![]() ![]() 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. Let’s go ahead and generate a map of catalog properties to configure our RestCatalog.A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Note: To learn more about the file-io abstraction in Iceberg, check out one of our earlier blog posts that provides an excellent overview: Iceberg FileIO: Cloud Native Tables. ![]() We’ll use a Minio container that’s S3 compatible. Two properties commonly required by all catalogs are the warehouse location and the FileIO implementation. We’re using a REST catalog where we just have to point to the service. The properties required vary depending on the type of catalog you’re using. To load a catalog, you first have to construct a properties map to configure it. ![]() It’s even possible to plug in your own catalog implementation to inject custom logic specific to yourįor this walkthrough, we will use the RestCatalog that comes with Iceberg. Iceberg comes with many catalog implementations, such as REST, Hive, Glue, and DynamoDB. If you already have the tabulario/spark-iceberg image cached locally, make sure you pick up the latest changes by running docker-compose pull.Ī catalog in Iceberg is an inventory of Iceberg namespaces and tables. The easiest way to try out the java client is to use the interactive notebook Iceberg - An Introduction to the Iceberg Java API.ipynb, which can be found using the docker-compose provided in one of our earlier blog posts: Docker, Spark, and Iceberg: The Fastest Way The Iceberg java client provides valuable functionality to enable working with Iceberg tables. Whether you’re a developer working on a compute engine, an infrastructure engineer maintaining a production Iceberg warehouse, or a data engineer working with Iceberg tables, This blog post is the first part of a series that covers the underlying Java API available for working with Iceberg tables without an engine. With Iceberg’s integration into a growing number of compute engines, there are many interfaces with which you can use its various powerful features.
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