What is the best batch processing technology for your project?

The Hadoop ecosystem operates as a federation of various tools, and its key success lies in the capability to integrate numerous big data tools. This includes processing, storing, and visualizing tools that can seamlessly connect and communicate within a distributed system. This functionality greatly aids in managing data effectively on an on-premise platform, providing all the necessary tools for improved data management.

Description: An open-source framework for distributed storage and processing of large data sets. Use Cases: Well-suited for processing large-scale data across a cluster of machines.

Over the years, Hadoop has undergone several generations, each bringing new features, upgrades, and adjustments to meet the shifting demands of massive data processing. MapReduce is the main batch-processing programming model from the first generation (Hadoop 1). One JobTracker for task scheduling and work coordination, managed by a single resource manager. Second Generation Hadoop 2: Multiple applications can share resources thanks to YARN (Yet Another Resource Negotiator), a resource manager that distinguishes between processing and resource management. Third Generation (Hadoop 3): Improved Performance: Integration with containerization technologies for better resource isolation. Enhanced HDFS Federation, GPU Support.

There are several tools available to use depending on the technical stack you can leverage at your organization. 1) Informatica Power Center / Datastage / SSIS - These are very popular etl tools for batch processing especially when working with relational databases and have been in use for a long time. 2) Hadoop - Hive utility can be used to write sql and schedule the queries for batch processing. 3) Teradata BTEQ - A native utility of Teradata that can be used for batch processing with in Teradata platform. 4) Databricks notebooks - Write either SQL or Python or a combination of both and schedule the notebooks for batch processing. 5) Spark - Can be used for both for real time as well as batch processing.

Determining the optimal batch processing technology for a project necessitates a nuanced evaluation based on specific requirements and objectives. Factors such as data volume, processing speed, and scalability play pivotal roles. Traditional tools like Apache Hadoop offer robust parallel processing, while newer frameworks like Apache Spark bring speed and versatility. Cloud-based solutions, exemplified by AWS Batch or Google Cloud Dataflow, provide scalability and cost-efficiency. Ultimately, the "best" technology hinges on project intricacies, emphasizing the need for a tailored approach that aligns seamlessly with the project's unique demands.

Sans aucun doute, Spark se distingue comme l'outil le plus robuste pour la transformation de données massives, surtout dans le contexte actuel où les données proviennent de diverses sources, présentant des formats différents et une vélocité variée. Il s'impose comme un outil polyvalent, offrant une robustesse remarquable dans tous les contextes.

Connu pour sa polyvalence, Spark est adapté à une variété de charges de travail, y compris le traitement par lots. Il offre des performances élevées grâce à son architecture de traitement en mémoire.

Na minha experiência, em um cenário de Big Data com grandes volumes de dados o Apache Spark na maioria das vezes foi uma excelente opção. Importante experimentar outras alternativas e realizar experimentações com outras soluções para verificar a que melhor atende a instituição.

The best tech depends on your project's needs. Think size, speed, and budget. For massive data crunching, Hadoop might work. For quicker processing and flexibility, Spark is a champ. Flink shines for real-time insights. Cloud services like AWS Batch and Google Cloud Dataflow offer managed solutions.

In my experience, choosing the best batch processing technology depends on project requirements. Apache Spark has often proven versatile, offering scalability, fault tolerance, and compatibility with various data sources. Its unified approach for batch and stream processing makes it a go-to choice for complex data workflows. However, the optimal choice also considers factors like project scope, data volume, and integration needs. Evaluating specific project requirements will guide the selection of the most suitable batch processing technology.

As a data engineer working with massive datasets and ETL pipelines, I often rely on Apache Spark for my batch processing needs. Apache Spark's in-memory distributed computing shines for rapid batch processing at scale, fluidly parallelizing analytical workloads often not possible on single machines. Careful config tuning avoids resource pitfalls for performant pipelines. Code libraries keep development flexible across Python, Java, Scala. Foundational for scaling analyst models and data apps. When architected right, Spark unlocks acceleration and cost-effective analytics not achievable through other distributions. The art is aligning infrastructure to current & future needs.

An open-source distributed computing platform called Apache Spark offers a quick and flexible cluster computing framework for handling large amounts of data. Provides quick, in-memory data processing that is appropriate for interactive data analysis and iterative algorithms. Supports a wide range of data processing tasks across numerous languages, including Scala, Java, Python, and R. The extensive MLlib (Machine Learning Library) offered by Apache Spark is made to make machine learning activities on big datasets easier to understand and scale. Because of its speed, adaptability, and simplicity of usage, It is now a well-liked option for businesses handling large-scale data processing and analytics tasks.

Há bastante tempo uso o Apache Spark em projetos de engenharia de dados, e aprecio especialmente sua API de alto nível, que permite escrever scripts de processamento de dados em Python usando PySpark. Além disso, a ampla adoção do Spark no mercado facilita sua integração com diversas soluções.

