Data warehouse implementation is one of the trickier jobs in analytics. But with the right tools and good planning, you can easily wrangle all of your data into one place. Here's how.
A data warehouse is a centralized repository that stores integrated data from multiple systems.
A typical business has several mission-critical systems. There might be a CRM , ERP , e-commerce system, or marketing automation platform. All of these systems run on a relational database that holds crucial data.
You can consolidate all of this information by setting up a data pipeline , powered by ETL. This pipeline extracts data from your essential systems, integrates and cleanses it, and then stores it all in one big relational database: a data warehouse.
There's a misconception that data warehouse implementation is something that you only need to think about when your data reaches a specific volume. Small and mid-size companies delay this step until later in their development because they don't see the value of a centralized repository.
Most businesses need a data repository right from the get-go, and for one specific reason: analytics. A data warehouse is one of the fastest and most reliable ways to consolidate data from multiple systems, giving your analytics team a 360° view of your customers and your operations.
There are other reasons to consider a centralized repository, like system integration or having secure backups for disaster recovery. And getting started is easy if you have the right data warehouse implementation plan.
Once you've identified the need for a data warehouse, it's time to start planning. Follow these steps for implementing a data warehouse:
A company-wide data project like this will involve multiple stakeholders. You'll need to talk to:
Once everyone is on board, you're ready to start your data warehouse implementation.
At this stage, you have several options for your warehouse environment, such as:
A public cloud is often the cheapest and easiest option, as your host does most of the hard work for you. However, there are latency and security issues that might make you consider an on-premise or hybrid option.
Whichever you choose, you'll need to create three separate environments:
You can create more than three environments if required. For example, you might need separate warehouses for testing and QA. But you will need at least three, as your development team can't try out new features on the production data.
Data modeling is perhaps the most difficult part of data warehouse implementation. Every source database has its own schema . Your warehouse will have a single schema, and all incoming data must fit this schema. So you need a model that suits all existing data and can scale up for the future.
Some of the main types of schemas are:
Designing a schema from scratch is generally the work of a data scientist. Many cloud and commercial on-premise systems will help you to adopt a schema model to your needs.
Connecting is a two-step process. First, you extract data from the target source, and then you upload it to the data warehouse.
Extraction can happen in several ways, such as:
Once you've obtained the data, you need to load it into the data warehouse.
Because of this task's complexity, most people rely on an automated ETL ( Extract, Transform, Load ) to handle the entire process. Integrate.io comes with a library of integrations to automatically extract from the target and load to the destination.
When you have an automated process moving data in this way, it's known as a data pipeline.
ETL has a vital step between extract and load. Transformation is an intermediate stage, where the ETL process converts data from its original schema to the destination schema. Without transformation, your data can't slot into the destination tables.
Transformation can also include other steps, such as:
If you use a tool like Integrate.io, you can create schema mappings without coding. You can also set rules for validation, cleansing, and other data hygiene actions.
Data warehouses store everything, but most people don't need access to everything. Sales teams want sales figures; operations teams need ops data, and so on.
The solution? A data mart. Marts are a logical division within the warehouse – a limited view that only shows relevant results.
You can often manage this with the right metadata. For example, you may tag some records with "Sales" and others with "Finance". Marts can show records with each matching tag. A record that's relevant to both teams, such as a sales invoice, can have both tags. It will then appear in both data marts.
Marts are a great way of delivering targeted results. They're also an excellent way of improving data security, as they restrict people from viewing relevant data.
Most commercial Business Intelligence (BI) and analytics tools offer simple integration with a data warehouse. You can also connect these tools directly to your ETL platform, offering even faster insights and visualizations.
BI and analytics tools rely on :
If you've followed your data warehouse implementation plan, you should be able to deliver the data your analytics team needs.
Once your data warehouse is operational and your analytics team has what they need, you can start putting measures in place to ensure data quality.
This may involve using automated data quality testing tools to measure the quality of your warehouse contents. You can also perform sense checks to see that there are no obvious discrepancies between raw data and stored data.
A data warehouse implementation is a big project, and it can go awry. If you've followed the plan, you shouldn't have any issues. But here are a few things to watch out for:
Solution: Security is an ongoing process. Transforming fields to remove, encrypt, or mask sensitive data is the first step to preventing data loss. You have to keep a close on your compliance and security habits as well as in your choice of cloud partner who will deal with emerging threats on their end.
Solution: Your ETL can help to obfuscate sensitive data before it goes to the warehouse, which should resolve compliance issues.
Solution: First, check the source and make sure that the data is clean at the point of origin. If so, then it's likely to be one of the stages in your ETL process:
The latter problem might require you to rethink the structure of your warehouse.
Solution: This usually means that you've selected the wrong data sources. Sit with your analytics team and review the data that's going in. Sometimes, you may need to go further downstream and look at ways of capturing new data.
In many ways, a data warehouse is similar to a regular warehouse in that they are both all about processes – how things arrive in, how the items are stored and ordered, and how to process fulfillment requests as quickly as possible.
Your data warehouse is ultimately just a big relational database. What makes it exciting are the processes that keep it going. How you ingest data, how you integrate data, and how you feed that data out to your BI and analytics tools.
Integrate.io is the perfect platform for an orderly warehouse. Book a demo today and discover what a no-code ETL can do for you.