extraction and toward a fully automated setup, so it could increase its customer base. Fivetran’s secure and reliable data pipeline operation allowed MindMax to gain maximum value and trust the results it was getting.
Setting up a reliable data pipeline doesn’t have to be time-consuming or complex.
Consider your options carefully, decide to build or deploy, designate your data sources and destinations, and then you’ll have your data flowing, giving you many opportunities to infuse analytics across your business.