Data Warehouse and Database Design Architecture

What is Database Designing?

Database design defines the process in which requirements, structure, relationships and all are analyzed in detail. The basic flow 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 a key whereas 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 the Data warehouse and Analytics. It follows the approach of “The better the Query optimization, better will be the performance output.”

Why is Designing important?

  • Some points prove designing is significant either it is a database or a data warehouse.
  • If database or data warehouse is designed correctly and layout 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.
  • It helps to maintain 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 databases at the physical level, the first thing is to create a logical view or model of that. 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 lay out 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, a 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.

4. Normalization

  • Now when the logical structure is ready, one can implement normalize tables to make tables more structured and correct.
  • Normalization must be applied according to requirement, i.e., this is not mandatory to design secondary database structure.

Data Warehouse Development Life cycle

Data warehouse development life cycle follows some steps that help to tune the warehouse and security will be maintained properly.

  • Gather all warehouse related requirements.
  • Setup the physical environment by defining Modeling, ETL processes.
  • Define OLAP cube requirements and dimensions.
  • Check how the database is working and what will be the Query structure.
  • Optimize Query structure to achieve proper tunning data warehouse.
  • Once all this is Done Get it into production.

Tools for Designing Database

Database designing tools help to develop some complex data models. 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 Designing Data Warehouse

Some of the top level data warehouse designing tools are –

Management of Database and Data Warehouse

Monitoring Databases

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

How to monitor?

Several tools help to monitor data matrics. These matrics need to be properly implemented on databases and warehouses.

Define the range of matrics 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.

While monitoring never considers the current flow, think for the entire problem set.

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 for issue tracking and building 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.
  • Highly available servers and highly scalable data sources can help to trace the roots of data issues before they arise.

Performance Analysis Tools

  • Oracle
  • Informatica
  • Redshift
  • Talend

Data Backup

Backup is the process of creating duplicate copies or replica of data to another location for recovery and other purposes.

Importance of Data Backup

Sometimes there may arise some circumstances such as power shutdown, system out of memory and so on that led to the loss of data. In that situation backups are helpful.

Backups provide mirroring effect to databases and data warehouse systems as we can use them in the future for new setup or testing purposes.

Backup Tools

  • MS SQL
  • Cassandra
  • Snowflake
  • Azure

Disaster and Recovery

Disaster is the case that occurs when a server or system goes down or become 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 to restore data or data states from a certain point. Most of the times 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 the form of 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 helps to identify recovery and disaster points. It also helps to maintain integrity and data accuracy. For managing your Database and Data warehouse, we advise taking the following steps –

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