Data Warehouse System Processes | Lecture #5 | Data Warehouse Tutorial for beginners
Summary
TLDRThis 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
📊 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.
🔄 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.
Mindmap
Keywords
💡Data Warehouse
💡Operational Database
💡Extract and Load
💡Cleaning and Transforming
💡Partitioning
💡Aggregation
💡Backup and Archiving
💡Query Management
💡Open System Technologies
💡Normalization
💡Consistency
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
please consider subscribing and ring the
notification
bell to get the latest updates thanks
for watching
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