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Apache Iceberg Tutorial: Learn the Problem & Solution Behind Iceberg's Origin Story

This Apache Iceberg 101 Course will cover the origin story of the data lakehouse table format. We will explore what Hive is, and why it was beneficial. We will also discuss the problems associated with Hive, as well as the solution that Apache Iceberg provides. Hive is an open source data warehouse system developed by Facebook in 2008. It was created to provide an efficient way to manage large amounts of data stored in Hadoop clusters. The system was designed to make it easier for developers to write queries and manipulate data without having to learn a new programming language. Hive allowed users to work with structured data using SQL-like commands, making it easier for them to access their data from different sources. The benefits of Hive were numerous, including its scalability, cost-effectiveness, and ease of use. However, there were some drawbacks associated with it as well. One issue was that Hive wasn’t able to handle unstructured or semi-structured data very well. This caused problems when users needed to access their data from different sources or when they wanted to use more complex queries than what Hive could provide. Another issue was that Hive had a steep learning curve, making it difficult for new users or those who weren’t familiar with SQL-like commands to get started quickly and easily. In order to address these issues, Apache Iceberg was created as an open source alternative to Hive in 2016. Apache Iceberg is a Data Lakehouse engine that provides better support for unstructured and semi-structured data than what traditional Data Warehouses provide. It also makes querying and manipulating this type of data much simpler by providing a unified query language that works across multiple sources and systems. This makes it easier for developers and analysts alike to quickly get up and running without having to learn a new programming language or understand complex database architectures. Apache Iceberg also provides performance benefits over traditional Data Warehouses due its ability to store large amounts of unstructured or semi-structured data in a more efficient manner than other systems can offer. By using columnar storage formats such as Parquet, Apache Iceberg can dramatically reduce the amount of time needed for I/O operations on large datasets while still providing fast query performance over the same datasets. If you’re looking for more great content on the topic of Data Lakehouses like Apache Iceberg, be sure to check out dremio.com/subsurface where you can find tutorials, articles, webinars, and more! Connect with us! Twitter: https://bit.ly/30pcpE1 LinkedIn: https://bit.ly/2PoqsDq Facebook: https://bit.ly/2BV881V Community Forum: https://bit.ly/2ELXT0W Github: https://bit.ly/3go4dcM Blog: https://bit.ly/2DgyR9B Questions?: https://bit.ly/30oi8tX Website: https://bit.ly/2XmtEnN