How can you analyze and visualize data using warehouse tools and techniques?

1 Data warehouse architecture

The first step to analyze and visualize data using warehouse tools and techniques is to understand the data warehouse architecture. A data warehouse architecture consists of three layers: the data source layer, the data integration layer, and the data presentation layer. The data source layer contains the original data from various systems, such as databases, files, or web services. The data integration layer performs the ETL process, which extracts data from the source layer, transforms it into a consistent format, and loads it into the data warehouse. The data presentation layer provides access to the data warehouse through various tools, such as query languages, reporting tools, or visualization tools.

2 ETL tools

ETL tools are software applications that automate the ETL process. They help you to connect to different data sources, define the data extraction rules, apply the data transformation logic, and load the data into the data warehouse. Some of the popular ETL tools are Informatica, Talend, SSIS, and Pentaho. ETL tools enable you to perform tasks such as data cleansing, data validation, data mapping, data aggregation, and data partitioning. ETL tools also provide features such as scheduling, monitoring, logging, and error handling.

3 Query languages

Query languages are languages that allow you to retrieve and manipulate data from the data warehouse. They help you to filter, sort, group, join, and aggregate data, as well as perform calculations and functions on data. Some of the common query languages are SQL, MDX, and DAX. SQL is a standard language for relational databases, which store data in tables. MDX is a language for multidimensional databases, which store data in cubes. DAX is a language for tabular databases, which store data in columns. Query languages enable you to write queries that can be executed by the data warehouse or by other tools, such as reporting tools or visualization tools.

4 Reporting tools

Reporting tools are software applications that allow you to create and distribute reports based on the data warehouse. They help you to design the layout, format, and style of the reports, as well as add charts, graphs, tables, and other elements. Some of the popular reporting tools are Crystal Reports, SSRS, and Cognos. Reporting tools enable you to generate reports that can be viewed online, printed, or exported to different formats, such as PDF, Excel, or Word. Reporting tools also provide features such as filtering, sorting, grouping, drilling, and slicing.

5 Visualization tools

Visualization tools are software applications that allow you to create and display visual representations of the data warehouse. They help you to explore, analyze, and communicate data using various types of charts, graphs, maps, and other visuals. Some of the popular visualization tools are Tableau, Power BI, and QlikView. Visualization tools enable you to create dashboards that can be interactive, dynamic, and customizable. Visualization tools also provide features such as data blending, data discovery, data storytelling, and data sharing.

6 Data warehouse best practices

The last step to analyze and visualize data using warehouse tools and techniques is to follow some of the data warehouse best practices. These guidelines are designed to improve the performance, quality, and usability of the data warehouse. First, design the data warehouse schema according to the business requirements and the analysis goals; you can use star, snowflake, or galaxy schemas depending on the complexity and granularity of the data. Additionally, optimize queries by using indexes, partitions, views, and stored procedures; you can also employ query optimization techniques such as query rewriting, query caching, and query parallelization. Furthermore, monitor and maintain the data warehouse by using tools that track activity, performance, and health; you can also use tools that backup, restore, and recover in case of failures or disasters. Finally, secure the data warehouse by using tools that encrypt, authenticate, authorize access; you can also use tools that audit, log, and alert activity and events.

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?