Building a mobility data mart requires cleanse, then join, then aggregate stages that do not fit cleanly in one MapReduce job. What pipeline approach is appropriate?

Complex analytics often decompose into multiple MapReduce stages with intermediate HDFS outputs between them. Sequencing cleanse, join, and aggregate jobs is a standard pipeline pattern for marts. Single-mapper extremes, browser-cookie storage, and unrelated cloud-fundamentals content do not replace multi-job workflow design.