Throughput refers to how much data can be transferred from one location to another in a given amount of time. The throughput is usually measured in bits per second (bit/s or bps), and sometimes in data packets per second or data packets per time slot. A throughput measure is an amount of something per unit time. For operating systems throughput is often measured as tasks or transactions per unit time. For storage systems or networks throughput is measured as bytes or bits per unit time. For processors, the number of instructions executed per unit time is an important component of performance.A system’s throughput defines its overall capability to process data. DBMS throughput can be measured in queries per second, transactions per second, or average response times. DBMS throughput is closely related to the processing capacity of the underlying systems (disk I/O, CPU speed, memory bandwidth, and so on), so it is important to know the throughput capacity of your hardware when setting DBMS throughput goals.
Analytic System

Analytic system supports the evaluation of the process. For example: How the sales orders are trending this month versus last month? Who are our best customers? Sales goal versus actual sales? Is particular promotion working or not?

The interaction with an analytic system takes place exclusively through queries that retrieve data about business processes. Here information is neither created nor modified.

                                                                          Operational Systems                                    Analytical Systems

Purpose	Execution of a business process	Measurement of a business process
Primary Interaction Style	Insert, Update, Delete, Query	Query
Scope of Interaction	Individual transaction	Aggregated transactions
Query Patterns	Predictable and stable	Unpredictable and changing
Temporal Focus	Current	Historic and Current
Design Optimization	Updated concurrency	High-performance query
Design Principle	ER design in 3NF	Dimensional Design (Star Schema, Cube)
Also Known As	OLTP Source System Transaction System	Data Warehouse Data Mart

Operational System vs. Analytic System

Star Schema

Star schema consists of fact and dimension tables. The fact table holds the main data. The dimension tables describe the facts presented in the fact table. Most queries against a star schema follow a consistent pattern. One or more facts are requested, along with the dimensional attributes that provide the desired context. The focus is on summarized data.

The star schema gets its name from its appearance; when drawn with the fact table in the center, it looks like a star or asterisk.

Fact Table

At the core of the star schema is the fact table. In addition to presenting the facts, the fact table includes surrogate keys that refer to each of the associated dimension tables. Each row in the fact table stores facts at a specific level of details. This level of detail is known as the fact table’s grain.

Dimension Tables

In a star schema, a dimension table contains columns representing dimensions. These columns provide context for facts. A well-developed set of dimension tables provides powerful and diverse analytic capabilities. Dimension tables with a large number of attributes maximize analytic value.

Snowflake Schema
In the snowflake schema, multiple centralized fact tables connect to multiple dimension tables. A single dimension table may have multiple parent tables.

ETL
ETL means extract, transform and load. You extract the data, perform the transformation on the ETL machine, and then load the data. ETL is tied to the hardware. There are many ETL tools available in the market.

ELTMPP data warehouse solutions, like Teradata, Oracle Exadata, IBM Netezza and Microsoft Parallel Data Warehouse or Greenplum are examples of these approaches. Usually, the approach is a shared nothing MPP architecture of nodes, which have their own segment of data on their own disks (not a shared memory or disk). Most consequently all components (including the hardware) are perfectly tuned and aligned for this purpose. To achieve this, pre-installed and configured appliances are commonly used, so instead of buying hardware and software individually and trying to make it run well and fast, you get a “black box” (i.e. one or more racks) of components and software that are selected and configured in the best possible way.