What are the most popular batch processing frameworks for data engineering?

The 6th and very popular option used by many is Spring Batch. It can process high volumes of data with good amount of control

Here are the most popular batch-processing frameworks are: - Google Cloud Dataflow: A fully managed stream and batch processing service that is a part of the Google Cloud Platform. It supports Apache Beam pipelines. Components: Managed pipeline execution, auto-scaling, and dynamic work rebalancing. Strengths: Integration with other Google Cloud services, ease of use, and efficient resource management. Apache-Hadoop: One of the most established frameworks, Hadoop is an open-source platform that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Key Components: HDFS, YARN, and MapReduce. Strengths: Scalability, fault tolerance, and the ability to handle diverse data types.

Hadoop est l'un des frameworks de traitement par lots les plus populaires et les plus largement utilisés pour l'ingénierie des données. Il permet de stocker et de traiter de grands volumes de données distribuées sur un cluster de machines. Hadoop utilise le système de fichiers HDFS (Hadoop Distributed File System) pour stocker les données et le modèle de programmation MapReduce pour traiter les données.

Bill Gates once said, 'We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.' Apache Hadoop is a cornerstone for big data handling. It’s renowned for its scalability and fault tolerance, making it indispensable in high-throughput applications. For example, using Hadoop's HDFS and MapReduce, a team can efficiently process terabytes of data, ensuring reliability and scalability. Tools like Hive and Pig further extend its capabilities, making Hadoop a comprehensive solution for data engineering.

Azure Data Factory provides the orchestration services, it manages data pipelines for batch processing, integrating with various data storage and processing services in Azure along with the visual interface for designing data transformation processes. Databricks is a unified analytics platform based on Apache Spark, it provides a collaborative environment for data engineers and data scientists to process large-scale data with the delta lake, it enhances spark with ACID transactions and schema enforcement for reliable batch processing. AWS Glue is serverless ETL Service which Simplifies batch processing on AWS with built-in transformations, data cataloging, and integration with other AWS services.

Great features - In-memory processing, fault tolerance, scalability, and a rich ecosystem of libraries. Enables real-time data analysis without disk I/O bottlenecks.

It seems like most Data Engineers in my network have at least touched Spark at some point in the past few years. The question is, why is that the case? The primary advantages of Spark include its speed (it is faster than Hadoop in a lot of scenarios), the ability to script out queries in a few different languages (PySpark, SparkSQL, Scala, etc.), the ability to run several workflows at once, its real time features, and its great performance. Assuming that you are an experienced programmer, Spark is relatively simple to get adapted to (hard to master though). Therefore, it is something that can immediately be used in a variety of data engineering teams' workflows.

Spark's primary advantages lie in its exceptional speed, often outperforming Hadoop in numerous cases, and its flexibility in supporting multiple scripting languages such as PySpark, SparkSQL, and Scala. Moreover, Spark can manage multiple workflows concurrently and offers real-time processing capabilities along with superior performance. For seasoned programmers, Spark is relatively straightforward to learn, making it a versatile tool that can be quickly integrated into the workflows of various data engineering teams.

Spark est un autre framework populaire pour le traitement par lots de données. Il est conçu pour être plus rapide que Hadoop et prend en charge le traitement en temps réel ainsi que le traitement par lots. Spark utilise le concept de RDD (Resilient Distributed Datasets) pour stocker les données en mémoire et prend en charge plusieurs langages de programmation, notamment Scala, Java, Python et R.

Flink est un framework de traitement de données en temps réel et par lots. Il prend en charge le traitement de flux de données en continu ainsi que le traitement par lots. Flink est conçu pour être évolutif et peut traiter de grands volumes de données en temps réel. Il prend en charge plusieurs langages de programmation, notamment Java, Scala et Python.

Apache Beam offers a unified programming model for both batch and stream data processing, allowing developers to write pipelines that can run on multiple execution engines like Apache Flink, Apache Spark, and Google Cloud Dataflow. Additionally, Beam's rich set of built-in transformations and IO connectors simplifies the construction of complex data processing workflows, ensuring flexibility and scalability.

Beam est un framework unifié pour le traitement de données par lots et en temps réel. Il prend en charge plusieurs runners, notamment Apache Flink, Apache Spark et Google Cloud Dataflow. Beam permet aux développeurs de créer des pipelines de traitement de données portables qui peuvent s'exécuter sur différents systèmes de traitement.

Airflow is the very rich in functionality and developer friendly scheduler and pipelines to quickly authorize and run it in production. Airflow Dags code is python which gives the much flexibility in flow of pipeline runs Airflow have plethora of operators to interact with services in major cloud providers services like s3, snowflake, SQS etc. One of the most used feature as Airflow user is using the run and pause functionality of Dag task helps to debug pipelines in real time. Statistics available on Dag run time and performance help to improve the Dags performance. Also due to opensource it's easy to add new operators and lot of community support until other competitors like Mage get same support

Features of Airflow: DAGs (Directed Acyclic Graphs): Visualizes and defines batch workflows with clear task dependencies, ensuring orderly and efficient execution. Robust Scheduling: Offers advanced scheduling options, including cron-like schedules, to automate and time batch processes accurately. Retry Mechanism: Automatically retries failed tasks, enhancing the reliability and success rate of batch processing workflows. Scalability: Efficiently scales to manage increasing data volumes and complex workflows, making it suitable for growing business needs. Extensive Integrations: Seamlessly connects with various data sources, cloud services, and third-party tools, providing versatility in handling diverse batch processing tasks.

Airflow est un framework de workflow open source pour l'orchestration de tâches de traitement de données. Il permet de créer des workflows dirigés par des données (DAG) pour automatiser le traitement de données. Airflow prend en charge plusieurs langages de programmation, notamment Python, et peut être intégré à d'autres frameworks de traitement de données tels que Hadoop et Spark.

Apache Airflow is not a batch processing framework per se, but a workflow management system that can orchestrate and monitor batch jobs. Airflow allows data engineers to: Define workflows as directed acyclic graphs (DAGs), which consist of tasks and dependencies. Execute tasks on a schedule or on demand. Key Features: Workflow Definition: Use DAGs to organize tasks and their dependencies. Scheduling: Automate task execution on a defined schedule. Web Interface: Provides a user-friendly interface for managing workflows. Metadata Database: Stores metadata about workflows and task instances. Rich Set of Operators: Integrate with various data sources and destinations using built-in operators and hooks.

The most popular batch processing frameworks for data engineering include Apache Hadoop, Apache Spark, and Apache Flink. Apache Hadoop is renowned for its distributed storage (HDFS) and processing (MapReduce) capabilities, making it a staple in large-scale data processing. Apache Spark, known for its speed and ease of use, extends Hadoop's capabilities with in-memory processing and a rich API for complex data workflows, supporting languages like Java, Scala, Python, and R. Apache Flink, while supporting both batch and stream processing, excels in handling event-time processing and complex data stream operations, making it versatile for real-time analytics.

Serverless managed solutions have become popular due to their ease of use and cost-effectiveness. Google Cloud Platform's (GCP) Cloud Batch provides a fully managed service for running batch jobs at scale, without the need to provision or manage infrastructure. Similarly, AWS Batch on Amazon Web Services allows users to run batch computing workloads on various compute environments. Azure Batch on Microsoft Azure also offers a scalable platform for running large-scale parallel and high-performance computing (HPC) batch jobs. These serverless solutions handle much of the complexity of managing batch processing infrastructure, enabling data engineers to concentrate on developing and deploying their batch processing applications.