Table Format Partitioning Comparison: Apache Iceberg, Apache - Dremio This repository holds sample code for the blog: Get a quick start with Apache Hudi, Apache Iceberg and Delta Lake with EMR on EKS. This indexing mechanism is extensible and scalable to support any popular index techniques such as Bloom, Hash, Bitmap, R-tree, etc. It achieves high performance and scalability by using several techniques: Iceberg suits many use cases requiring reliable and efficient data management and analytics over large-scale data lakes. Iceberg has no solution for a managed ingestion utility, and Delta Autoloader remains a Databricks proprietary feature that only supports cloud storage sources such as S3. The solution provides two sample CSV files as the data source: initial_contact.csv and update_contacts.csv. for charts regarding release frequency. In the chart above we see the summary of current GitHub stats over a 30-day period, which illustrates the current moment of contributions to a particular project. Delta Lake 2.1.x is compatible with Apache Spark 3.3.x. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. For more details, check out the tutorial on GitHub. Unlike immutable data, our CDC data have a fairly significant proportion of updates and deletes. Hudi allows you the option to enable a, for query optimization (The metadata table is now on by default. Query an earlier version of a table. She is an experienced analytics leader working with AWS customers to provide best practice guidance and technical advice in order to assist their success in data transformation. All three of these technologies, Hudi, Delta, Iceberg have different origin stories and advantages for certain use cases. To create external tables, you must be the owner of the external schema or a superuser. The following is the Delta code snippet to load initial dataset; the incremental load MERGE logic is . Keep in mind Databricks has its own proprietary fork of Delta Lake, which has features only available on the Databricks platform. Partitions are tracked based on the partition column and the transform on the column (like transforming a timestamp into a day or year). In this article we will compare these three formats across the features they aim to provide, the compatible tooling, and community contributions that ensure they are good formats to invest in long term. What is the difference between a data lake with HDFS or S3 in AWS? While this enabled SQL expressions and other analytics to be run on a data lake, It couldnt effectively scale to the volumes and complexity of analytics needed to meet todays needs. Watch the sessions on-demand that include topics such as Data Mesh and Iceberg. It contains AWS Glue permissions because we use the Data Catalog as our metastore. June 9, 2022 2 mins read Delta Lake is an open-source storage layer that brings reliability to data lakes. They both have active and helpful communities and support available. As a one-off task, there should be two tables set up on the same data: The full version code is in the delta_scd_script.py script. Iceberg enables great functionality for getting maximum value from partitions and delivering performance even for non-expert users. The critical innovation of Iceberg is its table format. This is a huge barrier to enabling broad usage of any underlying system. Weve been busy incorporating Hudi into our CDC transaction pipeline and are thrilled with the results. Use the vacuum utility to clean up data files from expired snapshots. Below are references to relevant benchmarks: Databeans worked with Databricks to publish a benchmark used in their Data+AI Summit Keynote in June 2022, but they misconfigured an obvious out-of-box setting. Data lakes are centralized repositories that allow you to store all your data in its original form without pre-defining its structure or schema. It provides concurrency controls that ensure atomic transaction with our Hudi and Iceberg tables. When you choose which format to adopt for the long haul make sure to ask yourself questions like: These questions should help you future-proof your data lake and inject it with the cutting-edge features newer table formats provide. Which format has the most robust version of the features I need? Metadata structures are used to define: What is the table? Delta Lake also supports ACID transactions and includes SQ, Apache Iceberg is currently the only table format with. All of these transactions are possible using SQL commands. The Hudi community has made some seminal contributions, in terms of defining these concepts for data lake storage across the industry. The plan is to have 100% of the platform using Iceberg by the end of the first quarter of 2021, according to Kowshik. What are the major differences between S3 lake formation governed tables and databricks delta tables? Store the initial table in Hudi, Iceberg, or Delta file format in a target S3 bucket (curated). In December 2022 Hudi and Iceberg merged about the same # of PRs while the number of PRs opened was double in Hudi. Its up to the downstream consumption layer to make sense of that data for their own purposes. The majority of data engineers today feel like they have to choose between streaming and old-school batch ETL pipelines. It can achieve something similar to hidden partitioning with its, feature which is currently in public preview for Databricks Delta Lake, still awaiting, Every time an update is made to an Iceberg table, a snapshot is created. The data is being processed by running a Spark ETL job with EMR on EKS. Apache Hudis approach is to group all transactions into different types of actions that occur along a timeline. Iceberg helps data engineers tackle complex challenges in data lakes such as managing continuously evolving datasets while maintaining query performance. One feature often highlighted for Apache Iceberg is hidden partitioning that unlocks what is called partition evolution. This distinction also exists with Delta Lake: there is an open source version and a version that is tailored to the Databricks platform, and the features between them arent always identical (for example SHOW CREATE TABLE is supported with Databricks proprietary Spark/Delta but not with open source Spark/Delta at time of writing). Using Apache Iceberg tables - Amazon Athena This info is based on contributions to each projects core repository on GitHub, measuring contributions which are issues/pull requests and commits in the GitHub repository. We also show how to deploy the ACID solution with EMR on EKS and query the results by Amazon Athena. along with updating calculation of contributions to better reflect committers employer at the time of commits for top contributors. What sets a data platform apart from data formats are the operational services available. I recommend. Delta Lake Before comparing the pros and cons of each format, lets look into some of the concepts behind the data lake table formats. Some of them are: In the following sections, we will compare Apache Iceberg and Delta Lake on these parameters and help you decide which is best suited for your data lake