delta VS iceberg

Compare delta vs iceberg and see what are their differences.


An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs (by delta-io)
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delta iceberg
44 9
5,433 3,617
1.8% 3.3%
9.9 9.9
8 days ago 5 days ago
Scala Java
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of delta. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-24.
  • The Evolution of the Data Engineer Role
    2 projects | | 24 Oct 2022
    FACT table (sale $, order quantity, order ID, product ID)

    customer, account_types etc are dimensions to filter your low-level transactional data. The schema like a snowflake when you add enough dimensions, hence the name.

    The FACT table makes "measures" available to the user. Example: Count of Orders. These are based on the values in the FACT table (your big table of IDs that link to dimensions and low-level transactional data).

    You can then slice and dice your count of orders by fields in the dimensions.

    You could then add Sum of Sale ($) as an additional measure. "Abstract" measures like Average Sale ($) per Order can also be added in the OLTP backend engine.

    End users will often be using Excel or Tableau to create their own dashboards / graphs / reports. This pattern makes sense in that case --> user can explore the heavily structured business data according to all the pre-existing business rules.


    - Great for enterprise businesses with existing application databases

    - Highly structured and transaction support (ACID compliance)

    - Ease of use for end business user (create a new pivot table in Excel)

    - Easy to query (basically a bunch of SQL queries)

    - Encapsulates all your business rules in one place -- a.k.a. single source of truth.


    - Massive start up cost (have to work out the schema before you even write any code)

    - Slow to change (imagine if the raw transaction amounts suddenly changed to £ after a certain date!)

    - Massive nightly ETL jobs (these break fairly often)

    - Usually proprietary tooling / storage (think MS SQL Server)


    2. Data Lake

    Throw everything into an S3 bucket. Database table? Throw it into the S3 bucket. Image data? Throw it into the S3 bucket. Kitchen sink? Throw it into the S3 bucket.

    Process your data when you're ready to process it. Read in your data from S3, process it, write back to S3 as an "output file" for downstream consumption.


    - Easy to set up

    - Fairly simple and standardised i/o (S3 apis work with pandas and pyspark dataframes etc)

    - Can store data remotely until ready to process it

    - Highly flexible as mostly unstructured (create new S3 keys -- a.k.a. directories -- on the fly )

    - Cheap storage


    - Doesn't scale -- turns into a "data swamp"

    - Not always ACID compliant (looking at you Digital Ocean)

    - Very easy to duplicate data


    3. Data Lakehouse

    Essentially a data lake with some additional bits.

    A. Delta Lake Storage Format a.k.a. Delta Tables

    Versioned files acting like versioned tables. Writing to a file will create a new version of the file, with previous versions stored for a set number of updates. Appending to the file creates a new version of the file in the same way (e.g. add a new order streamed in from the ordering application).

    Every file -- a.k.a. delta table -- becomes ACID compliant. You can rollback the table to last week and replay e.g. because change X caused bug Y to happen.

    AWS does allow you do this, but it was a right ol' pain in the arse whenever I had to deal with massively partitioned parquet files. Delta Lake makes versioning the outputs much easier and it is much easier to rollback.

    B. Data Storage Layout

    Enforce a schema based on processing stages to get some performance & data governance benefits.

    Example processing stage schema: DATA IN -> EXTRACT -> TRANSFORM -> AGGREGATE -> REPORTABLE

    Or the "medallion" schema: Bronze -> Silver -> Gold.

    Write out the data at each processing stage to a delta lake table/file. You can now query 5x data sources instead of 2x. The table's rarity indicates the degree of "data enrichment" you have performed -- i.e. how useful have you made the data. Want to update the codebase for the AGGREGATE stage? Just rerun from the TRANSFORM table (rather than run it all from scratch). This also acts as a caching layer. In a Data Warehouse, the entire query needs to be run from scratch each time you change a field. Here, you could just deliver the REPORTABLE tables as artefacts whenever you change them.

    C. "Metadata" Tracking

    See AWS Glue Data Catalog.

    Index files that match a specific S3 key pattern and/or file format and/or AWS S3 tag etc. throughout your S3 bucket. Store the results in a publicly accessible table. Now you can perform SQL queries against the metadata of your data. Want to find that file you were working on last week? Run a query based on last modified time. Want to find files that contain a specific column name? Run a query based on column names.


    - transactional versioning -- ACID compliance and the ability to rollback data over time (I accidentally deleted an entire column of data / calculated VAT wrong yesterday)

    - processing-stage schema storage layout acts as a caching layer (only process from the stage where you need to)

    - no need for humans to remember the specific path to the files they were working on as files are all indexed

    - less chance of creating a "data swamp"

    - changes become easier to audit as you can track the changes between versions


    - Delta lake table format is only really available with Apache Spark / Databricks processing engines (mostly, for now)

    - Requires enforcement of the processing-stage schema (your data scientists will just ignore you when you request they start using it)

    - More setup cost than a simple data lake

    - Basically a move back towards proprietary tooling (some FOSS libs are starting to pop up for it)


    4. Data Mesh

    geoduck14's answer on this was pretty good. basically have a data infrastructure team, and them domain-specific teams that spring up as needed (like an infra team looking after your k8s clusters, and application teams that use the clusters). domain specific data team use the data platform provided by the data infrastructure team.

    Previously worked somewhere in a "product" team which basically performed this function. They just didn't call it a "data mesh".

