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Data warehouse vs Database

The major difference between Datawarehouse 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.

What is Data Warehouse Designing?

Data warehouse design is a process that describes task description, 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 designing is the most crucial part of Data Warehouse Architecture and Analytics. It follows the approach of “The better the Query optimization, the better will be the performance output.”

Why is Designing important?

  • Some points prove designing Data Warehouse Architecture is significant, either a database or a data warehouse.
  • If the database or data warehouse  is designed correctly and layout https://www.talend.com/https://www.talend.com/ is maintained correctly on logical as well as physical level, then it is always easy to handle any modifications (if required)
  • Design helps to identify recovery and problem identification points.
  • Efficient design is cost-effective and saves the storage space up to a large extent.
  • Data Warehouse Architecture maintains integrity and data accuracy as the data structure is managed correctly and designed for crucial times, such as a disaster.

Database Development Life cycle

Database development follows a cycle to develop efficient databases. This life cycle follows the following stages -
1. Requirement Analysis Before implementing Database Design architecture at the physical level, the first thing is to create a logical view or model. The requirement analysis does the same. In this, you have to think of data from every perspective, i.e., Who will be using it? In what way? And How many user types will be there? And so on. Try to layout every aspect of data generation and usage, such as How much data will be generated? Where is it stored? What kind of data will be created? And so on. The more in-depth will be the analysis, the better design can be obtained through it.
2. Organization of data into tables or table structures
  • Once the logical layout is planned and analysis is done, you need to create some view of those data instances.
  • Generate table structures and their data types.
  • Data types must be valid for that entity only. The better suitable data type usage will provide adequate storage space and throughput.
3. Keys and relationships
  • Keys are used to providing some authentication to data like uniqueness and relationship to other tables.
  • Relationships need to be implemented in such a way that data can be obtained faster and store faster. Try to implement only mandatory connections.
  • Keys and relationships define data integrity in Database Design architecture.
4. Normalization
  • When the logical structure is ready, one can implement normalize tables to make tables more structured and correct.
  • Normalization must be applied according to requirements; i.e., this is not mandatory to design 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 security will be maintained properly.
  • Gather all warehouse related requirements.
  • Set up the physical environment by defining Modeling, ETL processes.
  • Data Warehouse Architecture defines OLAP cube requirements and dimensions.
  • Check how the database is working and what will be the Query structure.
  • Optimize Query structure to achieve proper tuning data warehouse.
  • Once all this is Done, Get it into production.

Tools for Database Designing

Database designing tools help to develop some complex Database Design architecture. Following are some tools that can help to achieve proper functionality as needed:
  • SQL Server Database Modeler
  • Lucidchart
  • Visual Paradigm ERD tools
  • IBM InfoSphere

Tools for Data Warehouse Architecture Designing

Some of the top-level data warehouse designing tools are -

Management of Database and Data Warehouse Architecture

Monitoring Databases

Monitoring is the process of checking data performance from different matrics. Monitoring helps to identify issues related to internal working, performance, and existing solutions. It also helps to develop different types of databases that can overtake an existing solution with powerful matrics representation.

How to monitor Data Matrics?

Several tools help to monitor data matrics, including Graph Database Architecture. These matrics need to be properly implemented on databases and warehouses.
Step 1. Define the range of metrics to find bugs and issues. If matrics at some point didn't work according to that range, there must be an issue associated with that.
Step 2. While monitoring Database Design architecture never considers the current flow, think for the entire problem set.
Step 3. Thinking outside the box is, but internal functioning must be known during Monitoring.

Best Monitoring Tools

  • Prometheus
  • Azure
  • Redshift
  • Hadoop

Performance Analysis

Visual feedback and data analytics provides a discipline in data monitoring to analyze the performance. This performance analysis can be used to track and build a more powerful tool for monitoring or development.

How to analyze?

  • Active monitoring system establishment provides the root of the problem cause.
  • Sampling monitoring data throughout for further monitoring.
  • By establishing multi-dimensional data monitoring of Database Design architecture.
  • 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 replica of data to another location for recovery and other purposes.
data visualization tableau
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Importance of Data Backup

Sometimes, some circumstances such as power shutdown, system out of memory, and so on led to the loss of data. In that situation, backups are helpful. Backups provide a mirroring effect to databases and Data Warehouse Architecture as we can use them in the future for new setup or Database Testing purposes. Backup Tools Learn More About Data Warehouse Modernization Services

Disaster and Recovery

In Database Design architecture, Disaster is the case that occurs when a server or system goes down or becomes unavailable during the execution of data-related tasks. Disaster always led to issues such as data loss, partial commits on data, and so on. Recovery is the process of 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?

  • Disaster can occur in logical errors such as software bugs, viruses, or corrupted data files.
  • Physical damages can also occur in the form of disk damage or server damage.
  • Natural disasters are more dangerous such as fire, earthquake, etc.

Why is Recovery essential?

Data recovery is essential in any of the following cases -
  • Disasters such as natural, physical or logical
  • Power shutdowns failures and internal workflow errors

Tools for disaster recovery management

  • Azure
  • Redshift
  • Hadoop
  • Informatica

A Relational Approach to Data Warehouse and Database Design

A properly designed Database Design architecture helps to identify recovery and disaster points. It also helps to maintain integrity and data accuracy. For managing your Data Warehouse and Database Design, we recommend getting expert advice from our Certified Big Data Specialists.

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