A successful Big Data Pipeline transmits data efficiently, reducing pauses and blockages between various tasks, keeping each process operational. The Apache Airflow provides a customizable ecosystem for developing and managing data pipelines. Moreover, it eliminates the need for a collection of tools and various homegrown processes. Using real-time features and instances we will understand how Big Data Pipeline with Apache Airflow simplifies and automates data pipelines, minimize operational costs, etc. And how easily integrate all the technologies within the stack.

Furthermore, data pipelines are useful to extract transform and load (ETL) data to and through different sources.

Today, it is easier for us to build a data pipeline that can efficiently scale with the size of data as the important technologies within the Big Data environment are all open-source and free to use.

Therefore, we can use any kind of technology to store data, whether it could be a powerful Hadoop cluster or a trusted RDBMS (Relational Database Management System). Moreover, connecting it to a fully-active pipeline is a project. But, it may reward with invaluable insights. A big data pipeline should be composed of the three different technologies such as; Apache Airflow, Spark, and Zeppelin. This can be useful to easily integrate with the highest range of data architectures.

Let Airflow organize things for big data pipeline

Apache airflow is one of those important technologies that are useful & easy to put in place that offers extended capabilities. The workflow management system that was first introduced by Airbnb has gained maximum popularity. Due to its powerful UI feature and its effectiveness through the use of Python is much useful.

Moreover, the Airflow relies on four basic elements that help it to simplify any data pipeline. These are:

DAGs (Directed Acyclic Graphs)

Airflow uses the DAG concept to build batch jobs in the best and efficient way. Moreover, using DAGs the user may have a good number of chances to build his data pipeline most probably.

Tasks

The tasks are another part of the data pipeline building. The Airflow’s DAGs are divided into different tasks. And all of the work happens using the code that the user writes within these tasks. (It allows us to do anything within an Airflow task)

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X-COM

Within a wide range of business cases, the nature of the data pipeline may require to share information between the different tasks. Besides, using Airflow it can be easy to do through the use of X-COM functions. These functions mostly rely on Airflow’s database to store data we need to share from one task to another.

Scheduler

Unlike other workflow management tools under the Big Data universe, the Airflow contains its scheduler that makes building the pipeline much easier.

Moreover, using an Airflow server and scheduler up running is just left a few commands away to process. Therefore, within a few minutes, the user could find navigating the easy UI of his Airflow web-server.

The next step includes connecting Airflow to the database or data management system. Furthermore, the Airflow offers a direct possible way to do that through the use of the User interface. And it’s all the user needs to perform to have an up and running Airflow server combined within the data architecture. Now the user can use its powerful capacity to manage the big data pipelines by managing the existing pipelines through Airflow’s DAGs workflow.

Now Spark will perform the hard work

The Apache Spark no longer needs an introduction, as it’s a part of the distributed data-processing framework.

As long as the user runs it on a cluster relevant to the size of the data, the Spark offers extremely fast processing system. Moreover, through Spark SQL, it allows the user to query his data if he was using SQL or Hive-QL.

Now it’s all the user needs to perform is to use Spark within the Airflow job to process the data relevant to his business needs. For instance, using the pySpark SQL and then using Airflow’s Python Operator for various tasks is better to execute the Spark works directly in the Airflow Python activities.

Using a Zeppelin server

Zeppelin is a dynamic form. A Zeppelin dashboard becomes an efficient tool that offers users who don’t know to write a line of code instantly and they could have complete access to the companies information.

Just like Apache Airflow, Setting up a big data pipeline with the Zeppelin server is very easy. Besides, the user needs to configure the Spark interpreter program so that he can run PySpark scripts in the Zeppelin server. This will run on the data he builds using the Airflow-Spark pipeline.

In addition to this, Zeppelin offers a large number of interpreters or programs that allow it to run different types of scripts.

After loading the information, displaying it through different visualization types is instantly done. Moreover, it uses multiple paragraphs of the note to display this.

Here, the Spark SQL provides similar things to all of the operations that may be present within the user queries. Therefore, the transition will be seamless with no doubt. Using PySpark to restructure the information according to the needs and then using its extreme processing power to calculate different sum ups, lets the user could store its output on his database, using the Airflow hook.

 Look at the results using Zeppelin

Apache Zeppelin is another technology that the Apache Software contains, is gaining huge popularity these days. Moreover, by its use of the notebook became the go-to visualization tool within the Hadoop ecosystem.

Using Zeppelin allows the user to display data dynamically and in real-time. And it is also displayed using the form that the user creates within a zeppelin dashboard.  Thus, the user could easily create dynamic scripts that use the forms’ input to run a specialized set of operations on a dataset.

And with the release of Zeppelin’s new version, the users could now enhance its capabilities (like adding custom displays) using Helium, a new plugin system.

To integrate Zeppelin within the pipeline, the user needs to configure the Spark interpreter at first. Moreover, if the user prefers to access the data calculated with Spark using his database instead, that’s also possible with the use of the respective Zeppelin interpreter.

Final Thoughts

That’s all for the user to know about the usage of the Airflow, Spark, and Zeppelin to perform and running Big Data pipeline. Moreover, it allows him to extract and display a large amount of information. Start the process by putting an Airflow server that organizes the pipeline. Then rely on a Spark cluster to further process and gather the data. And finally, let Zeppelin guide the process through the different stories that information speaks. In this world, there is no such piece of information that can’t be useful for any work. Besides, every piece of information stored on the computers of your company is valuable.

So, learn the usage of various tools and techniques to build a successful data pipeline with our ITGuru experts. To get practical insights to build similar pipelines at your own, step into the Big Data Online Training by ITGuru. And learn through the expert’s voice dynamically. This learning may help to scale up your existing skills to build a successful career.

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