Airflow Hadoop: Critical Differences 101
An Apache Airflow platform allows workflows to be scheduled automatically. It schedules and executes complex workflows.
Table of Contents
In contrast, Apache Hadoop is an open-source framework that provides efficient storage and processing of large datasets ranging from gigabytes to petabytes. So what’s the difference between Airflow Hadoop?
While comparing Airflow Hadoop, you can analyze massive datasets in parallel by clustering multiple computers instead of using one large computer to store and process the data.
Let’s explore the differences between Airflow Hadoop in more detail!
Table of Contents
- What is Apache Airflow?
- Key Features of Apache Airflow
- What is Apache Hadoop?
- Key Components of Hadoop
- Differences between Airflow Hadoop
What is Apache Airflow?
Airflow is a new-age platform for designing, building, and monitoring workflows. This open-source ETL technology integrates easily with cloud services (such as Azure, Google Cloud Platform, and Amazon Web Services). Visualization is easy thanks to the interface’s easy-to-use nature. Scalability can be quickly achieved thanks to its modular architecture.
Apache Airflow was created to be a highly flexible task scheduler. Using Airflow, ML models can be trained, notifications can be sent, systems can be monitored, and power functions within APIs can be performed.
While Apache Airflow is adequate for most day-to-day operations (such as running ETL jobs and ML pipelines, distributing data, and so on), it is not the best option for performing streaming operations.
It aids in executing tasks on DAGs due to its modern UI, which is loaded with the best visualization elements. Pipelines, tracks, and bugs can all be easily visualized and repaired. Workflows are easy to manage because they are continuous and consistent.
How does Apache Airflow work?
Apache Airflow is used to schedule and orchestrate data pipelines or workflows. These data pipelines provide ready-to-use data sets for business intelligence applications, data science, and machine learning models that support big data applications. The orchestration of data pipelines refers to the sequencing, coordination, scheduling, and management of complex data pipelines from various sources.
These workflows are represented as Directed Acyclic Graphs (DAGs) in Airflow. To understand what a workflow/DAG is, consider the process of making pizza.
Workflows typically have an end goal, such as creating visualizations for sales figures from the previous day. The DAG shows how each step depends on several other steps that must be completed first. For example, you will need flour, oil, yeast, and water to knead the dough.
Similarly, ingredients are required for pizza sauce. In the same vein, you must first migrate your data from relational databases to a data warehouse to generate your visualization from the previous day’s sales.
The analogy also demonstrates that specific steps, such as kneading the dough and preparing the sauce, can be performed concurrently because they are not interdependent. You may need to load data from multiple sources to create visualizations.
Data pipelines that are efficient, cost-effective, and well-organized assist data scientists in developing better-tuned and more accurate ML models because those models have been trained with complete data sets rather than just small samples.
Key Features of Apache Airflow
Here are some key features of Apache Airflow:
1) Programmatic Workflow Management
Airflow includes options for creating programmatic workflows. Xcom and Sub-DAGs make it easier to create dynamic and complex workflows. Dynamics Dags, for example, can be easily configured based on the connections or variables defined in the Airflow UI.
The executors and operators can be easily defined, and the library can be extended to meet the abstraction level required by a specific environment.
3) Task Dependency Management
It’s excellent at dealing with various dependencies, such as dag running status, task completion, file/partition presence via a specific sensor, etc. It can even handle task dependency concepts like branching.
4) Monitoring & Management Interface
Airflow includes a monitoring and management interface. The various task statuses can be viewed immediately. It is also possible to initiate and terminate DAGs runs or tasks.
5) Automate your queries with Python Code
Airflow is equipped with several operators for executing codes. A wide range of databases can be accessed by its operators. Because it is written in Python, its PythonOperator enables rapid deployment of Python code.
The Benefits of Using Apache Airflow
- Community: Airbnb founded Airflow in 2015. Since then, the Airflow community has grown. There are over 1000 contributors to Airflow, and the number is growing at a healthy rate.
- Extensibility and Functionality: Apache Airflow is highly extensible, it can be tailored to any specific use case. Adding custom hooks/operators and other plugins allows users to quickly implement custom use cases without relying entirely on Airflow Operators. Airflow has already gained several features since its inception. Airflow, created by a team of data engineers, is a complete solution that addresses numerous data engineering use cases.
