Data Warehouse System Processes | Lecture #5 | Data Warehouse Tutorial for beginners

Summary

This tutorial video delves into the processes involved in building a data warehouse. It covers the essential steps including data extraction and loading, cleaning and transforming, backup and archiving, and query management. The video emphasizes the importance of these processes for optimizing query performance, managing data efficiently, and ensuring data integrity. It also highlights the need for data warehouses to adapt as businesses grow, making it a valuable resource for anyone interested in data warehousing solutions.

Takeaways

• 📚 The video introduces various processes involved in a data warehouse and their significance with examples.
• 🔄 The first process discussed is 'Extract and Load', which involves taking data from source systems and loading it into the data warehouse after reconstruction.
• 🕒 The 'Controlling Process' is crucial for determining when to start data extraction and ensuring data consistency.
• 🔍 'Cleaning and Transforming' is the second major process, which includes making the data consistent and structuring it to improve query performance.
• 📊 'Partitioning' the data is part of the cleaning and transforming process, which optimizes hardware performance and simplifies data warehouse management.
• 🔢 'Aggregation' is performed to speed up common queries by analyzing subsets or aggregations of detailed data.
• 🛡️ 'Backup and Archiving' is essential for data recovery in case of data loss, software, or hardware failure, and for keeping old data accessible for restoration.
• 🔎 The 'Query Management Process' is vital for managing queries, speeding up their execution, directing them to effective data sources, and monitoring query profiles.
• 📈 Query management also helps in determining which aggregations to generate based on the information from query profiles, thus improving efficiency.
• 🌐 The tutorial aims to build data warehousing solutions on open system technologies like UNIX and relational databases.
• 🎥 The video is a tutorial that provides an introductory overview of the processes involved in a data warehouse, including extraction, cleaning, backup, archiving, and query management.

Q & A

What are the main processes involved in a data warehouse?

The main processes involved in a data warehouse are extract and load, cleaning and transforming data, backup and archiving data, and query management.

What does the extract and load process involve?

The extract and load process involves taking data from source systems and loading it into the data warehouse, ensuring the data is reconstructed in a way that is suitable for the data warehouse to store.

Why is it important to control the process during data extraction?

Controlling the process is important to determine when to start the data extraction and to check the consistency of the data. It ensures that the tools, logic modules, and programs are executed in the correct sequence and at the right time.

What should be considered when initiating the data extraction?

When initiating the data extraction, it is important to ensure that the data is in a consistent state and represents a single, consistent version of the information to the user.

What is the purpose of cleaning and transforming the data in a data warehouse?

Cleaning and transforming the data helps to speed up queries by making the data consistent and converting the source data into a structure that increases query performance and decreases operational cost.

What is partitioning in the context of data warehousing?

Partitioning is the process of dividing each fact table into multiple separate partitions to optimize hardware performance and simplify the management of the data warehouse.

Why is aggregation important in data warehousing?

Aggregation is important to speed up common queries by relying on the fact that most common queries will analyze a subset or an aggregation of the detailed data.

What is the significance of backup and archiving in a data warehouse?

Backup and archiving are crucial for recovering data in the event of data loss, software failure, or hardware failure. Archiving also allows for the removal of old data in a format that can be quickly restored when required.

What functions does the query management process perform?

The query management process manages and speeds up the execution time of queries, directs queries to their most effective data sources, ensures optimal use of system resources, and monitors actual query profiles.

How does the query management process help in improving the efficiency of the data warehouse?

The query management process improves efficiency by lowering operational costs and ensuring that all system sources are used in the most effective way, as well as by monitoring query profiles to determine which aggregations to generate.

What is the role of open system technologies in building data warehousing solutions?

Open system technologies like UNIX and relational databases provide the foundation for building scalable and flexible data warehousing solutions that can evolve as the business grows.

Outlines

00:00

📊 Introduction to Data Warehouse Processes

This paragraph introduces the topic of the video, which is the various processes involved in a data warehouse. It contrasts the fixed operations and techniques used in operational databases with the evolving nature of data warehouses, which must adapt as business grows. The tutorial will cover building data warehousing solutions using open system technologies and relational databases. Four major processes are highlighted: data extraction and loading, data cleaning and transforming, data backup and archiving, and query management. The paragraph emphasizes the importance of these processes in the context of decision support systems and the need for data warehouses to evolve over time.

