There is inconsistency in data types supported by different file formats. Support for mixed file formats – With existing solutions, if you rename a column in one file format (say Parquet, ORC, or Avro), you get a different behavior than if you rename a column in a different file format.ACID transactions, streaming upserts, file size optimization, and data snapshots – Existing tools that support these features lock you into specific file formats, complicating interoperability across the analytics ecosystem.In addition, you have to rewrite queries to use the new partition column in your table. Different query patterns on the same data – If you change the partitioning to optimize your query after a year, say from daily to hourly, you have to rewrite the table with the new hour column as the partition.Historically, schema changes required expensive backfills and redundant ETL processes. Now there is data written in both schemas. That rename operation has effectively dropped a column and added a new column. The consuming analytics tool now can’t read it because the metastore can’t track former names for columns. For instance, say a data engineer renames a column and writes some data. Continuous schema evolution – Simple DDL commands often render the data unusable.Common workarounds (such as rewriting all the data in all the partitions that need to be changed at the same time and then pointing to the new locations) cause huge data duplication and redundant extract, transform, and load (ETL) jobs. ![]() For huge tables, it’s not practical to use global locks and keep the readers and writers waiting. If you’re overwriting a partition, for instance, you might delete several files across partitions without having any guarantees that you will replace them, potentially resulting in data loss. ![]() Consistent table updates across multiple files or partitions – With Hive tables, writing to multiple partitions at once isn’t an atomic operation.This limitation is even more telling in real-time streaming workloads. Concurrent writes on the same dataset – Table formats relying on coarse-grained locks slow down the system.Reader-writer isolation – When a job is updating a huge dataset, another job accessing the same data frequently works on a partially updated dataset, leaving the data in an inconsistent state.Consistent reads and writes across multiple concurrent users – There are two primary concerns:.We’re increasingly seeing the following requirements (and challenges) emerge as mainstream: You can now keep one central copy of your data and share it with multiple user groups that run analytics and even make in-place updates on a data lake. This enables you to bring in data from multiple sources (for example, transactional data from operational databases, social media feeds, and SaaS data sources) using different tools, and each data source has its own transient EMR cluster to perform transformation and ingestion in parallel. Modern data lake challengesĪmazon EMR integrates with Amazon Simple Storage Service (Amazon S3) natively for persistent data storage, and allows you to independently scale your data in Amazon S3 and compute on your EMR cluster. ![]() You can also find this notebook in your EMR Studio workspace under Notebook Examples. You can access this sample notebook from the GitHub repo. Additionally, we provide a step-by-step guide on how to get started with an Iceberg notebook in Amazon EMR Studio. We also discuss how Iceberg solves these challenges. In this post, we discuss the modern data lake requirements and the challenges-including support for ACID transactions and concurrent writers, partition and schema evolution-that come with these. Iceberg adds tables to compute engines including Spark, Trino, PrestoDB, Flink, and Hive using a high-performance table format that works just like a SQL table.Īmazon EMR release 6.5.0 and later includes Apache Iceberg so you can reliably work with huge tables with full support for ACID (Atomic, Consistent, Isolated, Durable) transactions in a highly concurrent and performant manner without getting locked into a single file format. Iceberg adds functionality on top of that to help manage petabyte-scale datasets as well as newer data lake requirements such as transactions, upsert/merge, time travel, and schema and partition evolution. Table formats typically indicate the format and location of individual table files. Amazon EMR is a cloud big data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto.Īpache Iceberg is an open table format for huge analytic datasets.
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