What are the best ways to integrate data from multiple sources into your programming language?

In order to achieve effective data visualization, it's crucial to start with a compatible programming language. Choosing the right language lays the foundation for a rigorous yet simplistic ETL (Extract, Transform, Load) process. Among the array of options, several standout languages offer the versatility and tools necessary for seamless data visualization. Some of the most popular languages that can empower your data endeavors include Python, R, JavaScript, and Julia.

I started my career with an ETL/Data Visualization tool, QlikView, which let me extract, transform, and load data from multiple files such as Excel, Text, CSV, and databases. You can create outstanding visualizations in minutes once the data is loaded.

Harness the power of data formats and standards, ensuring a seamless integration of data from various sources. This step brings coherence and efficiency to the process, paving the way for rigorous yet simplistic ETL (Extract, Transform, Load). Among the wealth of options, certain data formats and standards have emerged as front-runners, offering a universal language for data integration. Tools like MongoDB can expedite a project to the MVP stage.

Use consistent data types, such as dates, when loading data in multiple formats. Understanding the various data types is the first step to integrating and consistently presenting your data. For instance, a CSV file might have a date field in the YYYY-MM-DD date format, while a different date field from your Database might have the MM/DD/YYYY format. First, check the format and data type for an efficient data integration strategy.

Now we delve into the realm of data connectors and drivers, essential components that facilitate the seamless communication between your chosen programming language and diverse data sources and platforms. These connectors and drivers are the linchpin that enables your programming language to engage with databases, web services, file systems, and cloud storage, allowing for a rigorous yet simplistic ETL (Extract, Transform, Load) process. It is important to decide these processes early and upstream, so that your decisions can be reflected system-wide.

Choosing the Right Data: Selecting the most relevant and accurate data sets is the cornerstone of any successful data project. It ensures that your insights are based on sound foundations. Selecting the Right Visualization: The way you present your data is crucial. Choosing the right visualization method can make complex data more understandable and engaging. Identifying the Right Audience: Different stakeholders have varying needs and levels of expertise. Tailoring your message to your audience ensures that your insights resonate with them. Crafting the Right Message: Your narrative should be concise, compelling, and aligned with your data. Crafting the right message is key to conveying the intended meaning.

I suggest avoiding overwhelming your audience with many charts and KPIs in one sheet. Try to break down the information with all the metrics you want to show your audience. My to-go strategy is to include KPIs with the main Metrics and have a Trend chart - either a bar/combo/line chart to show trends overtime.

In my experience, the majority of corporate data is still stored in SQL databases. Therefore, integrating data at the application level often comes down to querying different SQL tables and transforming that data into the most convenient format. E.g. Ruby on Rails is built on an object relational model that allows you to interact with a database through Ruby objects, and when you pull in data from external sources, you can update your objects based on the data you pull in. But a basic understanding of SQL tables and queries is still the most valuable first step in doing that.