Lean thinking is based on eliminating waste in order to improve efficiency. Lean project management relies on lean thinking concepts for the elimination of waste. Due to its inception in the quality and productivity improvement processes in Japanese manufacturing, especially the Toyota Production System, the three main categories of waste (3M) have Japanese names: muda, mura and muri .
Muda - any activity that consumes resources but creates no value for the customer
Agile is an approach to software development. Project management is the application of knowledge, skills and techniques to project activities to meet the project requirements. Agile is not a methodology. What does then "agile project management" mean ?
Simply put, agile project management can be defined as the application of agile methods and values to project management. While there are several agile methods (Scrum, Kanban, XP, FDD etc.), all of them share the same core values, inspired from the agile manifesto. These core values are:
Hadoop is designed to manage big data and by design this means HDFS is designed to store very large files in a distributed cluster with streaming access to this data. For reference, a typical block in HDFS is 64 MB or 128 MB. Each small file (few MB or less) is stored in a block and multiple small files could be stored in blocks across different nodes of the distributed cluster.
The use of lean practices like Kanban boards has become really popular in project management, especially those using agile methods. But what exactly is Lean project management ?
The application of lean manufacturing principles to project management can be roughly translated as lean project management. These principles were developed at Toyota, with the famous Toyota Production System employing kanban and the concepts of just-in-time (JIT) and “pull” to optimize flow and minimize inventory.
I recently gave a talk on data processing with Apache Spark using R and Python. tl;dr - the slides and presentation can be accessed below (free registration):
As noted in my previous post, Spark has become the defacto standard for big data applications and has been adopted quickly by the industry. See Cloudera’s One Platform initiative blog post by CEO Mike Olson for their commitment to Spark.
