The theory of databases is not covered in this course, but you should know their basic concepts.
When opening a new tab, a large amount of data is stored in a database that must be retrieved
and updated regularly. Data warehouses store data permanently for faster retrieval and updation
for analysis purposes. Legacy data needs to be stored separately from the main system.
Sorting Algorithms
The example used here is sorting arrays in ascending or descending order. An algorithm is a
series of steps to create a process. When sorting an array, steps must be taken to sort in
ascending or descending order. The steps taken to sort an algorithm into an array define the
algorithm.
Data Warehousing and Big Data
Data is the fuel of big algorithms these days, so it's essential not to lose the data. To prevent
data loss, the data is separated from the main system and stored in what is known as legacy
data. Data warehousing, on the other hand, deals with how to store legacy data efficiently in
different types of algorithms, analysis, and distributed systems that can handle huge databases
that regular applications or algorithms cannot. Big data is a separate field that requires a
different set of algorithms and analysis.
It's essential to understand data warehousing and big data, though they are beyond the scope
of this course. Do not use these terms, but understand their significance. The best way to learn
data structures and algorithms is to study C programming, specifically stacks and heaps. In the
context of C programming, understand that the memory layout of a C program is divided into
segments, such as the stack, heap, and segments for static and global variables.
Memory Layout of a C Program
The stack frame or activation record of the main function is always created first, and subsequent
activation records of other functions such as fun1 and fun2 are created when called. Variables
are created in their respective activation records. To better understand memory usage, think of it
as "looking back at your memory" and "reading your memory back from the page" to see what
you need to access. Understanding memory usage helps you to be able to optimize your
algorithms and take full advantage of available resources
When opening a new tab, a large amount of data is stored in a database that must be retrieved
and updated regularly. Data warehouses store data permanently for faster retrieval and updation
for analysis purposes. Legacy data needs to be stored separately from the main system.
Sorting Algorithms
The example used here is sorting arrays in ascending or descending order. An algorithm is a
series of steps to create a process. When sorting an array, steps must be taken to sort in
ascending or descending order. The steps taken to sort an algorithm into an array define the
algorithm.
Data Warehousing and Big Data
Data is the fuel of big algorithms these days, so it's essential not to lose the data. To prevent
data loss, the data is separated from the main system and stored in what is known as legacy
data. Data warehousing, on the other hand, deals with how to store legacy data efficiently in
different types of algorithms, analysis, and distributed systems that can handle huge databases
that regular applications or algorithms cannot. Big data is a separate field that requires a
different set of algorithms and analysis.
It's essential to understand data warehousing and big data, though they are beyond the scope
of this course. Do not use these terms, but understand their significance. The best way to learn
data structures and algorithms is to study C programming, specifically stacks and heaps. In the
context of C programming, understand that the memory layout of a C program is divided into
segments, such as the stack, heap, and segments for static and global variables.
Memory Layout of a C Program
The stack frame or activation record of the main function is always created first, and subsequent
activation records of other functions such as fun1 and fun2 are created when called. Variables
are created in their respective activation records. To better understand memory usage, think of it
as "looking back at your memory" and "reading your memory back from the page" to see what
you need to access. Understanding memory usage helps you to be able to optimize your
algorithms and take full advantage of available resources
