Your business depends on data. With each passing year, that becomes more and more obvious. No matter the size of your company, you need the information to keep you moving forward. That information might consist of customer details, sales data, employee records, supply chain records, products, clients, location data, or trends. The list of the data your company needs can get quite endless.
And the longer you’re in business, those stores can grow exponentially. Where do you keep that data? Once your business grows beyond a simple server or collection of servers to house your data, you’ll realize you need something serious, such as a data warehouse.
The very term probably conjures massive buildings housing rows and rows of servers that are all clustered together to keep all of that precious data safe. Although that’s an intriguing bit of imagery, such a concept only holds true for the largest companies.
For your business, a data warehouse is actually a large collection of business data that can be used to help your company make informed decisions. This concept has been around since the ’80s. At that point it had become obvious data was more than just a means to house information, but a way to help make important decisions that can reveal business intelligence.
Business Intelligence (BI) comprises both the strategies and the technologies that are used by enterprise-class companies to analyze collections of data. The analysis of such data has become key for corporations to strategize for the future. And given how competitive the world of business has become, every advantage (regardless of how small) can mean the difference between success and failure.
Although you might think you can get by with the traditional database and web-based GUI, there are 2 very important benefits to migrating to a data warehouse:
It's important to understand that a data warehouse isn't just a single collection of data. Rather, a data warehouse is a collection of stored databases. That means you could have different databases from a variety of sources or each of which houses data specific to such things as regions, clients, or products.
You might have heard the term "data lake." Although this is another important concept, you need to understand that a data warehouse and a data lake are 2 very different things. A data lake is a collection of multiple types of data, including raw, unstructured, and structured. These different sets of data are stored in their raw format until they are needed. A data warehouse, on the other hand, stores data in organized files and folders that is ready to be used by analytics tools.
First of all, a data warehouse isn't something your IT admin can download and point-and-click their way to deploying. This is a very complicated, involved, and lengthy procedure. That means you'll need to have the required staff to do in-depth research and who fully understand how data works.
So the first thing you're going to need to do is to collect your data, which can come from nearly any source. This can be ad performance, website or app tracking, e-commerce, marketing, customer relations, customer support, or financial data. You can collect that data with tools like Google Analytics, Snowplow, Heap, your company HRM tool, or Zendesk. That means you're going to need staff trained in the extraction of data from those platforms.
Once you have your data collected, you'll then need to turn to a company that offers data warehouse solutions. Yes, you could always build your own in-house data warehouse, but why reinvent the wheel? Some of the more startup-friendly data warehouse services include:
Of the above services only Panopy offers built-in, easy-to-use connectors for nearly any type of data you've collected. This makes Panoply the most user-friendly, Snowflake can get expensive, and Amazon Redshift can be the most complicated. However, if you expect your data warehouse to grow fast and large, Amazon certainly has the infrastructure to house any size data warehouse you need.
Next, you'll need the right ETL tool. ETL stands for Extract, Transform, Load. This will only be necessary if you opt to go with a data warehouse solution that doesn't include a connector for your data. If that's the case, you'll need to turn to the likes of Singer, Stitch, Blendo, or Fivetran. Naturally, you'll need staff members capable of using these tools.
Once you have all of those pieces together, your data warehouse is ready to be used.
As you can probably tell, creating a data warehouse isn't an easy task. That's why you'll need to make sure to hire data warehouse developers who are capable of putting these technologies together, so your business can make the most out of your data and raise your business intelligence game to the next level.
A data warehouse is a collection of data that is used as a management decision and/or business intelligence support system.
A fact table contains the measurement of business processes, as well as foreign keys used for dimension tables.
On-Line Transaction Processing
Online Analytical Processing
A view is a virtual table that takes the output of a query to be used in place of tables, while a materialized view is indirect access to the table data by storing the results of a query in a separate schema.
Non-addictive facts can’t be summed up for any of the dimensions present in the fact table.
You will be responsible for planning, connecting, designing, scheduling, and deploying our data warehouse systems. Other duties will include the development, monitoring, and maintaining of ETL processes, reporting applications, and data warehouse design.
First there was R, then there was Python, and now Julia is growing in popularity. Will it triumph over the others as the data scientists’ darling?
We should aim to make a more universal approach to data management that guarantees privacy, security, and ownership to users of any kind of service.
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