- Dynamic Pipeline Generation: Airflow pipelines are written in Python and can be configured dynamically. This enables the development of code that generates pipeline instances on the fly. We do not process data linearly or statically.
A dependency-based declaration is more closely related to airflow models than a step-based declaration. Steps can be defined in small units, but this quickly breaks down as the number of steps increases.
While comparing Airflow Hadoop, Airflow exists to aid in rationalizing this work modeling, which establishes linear flow based on declared dependencies. Versioning and accountability for change are two additional advantages of keeping pipelines updated with code.
While comparing Airflow Hadoop, Airflow is much better suited to supporting roll-forward and roll-back, offering more detail and accountability of changes over time. Even though not everyone uses Airflow in this manner, Airflow will change as your data practice does.
Pros of Apache Airflow
- Ease of use: Just a little python knowledge is required.
- Open-source community: It is free and has a large user community.
- Integrations: You can integrate Airflow with cloud platforms (such as Google, AWS, Azure, etc.) using ready-to-use operators.
- Standard Python coding: Enables the creation of flexible workflows without the need for specialized knowledge of other languages or frameworks.
- Graphical User Interface (GUI): Track and manage workflows, see the status of ongoing and finished tasks.
Cons of Apache Airflow
- It’s not possible to make quick, on-the-fly changes to workflows with Airflow, so you must be deliberate about what you do.
- Occasionally, there are glitches, especially when scaling. Furthermore, the web interface is obscure and requires a steep learning curve.
Why Apache Airflow Is the Best
Due to its focus on configuration as code for Airflow Hadoop, Airflow is among the best frameworks, especially among developers. According to its advocates, Airflow is distributed, scalable, flexible, and well-suited to orchestrating complex business logic.
While comparing Airflow Hadoop, Airflow is used by thousands of organizations, including Applied Materials, Disney, and Zoom, according to marketing intelligence firm HG Insights. As a commercial managed service, Amazon offers AWS Managed Workflows on Apache Airflow (MWAA). Managed Airflow services are also available from Astronomer.io and Google.
This is how Airflow makes a difference in Airflow Hadoop
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What is Apache Hadoop?
Hadoop, which was first released in 2006, was developed by software engineers Doug Cutting and Mike Cafarella to process large amounts of data. At first, it utilized the MapReduce programming model and processing engine, which Google had previously promoted in a technical paper in 2004.
Hadoop offers a way to split large data processing tasks into manageable chunks, perform local computations, and combine the outcomes. The distributed processing architecture of the framework makes it simple to create big data applications for clusters of hundreds or thousands of commodity servers, known as nodes.
How does Hadoop work?
Hadoop simplifies using all storage and processing capacity in cluster servers and executing distributed processes against massive amounts of data. Hadoop provides the foundation for other services and applications to be built.
Applications that collect data in various formats can place it in the Hadoop cluster by connecting to the NameNode via an API operation. The NameNode keeps track of the file directory structure and “chunk placement” for each file, which is replicated across DataNodes.
To query the data, provide a MapReduce job composed of many maps and reduce tasks that run against the data in HDFS distributed across the DataNodes. Map tasks are executed on each node against the supplied input files, and reducers are executed to aggregate and organize the final output.
While comparing Airflow Hadoop, The Hadoop ecosystem has expanded significantly throughout the years due to its extensibility. The Hadoop ecosystem now offers a wide range of tools and programs to gather, store, process, analyze, and manage big data.
Among the most widely used programs are:
- Spark: This is a widely used open source distributed processing system for big data workloads. With its support for general batch processing, streaming analytics, machine learning, graph databases, and ad hoc queries, Apache Spark provides quick performance through in-memory caching and optimized execution.
- Presto: This is an open-source, distributed SQL query engine for quick, on-the-fly data analysis. The ANSI SQL standard supports advanced queries, aggregations, joins, and window functions. Numerous data sources, such as the Hadoop Distributed File System (HDFS) and Amazon S3, can be processed by Presto.
- Hive: Allows users to use Hadoop MapReduce through an SQL interface, enabling distributed and fault-tolerant data warehousing and large-scale analytics.
