…similar solutions for addressing the scalability challenges:

On the Data Tier we see the following:

1. Adding a caching layer to take advantage of memory resources availability and reduce I/O overhead

2. Moving from a database-centric approach to partitioning, aka shards

On the Business Logic Tier:

3. Adding parallelization semantics to the application tier (e.g., MapReduce)

4. Moving to scale-out application models to achieve linear scalability

5. Moving away from the classic two-phase commit and XA for transaction processing (See: Lessons from Pat Helland: Life Beyond Distributed Transactions)

… already have equivalents in the .NET Framework 3.5.

1. Filter = Where

2. Map = Select

3. Reduce = Aggregate

Each of these are implemented as extension methods for IEnumerable. So, we can rewrite the code like this:

static void Main()
{

var numbers = new int[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 };
var sum = numbers.Where(x => (x % 2) == 0).Select(x => x * x).Aggregate(0, (x, y) => x + y);
Console.WriteLine(“Sum: {0}”, sum);

}