Description: A fast and general-purpose cluster-computing framework that supports in-memory data processing. Use Cases: Suitable for iterative algorithms, interactive data mining, and processing large-scale data.

Designed with low latency and high throughput capabilities for both batch and stream processing. supports exactly-once semantics and event-time processing.

Description: A stream processing framework with batch processing capabilities, designed for high-throughput and low-latency. Use Cases: Ideal for real-time analytics and event-driven applications.

AWS Batch seamlessly integrates with a spectrum of AWS services, including Glue for ETL, Lambda for serverless computing, Kinesis for real-time streaming, Lake Formation for secure data lakes, S3 for scalable storage, Redshift for data warehousing, and EMR for big data processing. It extends compatibility to RDS and EC2 for relational databases and compute, Athena for query analytics, CloudWatch for monitoring, Step Functions for workflow automation, DynamoDB for NoSQL storage, and SNS/SQS for messaging. IAM ensures robust access control, and support for Keyspaces caters to Apache Cassandra. This broad ecosystem integration underscores AWS Batch's technical prowess, offering a comprehensive solution for diverse batch processing needs.

Purpose-built service for running batch computing workloads. It dynamically provisions the optimal quantity and type of compute resources, based on the volume and specific resource requirements of your batch jobs.

An Amazon Web Services (AWS) batch processing service that is completely managed. Uses containerization (Docker) to make batch jobs easier to scale, and it interfaces with other AWS services.

Description: An open-source platform for orchestrating complex workflows and data pipelines. Use Cases: Great for managing and scheduling batch processing tasks in a systematic way.

It is obviously very specific to use case and one should always try to check as many options as possible before selecting any technology. I think Dataflow is a useful technology for batch processing because it is fully managed, serverless & scalable. Also it integrates well with Composer, GCS & Big Query. A combination of solution involving GCS bucket + Dataflow + Big query is common as well as useful to create batch processing workfkow (With complex transformations that can't be easily done using SQL on Big Query)

Description: A cloud-based big data platform that uses popular frameworks like Hadoop and Spark. Use Cases: Provides a scalable and managed environment for processing large amounts of data on the AWS cloud.

There's no single "best" batch processing technology, as it depends heavily on your use case-specific needs and requirements. The ideal choice for any use case is based on factors like - Data size and complexity (example: Massive datasets, smaller data sets) - Processing requirements ( basic transformations like filters, joins, aggregations, or iterative, complex ML algorithms) - Latency needs (near real-time or batch scheduled at specific intervals of days/times) - Finally, project budget and skills requirements All the above-mentioned technologies are popular and well-known choices. So, always keep these factors in mind while choosing the technology. Most of the time, you use a combination of two or more technologies.

It is easy to overthink a technology choice. They all have their pros and cons. As a rule, I use the following when making a technology choice. 1) What end-to-end architecture does the technology need to fit into? 2) What are the team's existing skills? Who will implement the technology? 3) What DevOps support exists? Kubernetes, Serverless, On-Premise? 4) When looking at an Open Source solution, is the project active? 5) For the data being processed, think of the three Vs: volume, variety, and veracity. Do you need streaming, micro-batch, or other 6) How easy will each one be to support, debug, and develop? Can I use automated deployment with any of them Finally, what use-cases, now and in the future will it need to support

When choosing a batch processing technology for your project, consider the size and complexity of your data, as well as the frequency of your batch jobs and your budget. If you have a large volume of data or highly structured data, you will need a batch processing technology that can handle the scale and complexity of your data.

In my experience, there is no size fits all technology and it really depends on the data that is being processed . In my case where we needed to migrate large datasets from csv to parquet , Spark was the winner . In other projects where data was streamed on the minute , Kafka ,though not easy to harness , was the right choice . I would look deeply at the data to be batch processed and might end up using 2 or more technologies within the same project pipelines .

Description: A Unified Analytics Platform that integrates with Apache Spark and provides collaborative environments for data science, analytics, and machine learning. Use Cases: Suitable for organizations looking for an integrated platform for data processing, analytics, and machine learning. When choosing a batch processing technology, consider factors like scalability, ease of use, community support, integration capabilities, and your team's familiarity with the technology. It's often beneficial to prototype with a few options and assess how well they meet your project's specific requirements before making a final decision.

The best approach for any batch processing is to think how to avoid it. 1. Start with good data model and 2. An automated (non-batched) ingestion setup. If your raw data has a stable schema (and you should always fight for this), then you can build anything on top of it. Reverse ETL, Data Marts, boring DWH, real-time DWH, or whatever you need. For processing the data, I recommend a SQL-based approach with a tool that knows all FROM sources and all SELECT outputs, i.e. the entirety of your data solution. And not just the „DAG“, but all schemas too. I currently use DBT for this, which supports batching through incremental update declarations. But even better are (plain|incremental|materialized) SQL views. No need to batch anything then.