What is a Data Pipeline? Examples and Elements

“With data collection, ‘the sooner the better’ is always the best answer.” 

— Marissa Mayer

 

What is a Data Pipeline?

 

Data pipeline consists of tools and activities that help the data to move from source to the destination. It includes the storage and the processing of the data. Data pipelines are automated and collect the data themselves from a variety of different sources and then modify the collected data and send it for analysis. 

 

In case some data has to be stored for future purposes then pipelines store it. Let us take a simple example on how the data pipelines work. Suppose you have a lot of data related to the customers and how they use your product and get in touch with your brand or products. This data can be their location, purchase histories, feedbacks and anything. 

 

What you do is you create customer profiles and feed all the relevant data accordingly. Now because of the analytical tools it is very easy to process the data and extract the relevant data for making decisions. 

 

These decisions can be strategic or operational. With the help of data pipelines all this data will flow and all the people responsible will get the data. Marketers, data scientists, managers, workers are some of those people that might need to extract data.

 

Here is another example to understand the concept of data pipelines better. Suppose a pizza delivery makes a pizza at their cafe and give it to their delivery guy to transport it so that you get it. 

 

A data pipeline is like a transporter of pizza that delivers you your pizza. Data is moved from one system to another through a pipeline. Just like pizza delivery guys, pipelines deliver the required data to the people. It is not the perfect metaphor but it will help you get the main purpose of using a data pipeline i.e data transmission. 

 

Also Read | What is Data Validation? Types, Benefits and Drawbacks

 

Examples of Data Pipeline

 

Here are some of the examples of a Data Pipeline:

 

1. User Groups

 

All the customer related information is very valuable for the company. Right from the POS to the feedback, all the data related to customers will help companies to grow and promote their products in a better way.  

 

Data pipelines are used for all these purposes as all the core data can be extracted and transmitted to the company. There are so many tools that help to understand what the customer wants so that the company can offer it.

 

2. Ad Analytics

 

You must have seen many ads on social media platforms and when you click on them you are directed to the page of the website of the brand. If the ad is influential enough it will convert into a sale and the customer will complete the purchasing. 

 

But to check whether the ad is enough we need to gather data and analyze it. For all the data movements involved in this process we need a data pipeline. It will help you check the revenue that you have earned along with the engagement. 

 

In case customers are facing difficulties in visiting the website through the ad then that can also be corrected. In short, in ad analytics data pipelines are used a lot and it has a lot of benefits.

 

3. Microservices

 

Microservices are a new concept. These services are for a very specific purpose. Just like debugging or improving the speed of the task. In this case, the data used is shared between many small applications. 

 

This increases the dependency on separate applications and complexity also increases. In order to remove these complexities data pipelines are needed so that data moves efficiently between systems and microservices such that productivity is not hampered in any way. 

 

Also Read | Guide to Data Profiling

 

Elements of Data Pipeline

 

“Without a systematic way to start and keep data clean, bad data will happen.” 

— Donato Diorio

 

In a data pipeline the data moves from one source to another source. During this transmission the data gets modified, analyzed, transformed and even optimized. This data is finally used for business insights and purposeful decision making. 

 

All the steps involved in aggregating data, moving data or even organizing data has the role of data pipeline in it. All the manual stages involved in the data processing aur converted to automated form via a data pipeline. 

 

Data pipeline when integrated with Business Intelligence is the best tool to gain a competitive advantage in business. There are 3 main elements of a Data Pipeline that are listed below:

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Elements of Data Pipeline

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1. Sources

 

Source is the place from where the data comes. There are so many database management systems from where the source data can be collected. It includes- MYSQL, CRMs, ERPs like SAP and ORACLE. Apart from these many IoT softwares are there and there social media management tools as well.

 

2. Processing Steps

 

Once the source data is extracted, collected and modified as per the needs of the business the next step is to send it to the destination. For this the processes involved are- augmentation, transformation, grouping and filtration etc.

 

3. Destination

 

After the data is processed, the last step is reaching the destination. The destination can either be a data warehouse or a data lake. Here the data is analyzed.

