Optimising queries with many joins is known to be a hard problem. The explosion of intermediate results as opposed to a much smaller final result poses a serious challenge to modern database management systems (DBMSs). This is particularly glaring in case of analytical queries that join many tables, but ultimately only output comparatively small aggregate information. Analogous problems are faced by graph database systems when processing analytical queries with aggregates on top of complex path queries. In this work, we propose novel optimisation techniques both, on the logical and physical level, that allow us to avoid the materialisation of join results for certain types of aggregate queries. The key to these optimisations is the notion of guardedness, by which we impose restrictions on the occurrence of attributes in GROUP BY clauses and in aggregate expressions. The efficacy of our optimisations is validated through their implementation in Spark SQL and extensive empirical evaluation on various standard benchmarks.