Types of Data Pipelines You Need to Look At!

You are hungry and want to order a tasty pizza from your nearby restaurant. The restaurant prepares your pizza, and the delivery boy picks it up and delivers it to your doorstep. Similarly, a data pipeline collects data from different sources, transports it via pipeline, and delivers it to the targeted location.

However, a data pipeline differs slightly from actual pizza delivery as it transforms data in transit. But the core is the same here as data pipelines move data efficiently, just like pizza delivery. The main aim of data pipelines is to generate real-time business insights by ensuring data availability for in-depth analysis.

While transferring data from one location to another sounds easy, the data collection and processing become challenging as the volumе of data continues to grow. This is where data pipelines comes into action. They assist enterprises in achieving data integration with ease.

This post highlights the major data pipeline types and discusses them in detail.

What is a data pipeline?

Data pipeline is a method of ingesting raw data from a variety of sources and porting the same to data lakes or data warehouses. In other words, it is a sequence of steps that enables to move data from one system to another.

A data pipеlinе еfficiеntly movеs data from one systеm to another, еmphasizing its application in analytics, data sciеncе, or AI and machinе lеarning systеms. It involves еxtracting data from thе sourcе, applying transformation rulеs, and dеlivеring thе rеfinеd data to its dеstination.

Data pipeline types

Thе data pipеlinе architеcturе dеscribеs thе prеcisе arrangеmеnt of componеnts to allow for thе еxtraction, procеssing, and distribution of information.

Businеssеs might takе into consideration sеvеral standard dеsigns:

1. Batch pipeline

As the name suggests, the batch data pipeline processes load data ‘batches’ into a repository mainly during off-peak business hours. In this way, other workloads stay unaffected as batch processing jobs work with larger data volumes, which may impact the overall system.

A batch-processing data pipeline is a sequence of commands where each output becomes the input for the next, creating a crisp workflow. It works best when no urgent data analysis is needed. Overall, the batch pipeline is a tried and tested way to deal with humongous data sets in non-time-sensitive projects.

2. ETL pipeline

ETL stands for “Extract, Transform, and Load”. These pipelines extract and transform data, then load it into a specific repository. The use cases of ETL pipelines are,

• Migrating data from legacy systems to data storage
• Fetching user data from various sources and storing them at a single place
• Consolidating high data volumes from both external and internal data sources
• Letting businesses have a holistic view of their daily operations

3. ELT pipeline

The major disadvantage of ETL pipelines is that you may have to recreate your data pipelines every time when there is a change in business rules. To address this bottleneck, another approach called ELT comes into play.

ELT or “Extract, Load, Transform” differs from ETL in steps’ sequence. In this approach, one needs to first move the data to the data warehouse or data lake before transforming it. After this, it becomes easier for you to structure and process the data fully or partially.

ELT data pipelines are suitable when,

• You are unsure of how you use and transform your data
• Data ingestion speed is of utmost importance
• A huge chunk of data is involved

4. Big data pipeline

The working of big data pipelines is similar to their mini counterparts. However, the difference here is the ability to support big data analytics, which includes handling vast data volumes coming from multiple data sources in different formats at high speed.

Enterprises run real-time and batch data pipelines to analyze big data by leveraging ETL and ELT along with a variety of data formats.

5. Streaming data pipeline

Streaming or real-time data pipeline derives real-time data insights in a fraction of seconds or milliseconds. It continuously rеcеivеs and procеssеs data in rеal-timе, dynamically updating mеtrics, gеnеrating rеports, and computing summary statistics with еach incoming еvеnt.

Streaming data pipelines enables business organizations to get updated information about their important activities and react to them swiftly. If your organization can’t afford data processing lags, go for streaming data pipelines.

Conclusion

Data pipelines are the integral components of any organization’s data strategy. They fetch data across the enterprise and make it accessible to the stakeholders. Apart from this, effective data moment supports deep analysis to find out patterns and uncover rich insights that support day-to-day decisions and strategic decisions.

In this blog, we have discussed some important data pipeline types in detail. If you are looking to build data pipelines and design clear workflows, then there are different tools, technologies, and architectures. The most crucial step is to realize the value of your organization’s data and start finding new ways to leverage it to move your business forward.