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Here's how you can identify the key competencies assessed in data warehousing performance evaluations.

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Assessing the performance of your data warehousing operations is crucial for maintaining an efficient and effective data management system. Data warehousing involves the storage and analysis of large volumes of data, and its performance can significantly impact your business intelligence outcomes. To ensure that your data warehousing processes are up to par, it's important to understand the key competencies that should be evaluated. These include the ability to manage data effectively, the speed and accuracy of data retrieval, the integration of various data sources, and the security measures in place to protect your data. By focusing on these areas, you can identify potential improvements and optimize your data warehousing for better decision-making.

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
Vincent Rainardi

Data Architect & Data Engineer
Saruq Shaikh

Data Platform Manager | Data Arch | Palantir Data Foundry | SQL | Pyspark | BI | SAFe? 6 Certified | Deakin MBA 2024'

1 Data Mastery

Understanding how to manage and manipulate large datasets is a fundamental competency in data warehousing. During performance evaluations, you'll want to assess an individual's proficiency in data modeling, ETL (Extract, Transform, Load) processes, and data normalization. Proficiency in SQL (Structured Query Language) is also essential, as it's the standard language for database management. An individual's ability to create efficient data structures and their understanding of data warehousing schemas like star and snowflake models are indicators of their mastery in handling data within a warehousing context.

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Saruq Shaikh

Data Platform Manager | Data Arch | Palantir Data Foundry | SQL | Pyspark | BI | SAFe? 6 Certified | Deakin MBA 2024'
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Instead of jumping straight into the ETL process, it's important to first understand the data you're dealing with. This means knowing where the data comes from, what it looks like, and what you need to do with it. Take the time to analyze the data and figure out what it can tell you. This makes it easier to handle and transform the data later on. Once you understand the data and have a plan, you can then start the ETL process with confidence. Having skills in ETL, SQL, and Python is crucial for a Data Engineer, as these tools help in effectively managing and mastering data. In my 7+ years of working with data, Datalake and Data Warehouses this key principal has always helped me implement things in a better way.

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2 Retrieval Efficiency

A key aspect of data warehousing is the ability to retrieve data quickly and accurately. When evaluating performance, consider how well your system responds to queries and how efficiently it can process large volumes of data. Look for competencies in optimizing query performance through indexing and partitioning, as well as an understanding of OLAP (Online Analytical Processing) systems, which enable swift computation of complex queries. The ability to troubleshoot slow queries and provide solutions for improving query response times is a valuable skill set in this domain.

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Kadhirvelu Ratnasabapathi

VP at Seacoast Bank, Aspiring CDO
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Should be well-versed in writing SQL queries when it comes to performance evaluation for data warehouses. When we write Source Qualifier SQL queries that is used to source data from database in the ETL jobs, an efficient SQL is needed to improve the performance. In my experience, I have noticed simple rearrangement of tables (low volume first) in joins improved the performance. Sometimes database hints in the query helped. Efficiency in sourcing the data improves the data warehousing solutions.

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3 Integration Skills

Integration skills are vital in a data warehouse where data from various sources must be combined into a cohesive whole. Evaluate how effectively your team can merge disparate data sets, ensuring consistency and accuracy. This includes assessing knowledge of data integration tools and methodologies, such as data warehousing appliances and middleware solutions. Competence in managing data from different systems, like CRM (Customer Relationship Management) or ERP (Enterprise Resource Planning), and transforming it into a unified format is a critical measure of performance in data warehousing.

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4 Security Protocols

In today's digital age, security is paramount, especially when it comes to sensitive data stored in warehouses. Performance evaluations should measure an individual's understanding of security protocols and their ability to implement measures like encryption, access controls, and auditing to safeguard data. It's important to evaluate the awareness of potential security threats and the proactiveness in applying best practices to prevent data breaches. A strong grasp of compliance regulations relevant to data security is also a key competency.

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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Topics like access control on column, table and row, encryption of columns and tables, IPs whitelist or connection protocols like SFTP or HTTPS are points to evaluate to guarantee the security and privacy on your data warehouse.

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5 Analytical Prowess

The ultimate goal of data warehousing is to support business decisions with robust analytics. Therefore, a critical competency is the ability to analyze warehoused data effectively. This includes skills in using BI (Business Intelligence) tools to create insightful reports and dashboards. Evaluate how well individuals can interpret complex data sets and extract meaningful patterns or trends. The capacity to translate analytical findings into actionable business strategies is a hallmark of top performance in data warehousing.

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6 System Optimization

Lastly, system optimization is a competency that ensures your data warehouse operates at peak efficiency. Evaluate skills related to the maintenance and tuning of the data warehouse infrastructure. This includes assessing knowledge of hardware performance, network configurations, and database optimization techniques. The ability to foresee potential system bottlenecks and preemptively address them is essential for maintaining a smooth and responsive data warehousing environment.

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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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Vincent Rainardi

Data Architect & Data Engineer
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In my opinion the key competencies in data warehousing are: 1. Data modelling 2. ETL 3. SQL 4. Data analysis 5. Data governance 6. Reporting Data modelling skills, particularly dimensional modelling and data vault, is required for designing and building a data warehouse. Data governance includes creating data catalog, creating data quality checks and monitoring, setting up data owners and data stewards, and the process to govern data access security, mastering and maintenance. Reporting includes creating dashboards and reports, as well as getting the data that the users need. It is the user data requirements that shape the design of the data warehouse so it is vital to understand it.

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Here's how you can identify the key competencies assessed in data warehousing performance evaluations.

1

2

3

4

5

6

7

1 Data Mastery

2 Retrieval Efficiency

3 Integration Skills

4 Security Protocols

5 Analytical Prowess

6 System Optimization

7 Here’s what else to consider

Data Warehousing

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Here's how you can identify the key competencies assessed in data warehousing performance evaluations.

1

2

3

4

5

6

7

1 Data Mastery

2 Retrieval Efficiency

3 Integration Skills

4 Security Protocols

5 Analytical Prowess

6 System Optimization

7 Here’s what else to consider

Data Warehousing

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