#Apache iceberg example how to#
#Apache iceberg example software#
#Apache iceberg example code#

This is not necessarily the case for every project calling itself “open source.” Apache Iceberg makes its project management public, so you know who is running the project.

#Apache iceberg example software#

The Iceberg project is managed by the Apache Software Foundation, which means it adheres to several important Apache Ways, including earned authority and consensus decision making. Iceberg is a well-run open source project This provides flexibility today, but also enables better long-term plugability for file formats that may emerge in the future. Iceberg also supports multiple file formats, including Apache Parquet, Apache Avro, and Apache ORC. Second, it’s common for large organizations to use several different technologies, and having choice enables them to use several tools interchangeably. For example, many businesses moved from Hadoop to Spark or Trino. First, the engines a company uses to process data can change over time. Choice is important for at least two key reasons. Instead of being forced to use one processing engine, engineers can pick the best tool for the job. Iceberg is agnostic to processing engine and file formatīy decoupling the processing engine from the table format, Iceberg provides greater flexibility and choice. Over time, other table formats will likely catch up, but as of now, Iceberg is focused on delivering the next set of new features, instead of looking back to fix old problems.

#Apache iceberg example how to#

Looking ahead, this also means Iceberg does not need to rationalize how to further break from related tools without causing issues with production data applications. How schema changes can be handled, such as renaming a column, is a good example. It was built from the ground up to address shortcomings in Apache Hive, which means it has avoided some of the undesirable qualities that held data lakes back in the past. Some table formats have evolved from older technologies, while others have made a clean break. The past can have a major impact on how a table format works today. Iceberg makes a clean break from the past

With several options available, we believe Iceberg is superior to other open table formats available.

As a Netflix engineer noted at the time, table formats for very large-scale data sets should work as reliably and predictably as SQL, “without any unpleasant surprises.” Iceberg was built from the ground up to address some of the challenges in Apache Hive when working with very large data sets, including issues around scale, usability, and performance. Over the past two years, we have seen significant support emerging for Apache Iceberg, a table format originally developed by Netflix that was open-sourced as an Apache incubator project in 2018 and graduated from the incubator program in 2020.

Ability to “time travel” across the table to view data at a given point in timeĬhoosing which table format to use is an important decision because it can enable or limit the features available.

Faster performance due to better filtering or partitioning.

Therefore, applying a table format to the data can offer a number of advantages, such as: Moreover, the table metadata can be saved in a way that offers more fine-grained partitioning. Instead of applying a schema when the data is read, clients already know the schema before the query is run. Table formats explicitly define a table, its metadata, and the files that compose the table.

This is where applying table formats to data becomes extremely useful. All of a sudden, what seemed like an easy-to-implement data architecture becomes more difficult than expected. Each query engine must have its own view of how to query the files. Additionally, files by themselves do not make it easy to change schemas of a table, or to “time travel” over it. Typical blob storage systems in the cloud lack the information required to show relationships between files or how they correspond to a table, making the job of query engines that much harder. But for very large volumes of data, generic cloud storage also presents challenges and limitations in how that data can be accessed, managed, and used. Import .The cloud has allowed data teams to collect vast quantities of data and store it at reasonable cost, opening the door to new analytics use cases that leverage data lakes, data mesh, and other modern architectures.

#Apache iceberg example code#

However, the reader prints the data of all the snapshots (8 rows) even though I set the snapshot of TableScan as current snapshot.īelow is the sample code package org.example2 While reading the data, I want to read the data of the latest snapshot only. The last file is marked as current snapshot which is right. In my example, each file contains 4 rows. While writing I am writing twice using 2 datafiles that create 2 snapshots.