 

These three are the main elements in the data pipeline. But there are small elements involved in each of these main elements. Let us understand about them.

 

4. Dataflow

 

When the data moves from origin to destination it is called Dataflow. It includes the changes done to the data as well.

 

5. Storage

 

During the dataflow in the pipeline, the data is stored and preserved at many places. It depends on the volume, type, issues and the use of the data that where and when it is stored. The storage places are just like the bookstores.

 

6. Workflow

 

Workflow is the complete sequence of the data and how it moves through the data pipeline. In the workflow, there are 3 main concepts. One is the job- it is what is done to the data. Job is a specified task. 

 

Second one is Upstream- it is the source from where the data enters the data pipeline. 

 

Lastly Downstream- It is the opposite of upstream. It is the flow of the data to the final destination. 

 

Just like how water flows in a pipeline similarly the data flows in the data pipeline. First we need to take utmost care of the upstream flow and then look into the downstream flow.

 

7. Monitoring

 

Monitoring is basically acting as a vigil to take care of the data in the data pipeline and keeping a check on what errors can possibly arise. Checking the accuracy of data, consistency of data and whether or not information is lost during the transmission. All this is checked by monitoring.

 

Also Read | 10 Tools for Data Analytics

 

Data Types in Data Pipeline and Types of Data Pipeline

 

“Data really powers everything that we do.” 

— Jeff Weiner

 

The main function of the data pipeline is to send the data gathered from multiple sources for analysis. Pipeline contains many layers of filters that protect the data against any threat or failure. 

 

You can see various organizations using data pipelines to fight competition in the market by gaining a competitive advantage through data integration. 

 

There are multiple data types that can be used in the data pipeline by the organizations. Let us discuss them one by one.

 

1. Raw Data

 

Raw Data just like the name suggests is data that has not been processed. Also known as primary data, raw data can contain anything from numbers, pictures, videos, text and even audios. It is very difficult to understand raw data as it has so many irregularities. 

 

2. Cooked Data

 

Cooked data is also a type of raw data which has been processed through the system. While processing this raw data has been organized and extracted in the system itself. Sometimes cooked data is stored and analyzed for future purposes and uses as well.

 

3. Processed Data

 

Processed data is basically raw data that has been processed by the systems and converted into meaningful information. This information does not consist of many irregularities and it is easy to understand by the reader. The data pipeline helps transport this processed data to multiple locations.

 

4. Structured Data and Unstructured Data

 

There are basically two data types in a data pipeline- structured and unstructured. Structured data sticks to a predefined manner and it can be analyzed quickly. 

 

Whereas unstructured data is not organized and contains a huge volume of texts, numbers. Because of this unorganized set of data it is difficult to interpret or analyze this data.

 

Just like we read about the types of data in the data pipeline. There are 4 types of data pipeline:

 

1. Batch Pipeline

 

When a company deals with a large amount of data it has to process it using a batch processing method. In the case of a batch data pipeline, the data cannot be transferred on a real-time basis. Many big companies use Batch data pipeline to integrate data into bigger systems for marketing purposes.

 

2. Real-time Data Pipeline

 

Real time data pipeline helps to process data on a real time basis. This type of data pipeline is used by companies that are associated with financial markets or need to transport data from a streaming location.

 

3. Cloud Data Pipeline

 

Cloud based Data pipelines are a great way to save money on infra and other resources. In this the company has to depend on the host that provides the cloud services for everything. In order to collect information, cloud providers expertise is very important.

 

4. Open source Data Pipeline

 

Open source data pipelines are a cheaper version and cost-effective versions of transmitting data. The tools used here are cheaper than the other market tools. This type is readily available in the market therefore people can adjust and modify it accordingly.

 

Also Read | Data Processing

 

Overall we can say that a data pipeline is a very efficient way to gather data from multiple locations and then analyze it. Pipeline helps to cut down any information lost during the transfer or extraction. If you are using a data pipeline in your business then you can benefit a lot.