At the Docker container level – You can customize an EMR on EKS image by packaging Delta dependencies into a single Docker container that promotes portability and simplifies dependency management for each workload.The JAR files will be downloaded and distributed to each Spark Executor and Driver pod when starting a job. At the application level – You install Delta libraries by setting a Spark configuration spark.jars or -jars command-line argument in your submission script.There are two ways to make it available in EMR on EKS: See Amazon EMR on EKS release versions for the list of supported versions and applications.Īs of this writing, Amazon EMR does not include Delta Lake by default. The runtime binary files of these frameworks can be found in the Spark’s class path location within each EMR on EKS image. To find out the latest and past versions that Amazon EMR supports, check out the Hudi release history and the Iceberg release history tables. For this demonstration, we use EMR on EKS release 6.8.0, which contains Apache Iceberg 0.14.0-amzn-0 and Apache Hudi 0.11.1-amzn-0. Custom library dependencies with EMR on EKSīy default, Hudi and Iceberg are supported by Amazon EMR as out-of-the-box features. We also show how to deploy the ACID solution with EMR on EKS and query the results by Amazon Athena. In this post, we walk through a simple SCD2 ETL example designed for solving the ACID transaction problem with the help of Hudi, Iceberg, and Delta Lake. We have to shorten the file retention period to reduce the data scan and read performance.The performance of extract, transform, and load (ETL) jobs decreases as all the data files are read each time. This leads to the creation of a large volume of append-only files. We track every single activity at source, including duplicates caused by the retry mechanism and accidental data changes that are then reverted.We don’t have an isolation guarantee whenever multiple workloads are simultaneously reading and writing to the same target contact table.Consistency and atomicity aren’t guaranteed because we just dump data files from multiple sources without knowing whether the entire operation is successful or not.We keep creating append-only files in Amazon S3 to track the contact data changes (insert, update, delete) in near-real time.Assume we centralize customer contact datasets from multiple sources to an Amazon Simple Storage Service (Amazon S3)-backed data lake, and we want to keep all the historical records for analysis and reporting. Let’s try to understand the data problem with a real-world scenario. For example, how do we run queries that return consistent and up-to-date results while new data is continuously being ingested or existing data is being modified? One of the most common challenges is supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions in a data lake. It’s up to the downstream consumption layer to make sense of that data for their own purposes. Unlike traditional data warehouses or data mart implementations, we make no assumptions on the data schema in a data lake and can define whatever schemas required by our use cases. In analytics, the data lake plays an important role as an immutable and agile data storage layer. As an example, we demonstrate how to handle incremental data change in a data lake by implementing a Slowly Changing Dimension Type 2 solution (SCD2) with Hudi, Iceberg, and Delta Lake, then deploy the applications with Amazon EMR on EKS. We focus on how to get started with these data storage frameworks via real-world use case. In this post, we explore three open-source transactional file formats: Apache Hudi, Apache Iceberg, and Delta Lake to help us to overcome these data lake challenges. Implementing these tasks is time consuming and costly. Another challenge is making concurrent changes to the data lake. Due to the flexibility and cost effectiveness that a data lake offers, it’s very popular with customers who are looking to implement data analytics and AI/ML use cases.ĭue to the immutable nature of the underlying storage in the cloud, one of the challenges in data processing is updating or deleting a subset of identified records from a data lake. Additionally, you can run different types of analytics against your loosely formatted data lake-from dashboards and visualizations to big data processing, real-time analytics, and machine learning (ML) to guide better decisions. You can keep your data as is in your object store or file-based storage without having to first structure the data. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale.
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