needs. All three take a similar approach of leveraging metadata to handle the heavy lifting. To avoid incurring future charges, delete the resources generated if you dont need the solution anymore. Add a Z-order index. This blog post will thoroughly explore Apache Iceberg vs. Delta Lake. new support for Delta Lake multi-cluster writes on S3, reflect new flink support bug fix for Delta Lake OSS. Many options are available in the market, each with strengths and weaknesses. This table will track a list of files that can be used for query planning instead of file operations, avoiding a potential bottleneck for large datasets. Most comparison articles currently published seem to evaluate these projects merely as table/file formats for traditional append-only workloads, overlooking some qualities and features that are critical for modern data lake platforms that need to support update heavy workloads with continuous table management. Alternatively, follow the instructions in How to customize Docker images and build a custom EMR on EKS image to accommodate the Delta dependencies. Article updated on May 12, 2022 to reflect additional tooling support and updates from the newly released Hudi 0.11.0. They were generated by a Python script with the Faker package. Iceberg was born at Netflix and was designed to overcome cloud storage scale problems like file listings. Some of the typical use cases where Delta Lake shines are: Now that we have seen an overview of Apache Iceberg and Delta Lake, lets compare them on performance, scalability, ease of use, features, integrations, community, and support. It provides fast query performance over large tables, atomic commits, concurrent writes, and SQL-compatible table evolution. These indexes are stored in the Hudi Metadata Table which is stored in cloud storage next to your data. When choosing an open-source project to build your data architecture around you want strong contribution momentum to ensure the project's long-term support. However, there are some differences in the integrations they offer: Iceberg and Delta Lake have active and helpful community and support for users and contributors. If you need hidden partitioning to simplify query syntax and avoid skewing query results due to partition pruning, you may prefer Iceberg. We like the optimistic concurrency or mvcc controls that are available to us. By default, Delta Lake maintains the last 30 days of history in the tables adjustable. By default, Delta Lake maintains the last 30 days of history in the tables adjustable data retention settings. The community can make or break the development momentum, ecosystem adoption, or the objectiveness of the platform. With Delta Lake, you cant time travel to points whose log files have been deleted without a checkpoint to reference. With Hive, changing partitioning schemes is a very heavy operation. Apache Hudi and Apache Iceberg have a strong diversity in the community who contributes to the project. The performance of extract, transform, and load (ETL) jobs decreases as all the data files are read each time. When one company is responsible for the majority of a projects activity, the project can be at risk if anything happens to the company. Writes to any given table create a new snapshot, which does not affect concurrent queries. Below are a few foundational ideas and features that originated in Hudi and that are now being adopted into the other projects. To maintain Apache Iceberg tables youll want to periodically. Partitions allow for more efficient queries that dont scan the full depth of a table every time. Create a key named --conf for your AWS Glue job, and set it to the following value. For Source, choose Amazon S3. However, there are some differences in how they provide this ease of use: Iceberg and Delta Lake provide many features that enhance the capabilities and usability of a data lake. Thank you! When the data is filtered by the timestamp column, the query is able to leverage the partitioning of both portions of the data (i.e., the portion partitioned by year and the portion partitioned by month). Another challenge is making concurrent changes to the data lake. The following code snippet shows the SCD2 type of MERGE operation: As demonstrated earlier when discussing the job execution role, the role emr-on-eks-quickstart-execution-role granted access to the required DynamoDB table myIcebergLockTable, because the table is used to obtain locks on Iceberg tables, in case of multiple concurrent write operations against a single table. The maximum data volume of a single table reaches 400PB+, the daily volume increase is PB level, and the total data volume reaches EB level., The throughput is relatively large. For Delta Lake as an example this was just a JVM level lock held on a single Apache Spark driver node which means you have no OCC outside of a single cluster, until recently. To transfer ownership of an external schema, use ALTER SCHEMA to change the owner. Display of time types without time zone - The time and timestamp without time zone types are displayed in UTC. Not the answer you're looking for? Vacuum unreferenced files. We track every single activity at source, including duplicates caused by the retry mechanism and accidental data changes that are then reverted. For additional information, read:Delta Tables: A Practical Guide to Data Lake, Privacy Policy Vacuuming log 1 will disable time travel to logs 1-14, since there is no earlier checkpoint to rebuild the table from. The info is based on data pulled from the GitHub API. The chart below compares the open source community support for the three formats as of 3/28/22. Complete following steps to write into Apache Hudi table using the connector: Open AWS Glue Studio. The full version is in the script hudi_scd_script.py. Download the sample project either to your computer or the CloudShell console: Run the following blog_provision.sh script to set up a test environment. On top of that, SQL depends on the idea of a table and SQL is probably the most accessible language for conducting analytics. The data schema is complex. Delta Lake boasts 6400 developers have contributed to Delta Lake, but this article only reflects what is independently verifiable through the open-source repository activity.]. High level differences: Delta lake has streaming support, upserts, and compaction. In Hive, a table is defined as all the files in one or more particular directories. With record level indexes you can more efficiently leverage these change streams to avoid recomputing data and just process changes incrementally. Hudi does not support partition evolution or hidden partitioning. Bring libraries for the data lake formats Today, there are three available options for bringing libraries for the data lake formats on the AWS Glue job platform: Marketplace connectors, custom connectors (BYOL), and extra library dependencies. The solution workflow consists of the following steps: Data ingestion: Steps 1.1 and 1.2 use AWS Database Migration Service (AWS DMS), which connects to the source database and moves incremental data (CDC) to Amazon S3 in CSV format.