Introduction to Data Warehouse and Database
The major difference between Data warehouse and Database Design is that the database contains information in a sequence of two-dimensional tables whereas, the Data warehouse consists of data in multi-dimensional form, made up of Rows and Columns.What is Database Designing?
Database design defines the process in which requirements, structure, relationships, and all are analyzed in detail. The Database Design architecture will always be specific as Requirement analysis, development, and then Implementation. Requirement analysis is the essential part of database designing. The Concept of Database designing is key, whereas the SQL queries part is relatively very simple.Structured data is integrated into the traditional enterprise data warehouse from external data sources using ETLs. Click to explore about, Data Lake vs Warehouse vs Data Lake House
What is Data Warehouse design?
Data warehouse design is a process that describes task descriptions, time requirements, Deliverables, and pitfalls. This phase occurs when team tool selection has been made and the data warehouse structure needs to be described. Data warehouse design is the most crucial part of Data Warehouse Architecture and Analytics. It follows the approach of “The better the query optimization, the better the performance output will be.”
Why is Design important?
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Some points prove designing a Data Warehouse Architecture is significant, either a database or a data warehouse.
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If the database or data warehouse is designed correctly and the layout https://www.talend.com/https://www.talend.com/ is maintained correctly on logical as well as physical levels, then it is always easy to handle any modifications (if required)
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Design helps to identify recovery and problem identification points.
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Efficient design is cost-effective and saves the storage space up to a large extent.
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Data Warehouse Architecture maintains integrity and data accuracy as the data structure is managed correctly and designed for crucial times, such as a disaster.
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What is the Database Development Life Cycle?
Database development follows a cycle to develop efficient databases. This life cycle follows the following stages -
Requirement Analysis
Before implementing database design architecture at the physical level, the first thing to do is to create a logical view or model. The requirement analysis does the same. In this, you must think of data from every perspective, i.e., Who will use it?
In what way? How many user types will there be? And so on. Try to lay out every aspect of data generation and usage, such as how much data will be generated, where it is stored, what kind of data will be created, and so on. The more in-depth the analysis, the better the design can be.
Organization of data into tables or table structures
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Once the logical layout is planned and analysis is done, you need to create some view of those data instances.
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Generate table structures and their data types.
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Data types must be valid for that entity only. Using the best-suited data type will provide adequate storage space and throughput.
Keys and Relationships
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Keys provide some authentication to data like uniqueness and relationship to other tables.
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Relationships need to be implemented in such a way that data can be obtained faster and stored faster. Try to implement only mandatory connections.
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Keys and relationships define data integrity in Database Design architecture.
Normalization
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When the logical structure is ready, one can implement normalize tables to make tables more structured and correct.
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Normalization must be applied according to requirements; i.e., this is not mandatory when designing a secondary database structure.
The two main approaches to the design of a database are referred to as bottom-up and top-down. Source- Database System Development Lifecycle (DSDLC)
Data Warehouse Development Life cycle
The Data Warehouse Architecture development life cycle follows some steps that help to tune the warehouse and ensure proper security maintenance.
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Gather all warehouse-related requirements.
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Set up the physical environment by defining Modeling, ETL processes.
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Data Warehouse Architecture defines OLAP cube requirements and dimensions.
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Check how the database is working and what will be the Query structure.
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Optimize the query structure to achieve proper tuning of the data warehouse.
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Once all this is done, Get it into production.
Tools for Database Designing
Database design tools help develop complex database design architecture. Following are some tools that can help to achieve proper functionality as needed:
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SQL Server Database Modeler
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Lucidchart
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Visual Paradigm ERD tools
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IBM InfoSphere
Tools for Data Warehouse Architecture Designing
Some of the top-level data warehouse designing tools are --
Informatica
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Management of Database and Data Warehouse Architecture
The steps for the management of the Database and Data Warehouse Architecture are listed below:
Monitoring Databases
Monitoring is the process of checking data performance from different matrices. It helps to identify issues related to internal workings, performance, and existing solutions. Monitoring also helps develop different databases that can overtake an existing solution with powerful matrix representation.
How to monitor Data Metrics?
Several tools, including Graph Database Architecture, help monitor data matrices, which need to be properly implemented in databases and warehouses.
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Define the range of metrics to find bugs and issues. If matrices didn't work according to that range at some point, there must be an issue associated with that.
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While monitoring Database Design architecture never considers the current flow, think for the entire problem set.
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Thinking outside the box is important, but internal functioning must be known during Monitoring.
Best Monitoring Tools
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Prometheus
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Azure
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Redshift
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Hadoop
Performance Analysis
Visual feedback and data analytics provide a discipline in data monitoring to analyze performance. This performance analysis can be used to track and build a more powerful tool for monitoring or development.
How to analyze?
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The establishment of an active monitoring system provides the root of the problem.
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Sampling monitoring data throughout for further monitoring.
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By establishing multi-dimensional data monitoring of Database Design architecture.
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Highly available servers and highly scalable data sources can trace the roots of data issues before they arise.
Performance Analysis Tools
Data Backup
Backup is the process of creating duplicate copies or replicas of data and moving them to another location for recovery and other purposes.
Importance of Data Backup
Sometimes, power shutdown, system out of memory, and so on lead to data loss. In that situation, backups are helpful. Backups provide a mirroring effect to databases and data warehouse architecture; we can use them in the future for new setups or database testing purposes.
Disaster and Recovery
In Database Design architecture, Disaster occurs when a server or system goes down or becomes unavailable during the execution of data-related tasks. Disaster always leads to issues such as data loss, partial data commits, and so on. Recovery is restoring data or data states from a certain point. Most of the time, recovery is needed during a disaster on databases and data warehouses. Data Recovery can be made from redo logs, checkpoints, replicas, and other sources.
Disaster cases?
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Disasters can occur due to logical errors such as software bugs, viruses, or corrupted data files.
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Physical damage can also occur in the form of disk or server damage.
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Natural disasters, such as fires, earthquakes, etc., are more dangerous.
Why is Recovery essential?
Data recovery is essential in any of the following cases --
Disasters such as natural, physical or logical
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Power shutdown failures and internal workflow errors
Tools for disaster recovery management
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Azure
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Redshift
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Informatica
A Relational Approach to Data Warehouse and Database Design
An adequately designed Database Design architecture helps to identify recovery and disaster points. It also helps to maintain integrity and data accuracy. We recommend getting expert advice from our certified big data specialists to manage your data warehouse and database design.
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