Welcome to the Lakehouse. A Lakehouse combines the best of both worlds of Data Warehouses and Data Lakes, no need to manage multiple systems or experience stale, inconsistent, redundant data. Using a Lakehouse, you can have a single source of truth for your data, allowing you to move fast without breaking thing. In this lesson, we'll discuss the Lakehouse paradigm as a solution to many of the problems we explored in the last lesson. By the end, you'll have a solid understanding of the types of features you need in order to do database-like operations in cheap cloud storage environments. Lakehouses work with any sort of data, structured, semi-structured, and unstructured. We can then land them in our Data Lake with appropriate metadata management, caching, and indexing. This makes our Data Lake reliable enough to build many different applications, whether you're doing business intelligence, machine learning or anything else. What's the big difference between this and other approaches? Let's talk about some common data engineering problems: if you're working with a Data Lake, you're likely working on files or maybe backups of databases. It can be hard to append data to those files. Other modifications of that data like deleting data in a file is also difficult, and if the job fails partway through an operation, then you might have some corrupt files sitting around. Doing real-time operations like streaming can also be difficult, and keeping historical data versions is also costly if you have to keep a database dump for say, each day of the year. These are all reliability issues. Data kept in Lakes can also lack good performance with large metadata files, and if you have too many small files, queries against those files take longer as you need one thread to read each of those files, this is the so-called small files problem. There are a wide variety of different performance issues like this. Next up is security, how can you ensure that correct people had access to the right data? For quality, how can you ensure that the data in your Lake matches the schema and other expectations you have about it? To the Lakehouse, this is unique in a number of ways. First, it's a simple way to manage data as it only needs to exist once to support all of your workloads, it's not siloed based upon type of workload you're performing. It's also open, that means that it's based on open source software and open standards to make it easy to work with without having to engage with expensive proprietary formats. Finally, it's collaborative, meaning engineers, analysts, and data scientists work together easily to serve a number of different workloads. The backbone of the Lakehouse is Data Lake, an open source software originally developed at Databricks and later open-sourced and donated to the Linux Foundation. That means that anybody can download the source code and use this framework to more efficiently manage their data applications. At a high level, Data Lakes have enhanced reliability by allowing for database or acid transactions against your data, more on this in the next video. It also has increased performance with indexing and partitioning in a bunch of related optimizations. There's improved governance with Table ACLs, an ACL is a so-called Access Control List, this is a common way of handling permissioning. Finally, you have better trust in your data through schema enforcement and other expectations. This is the Lakehouse, it's simple, open, and collaborative. We'll discuss more about the features of Data Lake in the next video. For now, think about this as unifying the best of Data Warehouses and Data Lakes for one scalable, flexible, and inexpensive way of managing many data needs.