Did you know that Facebook stores over 1000 terabytes of data generated by users every day? That’s a huge amount of data, and I’m only talking about one application! And hundreds of quintillion bytes of data are generated every day in total.
With so much data being generated, it becomes difficult to process data to make it efficiently available to the end user. And that’s why the data pipeline is used.
So, what is a data pipeline? Because we are talking about a huge amount of data, I will be talking about the data pipeline with respect to Hadoop.
What Is a Data Pipeline in Hadoop?
A data pipeline is an arrangement of elements connected in series that is designed to process the data in an efficient way. In this arrangement, the output of one element is the input to the next element.
If that was too complex, let me simplify it. There are different components in the Hadoop ecosystem for different purposes. Let me explain with an example.
Consider an application where you have to get input data from a CSV file, store it hdfs, process it, and then provide the output. Here, you will first have to import data from CSV file to hdfs using hdfs commands. Then you might have to use MapReduce to process the data. To store data, you can use SQL or NoSQL database such as HBase. To query the data you can use Pig or Hive. And if you want to send the data to a machine learning algorithm, you can use Mahout.
These are some of the tools that you can use to design a solution for a big data problem statement. When you integrate these tools with each other in series and create one end-to-end solution, that becomes your data pipeline!
Now that you know what a data pipeline is, let me tell you about the most common types of big data pipelines.
Types of Big Data Pipelines
When you create a data pipeline, it’s mostly unique to your problem statement. But here are the most common types of data pipeline:
- Batch processing pipeline
- Real-time data pipeline
- Cloud-native data pipeline
Let’s discuss each of these in detail.
Batch Processing Pipeline
In this type of pipeline, you will be sending the data into the pipeline and process it in parts, or batches. This type of pipeline is useful when you have to process a large volume of data, but it is not necessary to do so in real time.
For example, suppose you have to create a data pipeline that includes the study and analysis of medical records of patients. If you are using patient data from the past 20 years, that data becomes huge. But it is not necessary to process the data in real time because the input data was generated a long time ago.
Real-Time Data Pipeline
You will be using this type of data pipeline when you deal with data that is being generated in real time and the processing also needs to happen in real time.
For example, stock market predictions. There are different tools that people use to make stock market predictions. To design a data pipeline for this, you would have to collect the stock details in real-time and then process the data to get the output.
Cloud-Native Data Pipeline
Cloud helps you save a lot of money on resources. It prevents the need to have your own hardware. In the cloud-native data pipeline, the tools required for the data pipeline are hosted on the cloud. This is useful when you are using data stored in the cloud. You can easily send the data that is stored in the cloud to the pipeline, which is also on the cloud.
You now know about the most common types of data pipelines. So, let me tell you what a data pipeline consists of.
Components of a Hadoop Data Pipeline
As I mentioned above, a data pipeline is a combination of tools. These tools can be placed into different components of the pipeline based on their functions. The three main components of a data pipeline are:
- Storage component
- Compute component
- Message component
Because you will be dealing with data, it’s understood that you’ll have to use a storage component to store the data. This storage component can be used to store the data that is to be sent to the data pipeline or the output data from the pipeline.
When it comes to big data, the data can be raw. You can’t expect the data to be structured, especially when it comes to real-time data pipelines. To handle situations where there’s a stream of raw, unstructured data, you will have to use NoSQL databases. The most important reason for using a NoSQL database is that it is scalable. If you have used a SQL database or are using a SQL database, you will see that the performance decreases when the data increases. NoSQL works in such a way that it solves the performance issue.
Some of the most-used storage components for a Hadoop data pipeline are:
This component is where data processing happens. You are using the data pipeline to solve a problem statement. And for that, you will be using an algorithm. The execution of that algorithm on the data and processing of the desired output is taken care by the compute component.
In Hadoop pipelines, the compute component also takes care of resource allocation across the distributed system. You can consider the compute component as the brain of your data pipeline.
Some of the most-used compute component tools are:
The message component plays a very important role when it comes to real-time data pipelines. Messaging means transferring real-time data to the pipeline. Some of the most used message component tools are:
Starting With Hadoop Data Pipeline
The reason I explained all of the above things is because the better you understand the components, the easier it will be for you to design and build the pipeline. Now that you know about the types of the data pipeline, its components and the tools to be used in each component, I will give you a brief idea on how to work on building a Hadoop data pipeline.
The first thing to do while building the pipeline is to understand what you want the pipeline to do. You have to understand the problem statement, the solution, the type of data you will be dealing with, scalability, etc. This phase is very important because this is the foundation of the pipeline and will help you decide what tools to choose.
Once you know what your pipeline should do, it’s time to decide what tools you want to use. Every data pipeline is unique to its requirements. It’s not necessary to use all the tools available for each purpose. For example, if you don’t need to process your data with a machine learning algorithm, you don’t need to use Mahout. So, depending on the functions of your pipeline, you have to choose the most suitable tool for the task.
After deciding which tools to use, you’ll have to integrate the tools. You have to set up data transfer between components and input to and output from the data pipeline.
Finally, you will have to test the pipeline and then deploy it. And that’s how a data pipeline is built.
To Sum Up
I hope you’ve understood what a Hadoop data pipeline is, its components, and how to start building a Hadoop data pipeline. It may seem simple, but it’s very challenging and interesting. You will know how much fun it is only when you try it. So go on and start building your data pipeline for simple big data problems.
This post was written by Omkar Hiremath. Omkar uses his BA in computer science to share theoretical and demo-based learning on various areas of technology, like ethical hacking, Python, blockchain, and Hadoop.fValue Streams in Software: A Definition and Detailed Guide