Pre-trained word embedding models have become the de-facto standard to model text in state-of-the-art analysis tools and frameworks. However, while there are massive amounts of textual data stored in tables, word embedding models are usually pre-trained on large documents. This mismatch can lead to narrowed performance on tasks where text values in tables are analyzed. To improve analysis and retrieval tasks working with tabular data, we propose a novel embedding technique to be pre-trained directly on a large Web table corpus. In an experimental evaluation, we employ our models for various data analysis tasks on different data sources. Our evaluation shows that models using pre-trained Web table embeddings outperform the same models when applied to embeddings pre-trained on text. Moreover, we show that by using Web table embeddings state-of-the-art models for the investigated tasks can be outperformed.