One of the key components in the Hadoop framework is Map Reduce. It is a distributed data processing model and execution environment that runs on large clusters of commodity machines. It uses the MapReduce algorithm which breaks down all operations into Map, Sorting and Reduce functions. Coming from a non-programming background can make this a difficult concept to understand. A good analogy to use when trying to understand Map Reduce is to associate it to SQL based concepts. Most of the world is familiar with relational databases and SQL, thus, putting it in that context can put a tangible definition to Map Reduce. 

Listed below I have broken out each of the components of Map Reduce and provided a SQL construct as association. It is important to note that Map Reduce jobs and SQL Queries have their similarities but also can have very different purposes. As such, there are times when the SQL analogy cannot be used verbatim or may not align 100%.  

Maps are individual tasks that seek out and retrieve  Key Value pairs that match a criteria. Although Maps are functions, when using the analogy of SQL it is easiest to think of them as the FROM, WHERE and UNION operations in a SQL query.  Maps act to seek out data sets before any reducing, shuffling or sorting is performed . In SQL, the FROM, WHERE and UNION operations seek out data sets before reducing (SUM, COUNT, MIN...etc), shuffling or sorting (GROUP BY, ORDER BY) is performed.

Reduces are tasks that aggregate, summarize and reduce the shuffled and  sorted outputs of the mappers.  Using the analogy of SQL, the Reduce task is similar to the aggregate functions like SUM, DISTINCT, COUNT, MIN, MAX, AVG...etc in a SQL query. Reducers act to aggregate and summarize the data from a full data set into a smaller and manageable output that was originally specified by the user or job. In SQL, the aggregate functions (SUM, DISTINCT, COUNT, MIN, MAX, AVG..etc) operations act to reduce the data across multiple tables into a smaller output specified by the query or user. 

Shuffles and Sorts are tasks that group, sort and organize the outputs of the mappers. Using the analogy of SQL the Shuffle and Sort tasks closely resemble the GROUP BY and ORDER BY operations in a SQL query. Shuffles and Sorts are key to organizing the data in formats and orders that are easiest to perform reduce functions upon. In SQL, the GROUP BY and ORDER BY operations act similarly to organize and group data so that reducing (SUM, COUNT, MIN...etc) functions can be easily performed. 

Symmetric Multi-Processing (SMP)
System architecture where all of the processors connect to shared resources (memory, I/O, and network) under a single operating system. Each processor has a private cache memory, access to the main memory. Processors are interconnected using buses, switches or chip networks.

SMP Architecture

Advantages:
- Relatively inexpensive single machine design (no racks needed)
- Symmetric distributed computing
- Efficiently/Quickly  process small to medium data volumes

Disadvantages:
- Inefficient/Lengthy at processing large data volumes
- Scaling up or down requires a machine upgrade/downgrade
- Resource/Memory contention between processors
- External interrupts impact all processing
- Operating System limitation on scalability (OS can only support 64-100 multi-processors) 
- Expensive (time and cost) to upgrade hardware


Massive Parallel Processing (MMP)
System architecture where processing is parallelly distributed processing across an integrated set of servers known as compute nodes. Each compute node contains its own set of processors, memory and bus. Each server also comes with its own operating system and DMBS allowing it to run as an independent processing unit . Compute nodes are interconnected using a control and management node which split, distribute and mange processing. Compute nodes can be added or remove by adding or removing servers to the rack.

MPP Architecture

Advantages:
- Relatively inexpensive hardware needed to scale (cost of new server is cheaper than buying a new machine)
- No resource contention across compute nodes
- Scaling up or down is easy and can be performed without taking down the system
- Ability to add failover and backup servers
- Efficiently/Quickly  process large data volumes
- No limitation on the number of compute nodes that can be added

Disadvantages:
- Additional maintenance required (rack space, cooling, monitoring)
- Additional maintenance costs (power, cooling, hardware upgrades)
- Unused resources during small and medium data volumes

As mentioned earlier, traditional BI, Data Warehouse and DBMS tools are not able to keep up with the volumes of data. Moore's law is unable to keep up with velocity and volume of data growth. Shared resources and memory prevent scalability and distributed processing. These tools use a Symmetric Multi-Processing (SMP) architecture.
In order to accommodate for the hardware lag (disparity between Moor's Law and data growth), integration/coupling of hardware needs to be utilized. Massively Parallel Processing (MPP) architecture allows for the combination of hardware resources to be pooled to create a more powerful system that is able to meet the demands of processing and storing Big Data while still providing usability of tools using the SMP architecture.

Just came across this interesting visualization of the top tools and technologies for 2016 broken out by functionality. Hope this gives you a good understanding of the key players in each space. Use this as a template to exploring new tools and technologies. I guarantee that employers will start requiring skills with some of these tools real soon. I will try to post some interesting articles based on this image. If there is something specific you want me to cover please mention in the comments.