05:01

🔄 Detailed Explanation of Data Warehouse Processes

The second paragraph delves into the specifics of each major process in a data warehouse. It starts with the extraction and loading process, explaining the importance of data consistency and the need to reconstruct information for the data warehouse. The paragraph then discusses the cleaning and transforming process, which includes steps like making data consistent, structuring it to increase query performance, and partitioning data to optimize hardware performance. The backup and archiving process is crucial for data recovery in case of failures, and it involves keeping regular backups and archiving old data for quick restoration. Lastly, the query management process is described, which includes managing queries, speeding up execution time, directing queries to effective data sources, ensuring efficient use of system sources, and monitoring query profiles. The paragraph concludes by summarizing the processes discussed and encouraging viewers to subscribe for updates.

Keywords

💡Data Warehouse

A data warehouse is a large, centralized repository of data designed for query and analysis rather than for transaction processing. It relates to the video's theme as it is the main subject being discussed. In the script, the data warehouse is described as evolving with the business and requiring different processes compared to operational databases.

💡Operational Database

An operational database is a database that is used for the day-to-day operations of a business. It is mentioned in the script to contrast with a data warehouse, highlighting that operational databases use techniques suitable for transaction processing but not for decision support systems like a data warehouse.

💡Extract and Load

Extract and load (EtL) refers to the process of extracting data from external sources and loading it into a data warehouse. In the script, this process is the first major step in the data warehouse operations, where data is taken from source systems and prepared for storage in the data warehouse.

💡Cleaning and Transforming

Cleaning and transforming data involves making the extracted data consistent and suitable for the data warehouse's structure. The script explains that this process is crucial for speeding up queries and reducing operational costs by structuring the data to support performance requirements.

💡Partitioning

Partitioning in the context of a data warehouse is the process of dividing a large fact table into multiple separate partitions. The script mentions partitioning as a method to optimize hardware performance and simplify data warehouse management, making queries faster and more efficient.

💡Aggregation

Aggregation in data warehousing is the process of summarizing detailed data to speed up common queries. The script describes aggregation as relying on the fact that most queries analyze a subset or summary of the detailed data, thus improving query performance.

💡Backup and Archiving

Backup and archiving are processes that ensure data recovery in case of data loss or system failure. The script explains that regular backups are necessary, and archiving involves storing old data in a way that allows for quick restoration when needed.

💡Query Management

Query management is the process of managing, optimizing, and directing queries to their most effective data sources. The script discusses this as a critical process for speeding up query execution time, ensuring efficient use of system resources, and monitoring query profiles.

💡Open System Technologies

Open system technologies refer to systems that are based on open standards and can be used with a variety of software and hardware. The script mentions building data warehousing solutions on open system technologies like UNIX and relational databases, emphasizing flexibility and interoperability.

💡Normalization

Normalization in databases is the process of organizing data to minimize redundancy and dependency. The script refers to normalization as a technique used in operational databases to keep tables small and efficient, but notes that it may not be suitable for data warehouses.

💡Consistency

Consistency in the context of data warehousing refers to the state where the data represents a single, accurate version of the truth. The script discusses the importance of data being in a consistent state when extracted for the data warehouse, ensuring reliability for users and query accuracy.

Highlights

Introduction to various processes involved in a data warehouse.

Explanation of the difference between operational databases and data warehouses in terms of data handling and query execution.

The significance of data warehouse processes for decision support systems that require flexibility for future queries.

Building data warehousing solutions on open system technologies like UNIX and relational databases.

Identification of four major processes in a data warehouse: extract and load, clean and transform, backup and archive, and manage queries.

Details on the extract and load process, emphasizing the importance of data reconstruction for the data warehouse.

The role of the controlling process in determining when to start data extraction and ensuring data consistency.

Importance of data extraction consistency for representing a single version of information to the user.

The process of loading data into a temporary data store for cleaning and consistency checks.

Steps involved in cleaning and transforming data to improve query performance and reduce operational costs.

The transformation of source data into a structured format to support performance requirements.

Partitioning data to optimize hardware performance and simplify data warehouse management.

Aggregation of data to speed up common queries by analyzing subsets or aggregations of detailed data.

The necessity of backup and archiving data for recovery in case of data loss or system failure.

Archiving data in a format that allows quick restoration for scenarios like month-on-month sales analysis.

Query management process functions, including managing queries, speeding up execution time, and directing queries to effective data sources.

Importance of query management in ensuring system sources are used effectively, lowering operational costs, and improving process efficiency.

Monitoring query profiles to inform warehouse management on which aggregations to generate.

Summary of the introductory part of data warehouse processes, including extract and load, clean and transform, backup and archive, and query management.