  • 5 Reasons Your Data Lakehouse should Embrace Dremio Cloud
    2 projects | | 9 Aug 2022
    You can query data organized in many open table formats like Apache Iceberg and Delta Lake. (Here is a good article on what is a table format and the differences between different ones)
  • Delta 2.0 - The Foundation of your Data Lakehouse is Open
    2 projects | | 5 Aug 2022
    Note that the roadmap can be found at and we’re actively asking for feedback so we can prioritize the remaining items. Please chime in there so we can track and re-prioritize! Thanks!
    2 projects | | 5 Aug 2022
    Still not quite completely on par with the Databricks version, with missing features like GENERATED ALWAYS AS IDENTITY, but this is getting good.
  • Databricks platform for small data, is it worth it?
    3 projects | | 29 Jun 2022
    Currently the infrastructure we have is some custom made pipelines that load the data on S3, and I use Delta Tables here and there for its convenience: ACID, time travel, merges, CDC etc...
  • Data point versioning infrastructure for time traveling to a precise point in time?
    2 projects | | 18 Jun 2022
    I've been playing around a bit with Delta (Table/Lake) whatever you want to call it. It has time travel so you can look back and see what the data looked like at a particular point in time.
  • How-to-Guide: Contributing to Open Source
    19 projects | | 11 Jun 2022
    Delta Lake
  • What companies/startups are using Scala (open source projects on github)?
    13 projects | | 24 May 2022
    There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.
  • What is a Delta Table?
    2 projects | | 20 May 2022
    Ah. I believe you are correct. As I look at the examples section for python in the github repo these are looking almost identical to what I was seeing.
    2 projects | | 20 May 2022
    It is a specific table format. it’s an open source project just read their website, will have way more info than these comments.


Posts with mentions or reviews of iceberg. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-14.
  • What is the Lakehouse, the latest Direction of Big Data Architecture?
    2 projects | | 14 May 2022
    Hudi focuses more on the fast landing of streaming data and the correction of delayed data. Iceberg focuses on providing a unified operation API by shielding the differences of the underlying data storage formats, forming a standard, open and universal data organization lattice, so that different engines can access through API. Lakesoul, now based on spark, focuses more on building a standardized pipeline of data lakehouse. Delta Lake, an open-source project from Databricks, tends to address storage formats such as Parquet and ORC on the Spark level.
  • Details of 4 best opensource projects about big data you should try out(Ⅰ)
    2 projects | | 7 Apr 2022
    The above is the detailed information about LakeSoul, and there is more information on its Github homepage for reference. In the following story, I will introduce the detailed information about Iceberg and make a comparison between them, which is beneficial for me to learn about data lake better. If it is helpful to you, please read it or share it. I also hope you can give me guidance and suggestions for my study. Thank you.
  • 4 best opensource projects about big data you should try out
    3 projects | | 24 Mar 2022
    2.Iceberg Iceberg is an open table format for huge analytic dataset with Schema evolution, Hidden partitioning, Partition layout evolution, Time trave, Version rollback, etc.
    4 projects | | 24 Mar 2022
  • Would ParquetWriter from pyarrow automatically flush?
    4 projects | | 11 Sep 2021 > Hidden partitioning prevents user mistakes that cause silently incorrect results or extremely slow queries > Version rollback allows users to quickly correct problems by resetting tables to a good state > Multiple concurrent writers use optimistic concurrency and will retry to ensure that compatible updates succeed, even when writes conflict
  • Apache Hudi - The Streaming Data Lake Platform
    8 projects | | 27 Jul 2021
    But first, we needed to tackle the basics - transactions and mutability - on the data lake. In many ways, Apache Hudi pioneered the transactional data lake movement as we know it today. Specifically, during a time when more special-purpose systems were being born, Hudi introduced a server-less, transaction layer, which worked over the general-purpose Hadoop FileSystem abstraction on Cloud Stores/HDFS. This model helped Hudi to scale writers/readers to 1000s of cores on day one, compared to warehouses which offer a richer set of transactional guarantees but are often bottlenecked by the 10s of servers that need to handle them. We also experience a lot of joy to see similar systems (Delta Lake for e.g) later adopt the same server-less transaction layer model that we originally shared way back in early '17. We consciously introduced two table types Copy On Write (with simpler operability) and Merge On Read (for greater flexibility) and now these terms are used in projects outside Hudi, to refer to similar ideas being borrowed from Hudi. Through open sourcing and graduating from the Apache Incubator, we have made some great progress elevating these ideas across the industry, as well as bringing them to life with a cohesive software stack. Given the exciting developments in the past year or so that have propelled data lakes further mainstream, we thought some perspective can help users see Hudi with the right lens, appreciate what it stands for, and be a part of where it’s headed. At this time, we also wanted to shine some light on all the great work done by 180+ contributors on the project, working with more than 2000 unique users over slack/github/jira, contributing all the different capabilities Hudi has gained over the past years, from its humble beginnings.

What are some alternatives?

When comparing delta and iceberg you can also consider the following projects:

hudi - Upserts, Deletes And Incremental Processing on Big Data.

kudu - Mirror of Apache Kudu

Apache Avro - Apache Avro is a data serialization system.

debezium - Change data capture for a variety of databases. Please log issues at

RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.

dvc - 🦉Data Version Control | Git for Data & Models | ML Experiments Management

delta-rs - A native Rust library for Delta Lake, with bindings into Python and Ruby.

lakeFS - Git-like capabilities for your object storage

Dask - Parallel computing with task scheduling


Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing