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You're a data manager who wants to be better at data warehousing. What's the first step?

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You're a data manager who wants to be better at data warehousing. What's the first step? Data warehousing is the process of collecting, integrating, and storing data from various sources for analysis and reporting. It's a crucial skill for data managers who need to provide insights and value to their organizations. But how do you start building and improving your data warehouse? Here are some tips to help you.

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Shubham Raj

Trinity HealthTech || TECHNOGYM || Data & Business Analyst

1 Assess your needs

The first step is to understand what kind of data you need, how much, and for what purpose. You should also consider the business goals, the users, and the requirements of your data warehouse. For example, do you need real-time or historical data? Do you need to support complex queries or simple reports? Do you need to comply with any regulations or standards? These questions will help you define the scope, the architecture, and the design of your data warehouse.

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Shubham Raj

Trinity HealthTech || TECHNOGYM || Data & Business Analyst
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My first step will always be to Understand the Business and Data. Then, will Create a prototype so and will discuss in my team that this how data will look in Data Data Base. Then, Will go for all the required tools then will plan Model the model with team. Once Planning done will perform ETL after that will do testing whether data is stored in correct manner or not then will secure the data.

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Ahmed Mohssen

Senior Consultant at Prometeia
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The first step is to understand the business requirements, business needs, business vision and the stockholders who will the information and the dashboards be delivered to them.

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2 Choose your tools

The next step is to select the tools and technologies that will support your data warehouse. You have many options, from traditional relational databases to cloud-based platforms. You should evaluate the pros and cons of each option, such as the cost, the performance, the scalability, and the compatibility. You should also consider the skills and preferences of your team, as well as the availability and reliability of the tools. Some popular tools for data warehousing are Microsoft SQL Server, Amazon Redshift, Google BigQuery, and Snowflake.

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Shubham Raj

Trinity HealthTech || TECHNOGYM || Data & Business Analyst
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We can choose warehousing tools like Amazon Redshift(AWS Services), Google BigQuery(Google GCP Services),Microsoft Azure Synapse Analytics (formerly Azure SQL Data Warehouse),Oracle Exadata.

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3 Plan your model

The third step is to plan the data model of your data warehouse. The data model is the structure and organization of your data, such as the tables, the columns, the keys, and the relationships. You should follow some best practices for data modeling, such as using a dimensional model, applying a consistent naming convention, and documenting your metadata. You should also test and validate your data model before implementing it, to ensure its accuracy and quality.

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Akanksha Yadav

Senior Application Engineer at Oracle
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In my experience, the first step to becoming better at data warehousing is to plan your data model. This involves structuring and organizing your data tables, columns, keys, and relationships. Following best practices like dimensional modeling and consistent naming conventions is crucial. Documenting metadata and thoroughly testing the data model before implementation ensures accuracy and quality. By prioritizing the planning phase, I've laid a solid foundation for successful data warehousing projects.

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4 Implement your ETL

The fourth step is to implement the ETL (extract, transform, and load) process of your data warehouse. The ETL process is the workflow that extracts data from various sources, transforms it into a consistent and standardized format, and loads it into your data warehouse. You should use a robust and flexible ETL tool that can handle different types of data, such as structured, unstructured, or streaming data. You should also monitor and optimize your ETL process, to ensure its efficiency and reliability. Some common ETL tools are Talend, Informatica, Pentaho, and Apache Airflow.

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5 Secure your data

The fifth step is to secure your data warehouse from unauthorized access or misuse. You should implement some security measures, such as encrypting your data, enforcing user authentication and authorization, applying role-based access control, and auditing and logging user activities. You should also comply with any relevant laws and regulations, such as GDPR, HIPAA, or PCI DSS. You should also backup and restore your data regularly, to prevent data loss or corruption.

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6 Evaluate your performance

The last step is to evaluate the performance of your data warehouse. You should measure some key indicators, such as the query speed, the data quality, the user satisfaction, and the business value. You should also collect and analyze feedback from your users, to identify any issues or opportunities for improvement. You should also benchmark your data warehouse against your competitors or industry standards, to see how you compare and what you can learn. You should also update and maintain your data warehouse, to keep it relevant and effective.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You're a data manager who wants to be better at data warehousing. What's the first step?

1

2

3

4

5

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1 Assess your needs

2 Choose your tools

3 Plan your model

4 Implement your ETL

5 Secure your data

6 Evaluate your performance

7 Here’s what else to consider

Data Management

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You're a data manager who wants to be better at data warehousing. What's the first step?

1

2

3

4

5

6

7

1 Assess your needs

2 Choose your tools

3 Plan your model

4 Implement your ETL

5 Secure your data

6 Evaluate your performance

7 Here’s what else to consider

Data Management

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