To start off, let's talk about data warehouses. In the early days of data, you'd have to set up your own data center, then you'd buy some proprietary closed source database software to help you store your data. Now, this was before cloud computing and open-source dominated the industry. These data warehouses are great at storing very structured data, they're excellent for BI and reporting, and they provide ACID guarantees. ACID guarantees means that you can do database-like operations against your data, which just is impossible with file types like CSV. But these data warehouses didn't provide support for all data types, especially unstructured data like images and videos. Also, there's often vendor lock-in instead of open-source protocols. Data warehouses provide a very limited support for more advanced machine learning workloads. Given these challenges, much of the industry turned to data lakes on scalable Cloud storage like AWS's S3, and Azure's Blob Storage. They are cheap, they're scalable, and they support a wide variety of data formats. However, they're also very difficult to manage, and they can often turn into data swamps due to difficulty in extracting value from them. Many enterprises would have a two-storage solution. Raw data would land in the data lake as it is cheap and infinitely scalable, then the business-critical data would be copied into a data warehouse for queries into power dashboards. However, this would lead to stale, inconsistent data as the data warehouses could only be updated with new data upon running some scheduled job. This is where lakehouses come in. Lakehouses combine the scalability and low-cost storage of data lakes with the speed and reliability and ACID transactional guarantees of data warehouses. A lakehouse architecture augments your data lake with metadata management for optimized performance. No need to unnecessarily copy data to or from a data warehouse. You get data versioning, reliable and fault-tolerant transactions, and a fast query engine all while maintaining open standards. It ensures that your data teams can access timely, reliable, high-quality data. Delta Lake is an open-source project from the Linux Foundation that supports the lakehouse architecture and has the tightest integration with Apache Spark among all of the open-source projects. It was originally built by Databricks' engineers to address issues our customers experienced when developing applications with Apache Spark, namely, the inability to do database-like operations on top of a data lake. This includes adding, deleting, and updating data, as well as handling corrupt data safely, such as rolling back mistakes, updating table schemas or optimizing data layout. Delta Lake is built on top of Parquet, a file format you've already been introduced to, but it adds an additional transaction log. The transaction log tracks the changes or the delta in your underlying data. Thus, if Conor's actively writing to a table that I'm reading from, if the write hasn't been committed yet or if it fails partway through, it will not be the transaction log, thus not impacting my read. But by the time that the read is committed, it will show up in the transaction log and any new readers of the table will have the updated data. As an added benefit of using this transaction log, you can always time travel back to an earlier point in the transaction log, proving that time travel really is possible. In summary, the lakehouse architecture is the best way to store your data. It provides massive scalability and performance, it provides transactional support, it maintains an open standard, and it supports diverse data formats and data workloads. All of this sets the foundation for successful data projects because now the data is well organized, optimized, and open for all of your downstream tasks. Now, I hope you brought your swimsuit to the lakehouse as we're about to dive into the Delta Lake.