Why Apache Iceberg will rule cloud data and is optimal for cloud data workloads?
Cloud data has allowed data teams to collect vast quantities of big data and store it at a reasonable cost, opening the door to new analytics use cases that leverage data lakes, data mesh, and other modern architectures. But for very large volumes of data sets, generic cloud storage also presents challenges and limitations. The importance of using Apache Iceberg arises in this scenario.
It has become challenging for cloud data to access, manage and use big data. This is where applying table formats to data becomes immensely useful. Identifying which table format to be used is a critical decision because it carries the potential to enable or limit the features available. Over the past two years, we have seen significant support emerging for Apache Iceberg, a table format originally developed by Netflix. 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. As a Netflix engineer recorded at the time, table formats for very large-scale data sets should work as authentically and predictably as SQL, “without any unpleasant surprises.”
With several options available, we believe Apache Iceberg is superior to other open table formats available. Here are five reasons why.
Apache Iceberg makes a clean break from the past. Iceberg was built from the ground up to address shortcomings in Apache Hive, which means it has avoided some of the undesirable qualities that held back data set lakes in the past. How schema changes can be handled, such as renaming a column, is a good example.
Apache Iceberg is agnostic to the processing engine and file format, by decoupling the processing engine from the table format, Iceberg provides greater flexibility and choice. Instead of being enforced to apply one processing engine, engineers can pick the best tool for the job.
Iceberg is a well-run open-source project. Apache Iceberg makes its project management public, so you are aware of who is running the project. Other table formats are not disclosing who has decision-making authority. A table format is an elementary choice in data architecture, so choosing a project that is truly open and collaborative can significantly reduce the risks of accidental lock-in.
Collaboration in Iceberg is spawning new ideas and help. There are numerous signs that the collaborative community around Apache Iceberg is helping users and setting the project up for long-term success. Iceberg includes features that are paid in other table formats. Unlike some other table projects, Iceberg has performance-oriented features built in from the start, which is helpful for users in multiple ways. There are some excellent resources within the Apache Iceberg community to learn more about the project and to get involved in the open-source effort.
The post Is Apache Iceberg the Future of Cloud Data Workload? appeared first on Analytics Insight.
Why Apache Iceberg will rule cloud data and is optimal for cloud data workloads?
Cloud data has allowed data teams to collect vast quantities of big data and store it at a reasonable cost, opening the door to new analytics use cases that leverage data lakes, data mesh, and other modern architectures. But for very large volumes of data sets, generic cloud storage also presents challenges and limitations. The importance of using Apache Iceberg arises in this scenario.
It has become challenging for cloud data to access, manage and use big data. This is where applying table formats to data becomes immensely useful. Identifying which table format to be used is a critical decision because it carries the potential to enable or limit the features available. Over the past two years, we have seen significant support emerging for Apache Iceberg, a table format originally developed by Netflix. 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. As a Netflix engineer recorded at the time, table formats for very large-scale data sets should work as authentically and predictably as SQL, “without any unpleasant surprises.”
With several options available, we believe Apache Iceberg is superior to other open table formats available. Here are five reasons why.
Apache Iceberg makes a clean break from the past. Iceberg was built from the ground up to address shortcomings in Apache Hive, which means it has avoided some of the undesirable qualities that held back data set lakes in the past. How schema changes can be handled, such as renaming a column, is a good example.
Apache Iceberg is agnostic to the processing engine and file format, by decoupling the processing engine from the table format, Iceberg provides greater flexibility and choice. Instead of being enforced to apply one processing engine, engineers can pick the best tool for the job.
Iceberg is a well-run open-source project. Apache Iceberg makes its project management public, so you are aware of who is running the project. Other table formats are not disclosing who has decision-making authority. A table format is an elementary choice in data architecture, so choosing a project that is truly open and collaborative can significantly reduce the risks of accidental lock-in.
Collaboration in Iceberg is spawning new ideas and help. There are numerous signs that the collaborative community around Apache Iceberg is helping users and setting the project up for long-term success. Iceberg includes features that are paid in other table formats. Unlike some other table projects, Iceberg has performance-oriented features built in from the start, which is helpful for users in multiple ways. There are some excellent resources within the Apache Iceberg community to learn more about the project and to get involved in the open-source effort.
The post Is Apache Iceberg the Future of Cloud Data Workload? appeared first on Analytics Insight.