Data pipeline

The efficient flow of data from one location to the other is one of the most critical operations in today’s data-driven enterprise.

Indeed, data flow can be precarious due to the variety of things that can go wrong during the transportation from one system to another :

• Data can become corrupted

• It can hit bottlenecks

• Data sources may conflict and/or generate duplicates

In this sense, by eliminating many manual steps from the process, data pipelines enables a smooth, automated flow of data from one station to the next.

But what is a data pipeline ?

A data pipeline automates the processes involved in extracting, transforming, combining, validating and loading data for further analysis and visualization. Furthermore, it can process multiple data streams at once and provides end-to-end velocity by eliminating errors and combatting bottlenecks or latency.

In short it is an absolute necessity for today’s data-driven enterprise.

Also one of the advantages of a data pipeline rely in the fact that it views all data as streaming data and allows for flexible schemas. Indeed, regardless of whether it comes from a static sources (like a flat-file database) or from real-time sources (such as online retail transactions),  data pipeline divide each data stream into smaller chunks that it process in parallel on order to confer extra computing power.

How is data pipeline different from ETL ?

ETL stands for Extract, Transform and Load. ETL systems extract data from one system, transform the data and load the data into a database or data warehouse. Legacy ETL pipelines typically run in batches, meaning that the data is moved in one large chunk at a specific time to the target system. Typically, this occurs in regular schedule d intervals; for example, you might configure the batches to run at 12 a.m. every day when the traffic is low.

By contrast “data pipeline is a broader term that encompasses the ETL as a subset. It refers to a system for moving data from one system to another. The data may or may not be transformed, and it may be processed in real time instead of batches.

Who needs a data pipeline ?

While a data pipeline is not a necessity for every business, this technology is especially helpful for those that :

• Generate, rely on, or store large amounts or multiple sources of data

• Maintain siloed data sources

• Require real-time or highly sophisticated data analysis

• Store data in the cloud

Types of data pipeline solutions

The most popular types of pipelines available are :

• Batch : Batch processing is most useful for when. You want to move large volumes of data at a regular interval, and you do not need to move data in real time.

         Example : Marketing data

• Real-time : These tools are optimized to process data in real time. Real-time is useful when you are processing data from a streaming source, such as the data from financial markets or telemetry from connected devices.

• Cloud native : These. Tools are optimized. To work with cloud based data, such as data from AWS buckets. These tools are hosted in the cloud, allowing you to save money on infrastructure and expert resources because you can rely on the infrastructure and expertise of the vendor hosting your pipeline.

• Open source : These tools are most useful when you need a low-cost alternative to a commercial vendor and you have the expertise to develop or extend the tool for your purposes.