Master your data storage strategy! Discover best practices for hiring elite Data Warehouse developers, ensuring scalable and insightful solutions.
Your business depends on data. With each passing year this becomes more and more obvious. No matter the size of your company, you need the information to move forward. This information may consist of customer details, sales data, employee records, supply chain records, products, customers, location data or trends. The list of data your business needs can be endless.
And the longer you're in business, these stores can grow exponentially. Where do you store this data? When your business grows beyond a simple server or set of servers to store your data, you'll realize you need something serious like a data warehouse.
The term itself probably conjures up huge buildings that house rows and rows of servers that are all grouped together to keep all that precious data safe. Although it is an intriguing image, such a concept is only valid 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 1980s. At that point, it became obvious that data was more than just a means of storing information, but a way to help make important decisions that can reveal business intelligence.
More about Business Intelligence
Business Intelligence (BI) comprises both the strategies and technologies used by enterprise-class companies to analyze collections of data. Analyzing this data has become essential for companies to outline strategies for the future. And given how competitive the business world has become, every advantage (regardless of how small) can mean the difference between success and failure.
While you may think you can get by with the traditional database and web-based GUI, there are two very important benefits to migrating to a data warehouse:
- Better data – you will be able to collect more consistent and relevant information from a specific source.
- Faster decisions – Because the data stored in a warehouse is in a more consistent format, the systems you use for analysis can reach decisions much faster.
It is important to understand that a data warehouse is not just a single collection of data. Instead, a data warehouse is a collection of stored databases. This means you can have different databases from different sources, or each one housing specific region, customer, or product data.
Data lake or not?
You may have heard the term “data lake”. While this is another important concept, you need to understand that a data warehouse and a data lake are two very different things. A data lake is a collection of various types of data, including raw, unstructured, and structured. These different data sets are stored in raw format until needed. A data warehouse, on the other hand, stores data in organized files and folders that are ready to be used by analytical tools.
What You Need to Build a Data Warehouse
First of all, a data warehouse is not something your IT administrator can download and point and click to deploy. This is a very complicated, complicated and time-consuming procedure. This means that you will need to have the necessary team to do in-depth research and who fully understands how the data works.
So the first thing you'll need to do is collect your data, which can come from virtually any source. This could be ad performance, website or app tracking, e-commerce, marketing, customer relations, customer support, or financial data. You can collect this data with tools like Google Analytics, Snowplow, Heap, your company's HR tool, or Zendesk. This means you will need personnel trained in extracting data from these platforms.
Once you have collected your data, you will need to turn to a company that offers data warehouse solutions. Yes, you can always build your own in-house data warehouse, but why reinvent the wheel? Some of the easiest data warehouse services to start include:
Of the services above, only Panopy offers built-in, easy-to-use connectors for virtually any type of data you've collected. This makes Panoply the easiest to use, 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 is only necessary if you opt for a data warehouse solution that does not include a connector for your data. If that's the case, you'll need to turn to people like Cantor , Ponto , Blendo , or Fivetran . Naturally, you'll need team members capable of using these tools.
Finally, you will need to employ the right analytical tools such as Google Data Studio , Observer , Metabase or Modo .
Once you have all these pieces together, your data warehouse is ready to use.
Interview Questions
What is a data warehouse?
A data warehouse is a collection of data that is used as a management decision support system and/or business intelligence.
What is a fact table?
A fact table contains the measurement of business processes as well as foreign keys used for dimension tables.
What are the 4 stages of data storage?
- Offline operational database
- Offline Data Warehouse
- Real-time data warehouse
- Integrated data warehouse
What does OLTP mean?
Online transaction processing
What does OLAP mean?
Online analytical processing
What is the difference between View and Materialized View?
A view is a virtual table that uses the output of a query to be used in place of tables, while a materialized view is indirect access to table data by storing the results of a query in a separate schema.
What are nonadditive facts?
Non-addictive facts cannot be summarized in any of the dimensions present in the fact table.
What are the 3 types of slowly changing dimensions?
- SCD 1 – a new record replaces the original record
- SCD 2 – a new record is added to the existing customer dimension table
- SCD 3 – original data is modified to include new data
Job description
You will be responsible for planning, connecting, designing, scheduling and deploying our data warehouse systems. Other duties will include developing, monitoring and maintaining ETL processes, reporting applications and data warehouse design.
Responsibilities
- Plan, create, coordinate and deploy company data warehouses.
- Design any required end-user interfaces or train users with third-party tools.
- Develop best practices for data loading and extraction.
- Develop and manage all aspects of data architecture, data modeling, and ETFL mapping solutions in a structured data warehouse environment.
- Develop and/or deploy required reporting applications.
- Develop and implement ETL routines.
- Support the development and validation required throughout the lifecycle of data warehouse and business intelligence systems.
- Maintain user connectivity and provide security for the data warehouse.
- Monitor the performance of data warehouse and business intelligence systems.
- Manage multiple projects at the same time.
Skills and qualifications
- Knowledge and understanding of the software development life cycle.
- Advanced knowledge and experience (minimum 5 years) in relational databases and SQL query language.
- Experience in database design and modeling for data warehouses.
- Experience with business intelligence applications (including relational database structures and normal forms).
- Analytical and problem-solving skills with complex technical issues and tasks.
- Minimum 5 years experience with SQL Server, TSQL, SSAS, SSIS, SSRS, SharePoint Development Studio and Oracle DBMS.
- Superior analytical skills with a good problem-solving attitude.
- Fundamental understanding of version control systems (such as Git).
- Solid problem-solving skills.
- Excellent written and verbal communication.
- Good organizational skills.
- Ability to work as part of a team.
- Attention to the details.
- Understand the nature of asynchronous programming and its peculiarities and workarounds
- A positive attitude.
Conclusion
As you can probably tell, creating a data warehouse is no easy task. That's why you'll need to hire data warehouse developers who are capable of bringing these technologies together, so your company can make the most of your data and take your business intelligence game to the next level.
