Connecting to different types of data sources in Power BI

Businesses in the 21st century are generating vast and diverse types of data due to technological advancements, the proliferation of digital platforms, and the integration of various business processes with data-driven tools. The need for automated business intelligence systems that can connect to multiple data sources arises from the growing complexity and scale of data needed to analyze.

Furthermore, automated business intelligence systems reduce data latency and provide real-time data analytics from the data warehouse, enabling businesses to make quicker and more informed decisions. The increasing automation of enterprises has created a need for quick decision-making capabilities that traditional business intelligence architectures struggle to provide, further driving the need for real-time business intelligence systems (Khan & Khan, 2016).

Importance of a Data Warehouse in modern business analytics

A data warehouse is a centralized repository that stores large volumes of structured and sometimes semi-structured data from various data sources within an organization. While traditional data storage focuses on the basic storage and retrieval of data, modern data warehousing is designed to support complex data analysis, reporting, business intelligence (BI) activities, and decision-making (Khan & Khan, 2016). Traditional data storage systems have high latency, referring to the delay between data availability for analysis and decision-making. Modern data warehousing aims to reduce decision latency, which is the time from the delivery of information to selecting a strategy to bring changes (Nguyen & Tjoa, 2006).

Data warehousing involves extracting data from many operational, legacy, and possibly heterogeneous data sources. These data sources can include sensor networks, networking flow logs, telecommunication fraud detection logs, e-commerce records, and stock market records. Modern data warehouse supports complex analytical queries that are beyond the capabilities of transactional databases. This enables advanced analytics, such as predictive modeling and data mining, which can provide deeper insights. A data warehouse is implemented separately from the operational data sources of an enterprise to speed up analytical queries and avoid negatively affecting the operational system’s (CRM, ERP, etc) performance (Nguyen & Tjoa, 2006).

SQL plays an essential role in Business Intelligence

SQL stands for Structured Query Language, and it plays a foundational role in data warehousing and business intelligence. The role of databases in modern business is significant. Databases, particularly Relational Database Management Systems (RDBMS), are the most widely used database platforms across the world and play an important role in implementing analytical processing (Ghosh, Haider, & Sen, 2015).

Furthermore, to deal with complex data sources and advanced data manipulation, SQL is essential. It can also be used to automate recurring data processing tasks using stored procedures, triggers, and other database programming constructs (Khan & Khan, 2016). Proficiency in SQL also helps to collaborate with IT and development teams, to understand the underlying database structures more effectively. Furthermore, by using SQL to pre-process data, more efficient and responsive Power BI reports can be created.

Role of different Data Connectors in Power BI

Data connectors in Power BI are interfaces that allow users to connect Power BI to various data sources, enabling data extraction, transformation, and loading for analysis and visualization. Many connectors also support real-time or near-real-time data updates, which is crucial for monitoring different business metrics and making timely decisions.

Below is an overview of the main types of data connectors available in Power BI:

• Database Connectors: SQL, MYSQL, PostgreSQL, Oracle Database, Teradata, SAP, Legacy OLE DB, ODBC and Azure SQL.
• File-Based: Excel, CSV, JSON, Text Files, XML and PDF.
• Cloud Services: Azure Blob, Azure Data Lake, Google BigQuery, Amazon Redshift, Salesforce, Google Analytics and Microsoft Dynamics.
• File Sharing Services: OneDrive, SharePoint, Microsoft Exchange, Azure DevOps and LinkedIn Sales Navigator.
• Web API: Websites, RESTful API, Facebook and OData Feed.
• Power Platform: Power BI Datasets, Power Apps and Power Automate.
• Big Data: Apache Spark, Azure HDInsight, R Scripts and Python Scripts.

Significance of historical and continuous data sources in Business Intelligence

Historical data is the data that was generated in the past or the event that triggered the generation of the data has passed. It can be voluminous, encompassing years or decades of information. Historical data is used to identify trends over time, such as analyzing sales data to determine seasonal patterns or long-term growth.

Consequently, continuous data is real-time data and has very low latency in generation as compared to historical data. It represents ongoing processes or events, providing a live view of what’s happening at any given moment. Continuous data is always changing and often comes at high speed. This data can be in the form of stock market prices, sensor logs and website traffic behaviour. Modern businesses use continuous data to make immediate decisions, such as adjusting pricing strategies, responding to real-time customer behaviour trends and detecting sensor faults or errors.

Furthermore, along with historical data sources stored in data warehouses, Power BI can also connect to continuous streaming data sources, allowing users to create real-time dashboards that update as new data comes in. Both types of data are valuable and leveraging them appropriately leads to better insights and reporting.

Common problems faced while connecting to various data sources

Connecting to data sources in Power BI is generally straightforward. Here are some common data connection problems in Power BI:

• Incorrect Data Load: Data is loaded incorrectly, resulting in missing or corrupted data in Power BI reports. When selecting folders as data sources incorrect data types or formats in the folder can throw an error.
• Authentication Issues: Difficulty authenticating to the data source, especially with cloud services or databases requiring specific credentials such as login credentials or lack of database permissions.
• Data Source Compatibility: Inability to connect to certain data sources because they are not supported by Power BI or are not native connectors.

References

• Ghosh, R., Haider, S., & Sen, S. (2015). An integrated approach to deploy data warehouse in business intelligence environment. Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT) (pp. 1-4). Hoogly: IEEE. doi: https://doi.org/10.1109/C3IT.2015.7060115
• Khan, Z. A., & Khan, M. (2016). Investigating BI in Real Time Systems: Concepts, Components, Techniques and Benefits. International Journal of u- and e- Service, Science and Technology, 367-376. doi: https://doi.org/10.14257/ijunesst.2016.9.12.32
• Nguyen, T. M., & Tjoa, A. M. (2006). Zero-latency data warehousing (ZLDWH): the state-of-the-art and experimental implementation approaches. 2006 International Conference onResearch, Innovation and Vision for the Future (pp. 167-176). Ho Chi Minh City: IEEE. doi: https://doi.org/10.1109/RIVF.2006.1696434