- HBase: is an open-source, non-relational, versioned database that utilizes the Hadoop Distributed File System (HDFS) or Amazon S3 (using EMRFS). Designed for real-time, strictly consistent, random access to tables with billions of rows and millions of columns, HBase is a massively scalable, distributed big data store.
- Zeppelin: is a computerized notebook that enables real-time data exploration.
Key Components of Apache Hadoop
Here are some key components of Apache Hadoop:
HDFS, based on a file system developed by Google, manages the process of distributing, storing, and accessing data across multiple servers. It can handle structured and unstructured data, making it an excellent choice for developing a data lake.
YARN, which stands for Yet Another Resource Negotiator but is more commonly known by its acronym, is Hadoop’s cluster resource manager, in charge of executing distributed workloads. It schedules processing jobs and assigns compute resources to applications, such as CPU and memory. When YARN was added as part of Hadoop 2.0 in 2013, it took over those tasks from the Hadoop implementation of MapReduce.
3) Hadoop MapReduce
While YARN has reduced its role, MapReduce is still the built-in processing engine used in many Hadoop clusters to run large-scale batch applications. It orchestrates the process of dividing large computations into smaller ones that can be distributed across multiple cluster nodes, and then it executes the various processing jobs.
4) Hadoop Common
This is a collection of utilities and libraries used by Hadoop’s other components.
Pros of Hadoop
- Cost: Due to Hadoop’s open-source nature and use of commodity hardware, it has a cost-effective model, unlike traditional relational databases that require expensive hardware and high-performance processors to handle big data.
- Scalability: Hadoop is a model that is extremely scalable. A large amount of data is distributed across multiple low-cost machines in a cluster and processed in parallel. The number of these machines, or nodes, can be changed depending on the needs of the business.
- Flexibility: Hadoop is built in such a way that it can handle any type of dataset, including unstructured (images and videos), semi-structured (XML, JSON), and structured (MySQL data), very effectively. This makes it extremely flexible because it can process any type of data with ease, regardless of its structure.
- Speed: Hadoop manages its storage using a distributed file system. A large file is divided into small file blocks and distributed among the nodes in a Hadoop cluster using DFS (Distributed File System). This makes Hadoop faster because of how well it performs when compared to traditional database management systems.
- Fault Tolerance: Data in Hadoop is replicated across multiple DataNodes in a Hadoop cluster, ensuring data availability even if one of your systems fails.
Cons of Hadoop
- Vulnerability: Hadoop is a framework written in Java, and because Java is one of the most widely used programming languages, it is insecure because it can be easily exploited by any cyber-criminal.
- Low Performance In Small Data Surrounding: While Hadoop is primarily intended for handling large datasets, its efficiency suffers when used in small data environments.
Why Hadoop is the Best
While comparing Airflow Hadoop, with Hadoop, you can make a cost-efficient, reliable, and scalable distributed computing by using a clustered file system called HDFS, which is an open-source, Java-based implementation. As a fault-tolerant architecture, HDFS can be deployed on low-cost hardware.
Using the MapReduce programming model, Hadoop uses clusters of computers to store and process large data sets. By using the Hadoop YARN framework, you can schedule jobs, manage cluster resources, and monitor your Hadoop cluster through a web interface.
This is how Hadoop makes a difference in Airflow Hadoop
Differences between Airflow Hadoop
Here are the key differences between Airflow Hadoop:
Airbnb created Apache Airflow, a platform for programmatically authoring, planning, and monitoring data pipelines. Airflow can be used to create workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler runs your tasks on various workers while adhering to your specified dependencies.
Rich command line utilities make complex DAG operations a breeze. The intuitive user interface makes it simple to visualize pipelines in production, monitor progress, and troubleshoot issues as they arise.
On the other hand of Airflow Hadoop differences, Hadoop is an open-source platform for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that enables the distributed processing of large data sets across computer clusters using simple programming models.
It is intended to scale from a single server to thousands of machines, each providing local computation, and storage.
Differences between Apache Airflow Hadoop are simple, Apache Airflow is a workflow management system, whereas Hadoop is a framework for the distributed processing of large data sets. The common thing in the differences is that Apache Airflow Hadoop are open-source tools.
That is the fundamental distinction between Airflow Hadoop. We hope you now understand the differences and how these two technologies work.
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