Data Warehouse And Business Intelligence Tutorial – How do you start connecting data from cloud applications to your internal databases to gain insight into your business? Maybe your organization has already standardized on Microsoft Power BI as your analytics tool, but you’re still learning how to use it with multiple data sources.
To analyze data from multiple sources, you need a data warehouse that consolidates all your data in one place. Many companies take advantage of cloud-based data warehouses such as Amazon Redshift, Google BigQuery, or Snowflake.
Data Warehouse And Business Intelligence Tutorial
You can extract and load the data you have stored in SaaS applications and databases into the data warehouse using an Extract, Transform, Load (ETL) tool. Once the data is available, your analysts can use it to create reports. In this post, we’ll take a look at how to start from scratch and create a report using Power BI.
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We start with Stitch, a simple and powerful ETL service for businesses of all sizes, all the way up to the enterprise. It can move data from dozens of data sources. Sign up and you can move data to a data warehouse in under five minutes. Select at least one of the Stitch integrations as a data source. Many companies get more value from their data as they integrate more data sources; for our example we are looking at data from two separate sources.
If you don’t already have a data warehouse, one of the easiest ways to get one is Panoply, a platform that offers a managed version of Redshift. You can easily set up a Panoply destination from within Stitch. On the Select Destination screen, click Panoply, click Create New Account and follow the instructions. Stitch uses the email address you logged in with, generates a secure password, and sets up a database destination. Save your credentials so that you can access your new data store in the future.
To illustrate, I’m using Stitch’s own business data, which we store in an Amazon Redshift data warehouse. Redshift was one of the first entrants to the cloud data warehouse market and remains a popular choice.
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Microsoft Power BI serves as the third layer of our data analytics stack. Power BI differs from BI tools such as Looker, Chartio, Periscope Data, and Mode in that it does not rely on or release SQL code to run its queries. Perhaps a fully visual report builder like Power BI should be easier for business users to learn than a query language like SQL. However, in practice, Power BI’s UI is quite complex, so it takes time to learn, just like SQL. To use it properly, you need to understand many of the same concepts you need to know about writing SQL queries.
Microsoft only offers a free Power BI Desktop application for Windows (which is what I used), the cloud-based Power BI Pro tool, and an on-premise Power BI Report Server.
If you’re new to Power BI, don’t wait to download the software and get started right away. Watch some tutorials or buy a book that will introduce you to the user interface and the process of creating a visualization. I found “Supercharge Power BI: Power BI is better if you learn to write DAX” helpful.
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I got permission from our CEO to share some real Stitch data for this exercise. I use data from a table in one of our back-end product databases to select all of our active customers, and then join that list with another table that tracks the number of Google connections our customers have made. I’m creating a report that shows the number of connections to Google Analytics over time, filtered to include only active customers.
To get started, I clicked Get Data in the ribbon on the Home tab, found Amazon Redshift and entered the server names and our Redshift database. (If you’re using a Panoply data store, you should still choose Amazon Redshift, but use db.panoply.io as the server you connect to.) You can choose to import data or use a direct query. I chose the former because I’m good with a snapshot that I can update as needed. If you’re working with a large number of records or data that needs near-real-time accuracy, you should use DirectQuery.
Power BI includes a Navigator panel where you can select data sources. I started with the connections table in our Connections_Service schema, which tracks all the connections our customers make to data sources.
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I clicked the data view in the left column of the Power BI Desktop window to make data preparation easier. I only needed three columns from the table of connections, so I deleted the others by right-clicking their names and selecting Delete from the drop-down menu.
For each connection I needed correctly formatted data to use as a selection field and to display results by month. To create this field I added a new column to the data to hold a formatted version of what was created
, and specify the desired format for this field using Power BI’s Data Analytics Expression (DAX) language.
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Since I was only interested in links related to Google Analytics, I also set up a text filter on the type field to limit my search to only rows containing the string google.
Then I went back to the Navigator and imported data from another source: the rjm_clients table from our platform schema, which contains all the information about our customers. Again I removed the columns I didn’t need, keeping only the client ID and a boolean indicator to show if the client was active.
Now I had to tell Power BI how to link the two tables. I clicked the Relationships button on the left side of the window to open the Relationships view, and then clicked the Manage Relationship button on the ribbon in the Home tab. From this screen you can set up foreign key relationships by linking key fields in tables; I have linked the customer IDs.
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Now that I had all the data I wanted (and none I didn’t have) and filtered and aggregated it appropriately, I was ready to visualize it by switching to report view.
Here you have created a new measure – a calculation on the data, which you make using a DAX function. I defined a measure called connects with the function COUNT(connections[client_id]); in this syntax connections is the table and client_id is the field name.
Then click the line chart icon in the visualization panel. I dragged the YM column from the Fields pane to the Axis field. I dragged Type into the Legend field. And dragged the measurement from the connections to the Values ​​field.
Document A Data Warehouse Schema
Once I looked at the data for the first time, I realized I was looking further back in time than I cared to. You can create filters in the report panel, like we did in the data panel when we restricted the type field to only show values ​​that contain google. That’s one of the many places where Power BI offers more than one way to accomplish the same thing. I indicated that I only wanted to see records from October 2015.
At this point, I had accomplished what I set out to do: create a usable visualization of our data. In it we see a couple of different datasets: an older version of our GA connector that was based on an old Google API, which is in disrepair, and a newer version that we introduced more recently that uses Google Analytics’ Reporting API, whose use is growing. A report like this would be helpful to our product team as they see new and improved versions of our technology taking over from the old ones we are phasing out.
Power BI gives you a lot more options than I’ve covered here. In addition to the 29 visualizations collected in the Desktop interface, you can click an icon to visit the Power BI Visuals Marketplace, where you can download hundreds of additional visualizations for free. You can make many kinds of customizations to the appearance of a report. And when you’re happy with your report, you can publish it to the Power BI Report Server or to a website.
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So there you have it – a short walk through the process of using an ETL tool like Stitch to move data from multiple sources into a data warehouse and then report on it using Power BI. Sign up for a free trial of Stitch and start making your own. Business Intelligence (BI) is a set of methods and tools used by organizations to access and explore data from various source systems to better understand how the business operates and make more informed decisions that improve performance and create new strategic create growth opportunities.
Data warehouse (DW) is simply a consolidation of data from various sources that lays the foundation for Business Intelligence, helping in better strategic and tactical decision making. So I can say that data warehouses have a business
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