Transcripts

[Music]

hello everyone welcome to my channel

and in this tutorial we are going to see

the various processes involved in a data

warehouse

so in the previous lecture we have seen

the delivery process

of a data warehouse so in this lecture

we are going to see

what are the different processes

involved in a data warehouse

the significance with some simple

examples

so without further ado let's get into it

so in the data warehouse we have a fixed

number of operations

to be applied on an operational database

and we have

well defined techniques to use a

normalized data

to keep the tables small etc so these

techniques are suitable for delivering a

solution

but in case of decision support systems

we do not know what

query and operation which needs to be

executed in future

so therefore the techniques applied on

the operational database

are not suitable for the data warehouse

as the data warehouse should evolve

as the business grows so in this

tutorial we will discuss

how to build the data warehousing

solutions on

open system technologies like unix and

relational databases

so in data warehouse there are four

major processes

so the first one is extract and load the

data

next one is cleaning and transforming

the data then the backup

and archiving the data and managing the

queries and directing

them to the appropriate data sources so

these are the major processes which

we'll see

in detail so the first process is

extracting

loading the data so the data extractions

takes the data from the source systems

and load it to the data warehouse

so the data load takes the extracted

data and loads it

into the data warehouse so you have to

remember one thing

before loading the data into data

warehouse the information extracted from

the external sources

must be reconstructed which will be

feasible for the data warehouse

to store that data so in this we have to

consider three points clearly

the first one is controlling the process

so the controlling process

involves determining when to start the

data extraction

and the consistency to check on the data

if the data looks good

controlling process ensures that the

tools the logic modules

and the programs which are executed in

correct sequence and the correct time

it is very important process the next

one is

when to initiate the extract so the data

needs to be in a consistent state

when it is extracted that is nothing but

the data warehouse

should represent a single consistent

version

of the information to the user so for

example

in a financial data warehouse which

stores the financial data such as

general ledger account payable and

account receivable

it is very illogical to merge the

consolidated

data when the quarterly reports are

being generated

so this would mean that the latest data

will not be refreshed as per the user's

requirements

and the next point you have to consider

is loading the data

so after extracting the data it is

loaded into a temporary data store

where it is cleaned up and made it

consistent

so the consistency checks are executed

only when all the data sources

have been loaded into the temporary data

store

so this is our first process which is

extract and loading

our next process is cleaning and

transforming the data

so once the data is extracted and loaded

into the temporary data store

it is time to perform the cleaning and

transforming the data

so here you can see the list of the

steps which is involved in the cleaning

and transforming stage so the first one

is clean and transform the loaded data

into a structure so the cleaning and

transforming

helps to speed up the queries so it can

be done by making the data consistent

so the transforming involves converting

the source data

into a structure so structuring the data

increases the query performance and

decreases the operational cost

so the data contained in a data

warehouse must be transformed to support

the performance requirements and control

the ongoing

operational cost so it is very crucial

the next step is partitioning the data

so it will optimize the hardware

performance

and simplify the management of data

warehouse

so here we partition each fact table

into a multiple separate

partitions so what it will do that the

huge

table which contains the billions of

record

will be partitioned so that the queries

will take

shorter time for the quick analysis and

lower operational cost

and it will also optimize the hardware

performance

and will avoid the long running queries

over the platform

so the next step is aggregation so the

aggregation is required to speed up the

common queries

so the aggregation relies on the fact

that most common queries

will analyze a subset or an aggregation

of the detailed data

our next process which is involved in

data warehousing

is backup and archiving the data

it is also very important process so in

order to recover the data

in event of data loss software failure

or a hardware failure

it is very necessary to keep the regular

backups

so the archiving involves removing the

old data

from the system in a format that allows

it quickly restored whenever required

so for example in a sales analysis data

warehouse

for xyz company it may be required to

keep the data

for at least four years with the latest

one year of data

being kept online in these scenarios

there is often requirement to be able to

do the month-on-month comparison

for the year and the last year so in

this case we require some data to be

restored from the archive and

one last process is query management

process

so this process performs this given

functions

so the first one is it manages the

queries

the next one is it helps to speed up the

execution time of the queries

so it is very important as when you

require the quick

analysis over the stored data it is

very crucial that the execution time of

the queries

will be as low as possible the next one

is

the directs the queries to their most

effective data sources

the next one is it ensures that all the

system sources are used in the most

effective way

so it will lower the operational cost

and improve the efficiency of the

process

and the last one is monitors the actual

query profiles

this functions involves the query

management process

so the information which is generated in

this process

is used by the warehouse management

process

to determine which aggregation to

generate

so this process does not generally

operate during the regular loads

of the transformation into the data

warehouse

so these are all the processes which are

involved in a data warehouse

which we have discussed in brief with

some simple examples

in this case we have seen the

introductory part of the processes which

are involved in a data warehouse

the processes which are extract and load

process

clean and transform process backing up

and archiving the data

and the query management process so if

you like this video

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for watching