The Top 7 Benefits of Using Apache Iceberg

Apache Iceberg is a table format that enables efficient, large-scale analytics on data stored in cloud object storage. It provides a solution to the challenge of efficiently capturing and representing data sets of varying sizes and shapes, making data easier to analyse and manage. Apache Iceberg can be considered a data lake as it allows for data to be stored in its raw form, which enables diverse analytics workloads to be run on the same data for improved data governance. Development of Apache Iceberg began in 2017.

All of your concerns will be addressed in this blog post.

Nowadays, businesses are required to manage and assess an abundance of extensive datasets. Both structured and unstructured data of any magnitude can be securely stored in data lakes, which additionally provide the ability to process the data according to the specific requirements and enable companies to make informed decisions based on data.

A definition for Apache Iceberg would be helpful.

Apache Iceberg is a new open table format that offers a range of features to simplify the storage, retrieval, and tracking of data stored in multiple files. Popular file formats such as Apache Parquet, Optimised Row Columnar, and AVRO can all benefit from the use of the Iceberg table format, which makes the management of large and complex datasets easier.

The Apache Iceberg project uses a tree structure to organise and catalogue all files. This table format includes an accurate reference to the metadata file that stores the data files.

Explain how Apache Iceberg is superior than Apache Hive.

Netflix has developed Apache Iceberg in order to address the bottlenecks in Apache Hive‘s data consistency and performance. Apache Hive stores data at the folder level, meaning that users must first conduct file list operations in order to access data tables. This can lead to issues where certain object storage appears to be missing during file list operations. Furthermore, large partition changes require complete partition rewriting in order to make the data accessible in the new location.

Furthermore, Apache Hive’s intricate directory structure and additional layer of inefficiencies render data exploration a laborious task as datasets become larger. Additionally, users must remain cognizant of the exact table structure when formulating queries.

Apache Iceberg is a data structure that is similar to that of Hadoop in that it manages and stores both metadata and data layers. In comparison to Apache Hive, which stores data at the partition level, Iceberg allows for quick and simple addition, removal, and updating of data as users can access files directly at the file level.

The Apache Iceberg architecture provides a snapshot querying approach that can be incredibly beneficial when dealing with large volumes of data. Through the utilisation of manifest and metadata files, data searching can be expedited, as it is available at the file level. This approach can significantly reduce the time and effort needed to locate information.

You should go with Apache Iceberg because…

  1. Evolution of Schemas

    Apache Iceberg is designed with schema evolution in mind, and thus no unexpected outcomes or dependencies arise from such transformations. To ensure that column names are persistently identified across all levels of metadata, a unique identifier is added to the metadata during the evolution process.

    Apache Iceberg databases enable users to add, remove, or rename columns, as well as reorder, adjust the width of, and modify the map keys, struct fields, and list items of the table layout.
  2. Cloaking the divisions

    Apache Iceberg has hidden partitioning, which enables users to do searches without being aware of the table’s internal dividing.

    Iceberg users have the ability to segment time stamps into various categories, such as day, date, year, and month. Furthermore, users can utilise hash buckets, truncation, and identification in order to further divide the columns.
  3. Scalable SQL

    The implementation of large-scale analytical queries on data lakes is made possible through Apache Iceberg, a system designed to enable versatile SQL commands to modify existing rows, merge new data, or remove rows and columns from tables as the volume of data increases exponentially. The system provides efficient scalability and presents an ideal solution for managing data at a large scale.
  4. Reversal and time travel

    Thanks to the Apache Iceberg framework, programmers have the ability to access and examine data from any point in the past or future. To ensure that any modifications to the table are documented and tracked, Apache Iceberg takes snapshots at periodic intervals.

    It is possible to revert changes or go back in time using either the snapshot-id or the as-of timestamp methods. Utilising millisecond-accurate as-of timestamps, one can select the current snapshot at a specific moment. Alternatively, using the snapshot-id method allows individuals to pinpoint a particular frozen state of a database.
  5. ACID conformity

    Atomicity, consistency, isolation, and durability (ACID) are essential characteristics of database transactions which, when applied properly, ensure that queries are processed in a timely manner. Without ACID compliance, the structure of a database can lead to slow response times when attempting to access data. It is therefore essential that ACID is implemented in order to maximise the efficiency of data queries.

    By reducing the amount of data stored, the Apache Iceberg framework improves the efficiency and cost-effectiveness of file-based searches. To simplify data and expedite query responses, Apache Iceberg stores relevant information in files.
  6. Allows for a wide variety of file types and search engines

    Developers can find a number of advantages in Apache Iceberg due to its versatility, as it provides support for a wide array of query engines, such as Hadoop, Trino, Hive, Flink, and Spark, as well as various file formats, including Apache Parquet, Avro, and ORC. This broad range of options allows developers to select the specific tools and formats that best meet their needs.

    Apache Iceberg’s versatile architecture gives developers the freedom to select the most suitable strategy for each individual situation. This open-source software provides a dependable and malleable platform for applications that can access tables.
  7. The Power of AWS Integrations

    Various Amazon Web Services (AWS) can be conveniently connected to Apache Iceberg via the iceberg module. Engines such as Apache Spark, Apache Flink, and Apache Hive can be interfaced with AWS using the Apache Iceberg framework. This streamlined integration of multiple popular cloud services provides users with the flexibility to develop their applications in a way that best suits their needs.

    A wide selection of specialised catalogues are available in a table format, including the Glue Catalogue, the DynamoDB Catalogue, and the RDS JDBC Catalogue. When constructing the Iceberg Catalogue, developers have the option of selecting from a variety of guides offered by Amazon Web Services.


In order to maximise the efficiency of data analysis, developers have developed Apache Iceberg, a modernised table format. Apache Iceberg is a highly favoured choice for large data systems due to its expeditious collaboration, secure and dependable data querying, and the ability to be utilised with multiple engines and catalogues.

As businesses become increasingly aware of the need for future-proof, scalable structures of the “lake house” style, they are actively seeking out experienced developers who are well-versed in the latest table formats and designs. By investing in such talent, businesses can ensure that their infrastructures are both reliable and secure.

I was wondering whether you have heard about Apache Iceberg.

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  1. When did development of Apache Iceberg begin?

    As of August 15, 2021, Apache Iceberg was made available.
  2. Can we call Apache Iceberg a data lake?

    Despite the widespread misconception, Apache Iceberg is not a data lake, but rather a table structure used to manage data within such systems. Data lakes are able to store both structured and unstructured data of any size.
  3. Can you explain the process of importing and exporting data in Apache Iceberg?

    Many developers leveraging Apache Iceberg have access to the data frame capabilities of a variety of query engines, such as Apache Spark and Apache Flink, which enable them to both read from and write to the data.

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