With tens if not hundreds of billions of logical statements, the Linked Open Data (LOD) is one of the biggest knowledge bases ever built. As such it is a gigantic source of information for applications in various domains, but also given its size an ideal test-bed for knowledge representation and reasoning, heterogeneous nature, and complexity. However, making use of this unique resource has proven next to impossible in the past due to a number of problems, including data collection, quality, accessibility, scalability, availability and findability. The LOD Laundromat and LOD Lab are recent infrastructures that addresses these problems in a systematic way, by automatically crawling, cleaning, indexing, analysing and republishing data in a unified way. Given a family of simple tools, LOD Lab allows researchers to query, access, analyse and manipulate hundreds of thousands of data documents seamlessly, e.g. facilitating experiments (e.g. for reasoning) over hundreds of thousands of (possibly integrated) datasets based on content and meta-data. This chapter provides the theoretical basis and practical skills required for making ideal use of this large scale experimental platform. First we study the problems that make it so hard to work with Semantic Web data in its current form. We’ll also propose generic solutions and introduce the tools the reader needs to get started with their own experiments on the LOD Cloud.