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Offline Data Pipeline Best Practices Part 2:Optimizing Airflow Job Parameters for Apache Hive

This post series is about mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. In Part 1 of our series explored the strategies for enhancing Airflow data pipelines using Apache Hive on AWS EMR. Our primary objective was to attain cost efficiency and establish effective job configurations. In this concluding Part 2, we will extensively explore Apache Spark, another pivotal element in our comprehensive data engineering toolkit. By optimizing the Airflow job parameters specifically for Spark, there is a substantial potential for enhancing performance and realizing substantial cost savings.

Why Apache Spark in Airflow?

Apache Spark is a really important framework and tool for data processing in companies all about data. It's genuinely outstanding at processing massive amounts of data quickly and efficiently. It's especially great for complex data analytics with fast query performance and advanced analytics capabilities. This makes Spark a preferred choice for enterprises handling vast amounts of data and requiring real-time analytics.

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Offline Data Pipeline Best Practices Part 1:Optimizing Airflow Job Parameters for Apache Hive

Welcome to the first post in our exciting series on mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. This post focuses on elevating our data engineering game, streamlining your data workflows, and significantly cutting computing costs. The need to optimize offline data pipeline optimization has become a necessity with the growing complexity and scale of modern data pipelines.

In this kickoff post, we delve into the intricacies of Apache Airflow and AWS EMR, a managed cluster platform for big data processing. Working together, they form the backbone of many modern data engineering solutions. However, they can become a source of increased costs and inefficiencies without the right optimization strategies. Let's dive into the journey to transform your data workflows and embrace cost-efficiency in your data engineering environment.

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Four Different Ways to Write in Alluxio

Alluxio is an open-source data orchestration system for analytics and AI workloads. Distributed applications like Apache Spark or Apache Hive can access Alluxio through its HDFS-compatible interface without code change. We refer to external storage such as HDFS or S3 as under storage. Alluxio is a new layer on top of under storage systems that can not only improve raw I/O performance but also enables applications flexible options to read, write and manage files. This article focuses on describing different ways to write files to Alluxio, realizing the tradeoffs in performance, consistency, and also the level of fault tolerance compared to HDFS.

Given an application such as a Spark job that saves its output to an external storage service; Writing the job output to the memory layer in a colocated Alluxio worker will achieve the best write performance. Due to the volatility of memory, when a node in Alluxio goes down or restarts, any data in that node’s memory is lost. To prevent data loss, Alluxio provides the ability to write the data to the persistent under storage either synchronously or asynchronously by configuring client-side Write Types. Each Write Type has benefits and drawbacks associated with it. Applications that write to Alluxio storage should consider the different write types and perform a cost-benefit analysis to determine the write type that is best suited for the application requirements.