Data warehousing best practices

How can data warehouse improve performance?
By partitioning your tables based on criteria such as date ranges or regions, you can improve query performance by reducing the amount of data that needs to be scanned.
Compression: Compression can significantly reduce the amount of storage required for your data warehouse..
What are data warehouse strategies?
Your data warehouse strategy expands and enables your existing data management efforts.
Data management involves everything from modernizing your data stack to putting together the right data analytics team.
It's a combination of all the tools, processes, and people who make data useful to your business..
What are the 5 basic stages of the data warehousing process?
The architecture of a modern data warehouse
extracting data from other sources.cleansing and preparing the data into a well-defined format.loading the data into the warehouse.maintaining and validating the data integrity on an ongoing basis..
What are the 5 factors to consider in data warehousing?
5 Critical Factors to Consider When Choosing a Data Warehouse
Business Requirements.
It is imperative to determine your business needs and specific use cases. Cost Estimations. Capabilities and Technology. Accessibility and Speed. Scalability..
What are the 5 factors to consider in data warehousing?
Data types: what type of data you want your warehouse to store.
Scale: the amount of data you plan to store.
Performance: how quickly you need your data when you query it.
Maintenance: how much engineering effort you're willing and able to dedicate to your warehouse..
What makes a good data warehouse?
Steps in Data Warehousing
Extraction of data – A large amount of data is gathered from various sources.Cleaning of data – Once the data is compiled, it goes through a cleaning process. Conversion of data – After being cleaned, the format is changed from the database to a warehouse format..
What makes a good data warehouse?
Data types: what type of data you want your warehouse to store.
Scale: the amount of data you plan to store.
Performance: how quickly you need your data when you query it.
Maintenance: how much engineering effort you're willing and able to dedicate to your warehouse..
5 strategies for data security and governance in data warehousing
Perimeter Security: The First Line of Defense.Encryption: Securing data in transit and at rest.Access Control: Limiting entry to authorized individuals.Authentication and authorization: Verifying and granting access.
There are multiple ways for users to query or access the data stored in a database or data warehouse.
1. Direct Queries
2. Intermediate Tables
3. Common Table Expressions (CTEs)
4. Data Sanitization
5. Indexing
6. Batching
7. Explain Plan
8. Snowflake

Data Warehouse Best Practices

Finding Data Warehouse Need In Your Company.
Begin With Solid Master Data Management (MDM) Practices.
Analyze How Frequently You Need to Load data.
Integrate Change Data Capture (CDC) Policy for Real-Time Data.
Prefer ELT Tools Instead Of ETL.
Define Permissions And Access Controls In Advance.

1. Data Warehouse Best Practices: Impact of Data Sources 2

Data Warehouse Best Practices: Impact of Data Sources

Kind of data sources and their format determines a lot of decisions in a data warehouse architecture

Data Warehouse Best Practices: The Choice of Data Warehouse

One of the most primary questions to be answered while designing a data warehouse system is whether to use a cloud-based data

Data Warehouse Best Practices: ETL vs ELT

The movement of data from different sources to data warehouse and the related transformation is done through an extract-transform-load or an

Data Warehouse Best Practices: Architecture Consideration

Designing a high-performance data warehouse architecture is a tough job and there are so many factors that need to be considered

Data Warehouse Best Practices: ETL Tool Considerations

Once the choice of data warehouse and the ETL vs ELT decision is made

Data Warehouse Best Practices: Identify Why You Need A Data Warehouse

Organizations usually fail to implement a Data Lake because they haven’t established a clear business use case for it

Data Warehouse Best Practices: Have A Data Flow Diagram

By having a Data Flow Diagram in place

Other Data Warehouse Best Practices

Other than the major decisions listed above, there is a multitude of other factors that decide the success of a data warehouse implementation

How can enterprise data modeling help with data warehousing?

The proven approach to seamlessly designing and deploying a data warehouse is putting enterprise data modeling at the center of your data warehousing process

By doing so, you can ensure a seamless path from design to development and deployment

How does a data warehouse improve data quality?

Data is collected at regular intervals from source systems such as ERP applications that store company information

When this data is moved to a dedicated data warehouse, data quality is improved by cleansing, reformatting, and enriching with data from other sources

What are the best practices in data warehouse design?

One of the best practices in data warehouse design is pruning messy data flows to reduce technical debt while you are improving your data infrastructure

A DFD is a valuable step in this process

It enables you to visualize the process for yourself and helps you convince others of the benefits of retooling their data systems

Best Practices for Building a Data Warehouse

1. Identify Why You Need a Data Warehouse ...
2. Have an Agile Approach Instead of a Big Bang Approach ...
3. Analyze and Understand Your Data ...
4. Analyze How Frequently You Need to Load Data ...
5. Define a Change Data Capture (CDC) Policy for Real-Time Data ...
6. Prefer ELT Tools Instead of ETL ...
7. Choose Whether You Want it On-Premise or in the Cloud ...

Data warehousing best practices

How can data warehouse improve performance?

What are data warehouse strategies?

What are the 5 basic stages of the data warehousing process?

The architecture of a modern data warehouse

What are the 5 factors to consider in data warehousing?

5 Critical Factors to Consider When Choosing a Data Warehouse