Commonly organisations have a number of disparate data sources, whether from operational systems, like a ERP or CRM, APIs from online services, or existing data marts or silos. When the organisation would like to have a business intelligence solution that provides a single view of from all these different types of data sources, they realise that, over time, it would not be feasible to connect Power BI directly to all the sources and expect Power BI to be the central repository or database data warehouse for historical data. For small data amounts this may work, but overtime as data amounts grow, soon the limitation of this type of architecture become apparent.

A Data warehouse (DW) is a centralized data repository that integrates data from various legacy, transactional, or external systems, applications, and sources. A data warehouse provides a dedicated environment separate from operational or transactional systems and is specifically designed for analytical-reporting, ad-hoc queries, data mining and decision-support. Some organisations already have data warehouses, however, their data warehouses are transactional databases which are not specifically designed for reporting purposes. The use of data determines the type of data warehouse structure developed.

A data warehouse helps solve the on-going problem of pulling large amounts of data from disparate sources like transactional or operational systems quickly and efficiently and transforming that data for optimised reporting. A Data warehouse also allows for the processing of large and complex queries in a highly-efficient manner.

To move data into a data warehouse, data is periodically extracted from multiple disparate sources that contain essentially information. As the data is moved, it can be cleaned, transformed, summarised, and reorganised. The data can be stored in the lowest level of detail, and have aggregated views available in the warehouse for easy extraction and analysis. A data warehouse becomes a permanent data store used to meet reporting and data analysis needs.

There are limits to how much data can be imported into a Power BI Desktop file, likewise there are also limits to the size of a Power BI Workspace, where the Power BI Desktop files are published for consumption in the Power BI service.

As a data warehouse is not reporting tool agnostic, nor does it confine to the data storage limitations of many reporting tools. When investing in a DW, you are not investing in a specific reporting tool, rather, you are future proofing your IT investment through an investment into data efficiency which empowers a historical and coherent picture of your business at any point in time.

Whether you’re using Microsoft Power BI, or any other BI tool, for your data analytics needs, it is best practice to not expect the BI tool to handle the data model processes and for it to become a historical data base. BI tools like Power BI are built to provide business users an interactive platform for data analysis, they not to be viewed as data warehouse solution. Data mining, Data integration, complex ETL process and data cleaning, is the not the role of a BI tool, especially when it comes to the demands of enterprise operational data and reporting needs. Investing in data warehouse architecture that integrates data from multiple sources, even unstructured data, and models this in a structure optimized for reporting needs, frees the BI tools used to focus on analysing data instead. Having all your data consolidated into a well designed relational database, STAR scheme, places your organisation at a massive reporting advantage.

Given that data warehouses are optimised for read access, generating reports is faster than using the source transaction system for reporting.

Other data warehouse benefits:

• The data warehouse can representing a single source of truth from multiple disparate data sources
• As data imported into the data warehouse the quality can be improved through data cleaning
• A data warehouse can be used for historical storing of data pulled from APIs that only provide snap shots (e.g. last 90 days)
• Data warehouses make it easier to provide secure access to only authorised users
• Instead of requiring a reporting tool to do the heavy lifting, complex data transformations or calculations of metrics and KPIs can be handle on the server side

Choose a reporting data warehouse when you need to turn massive amounts of data from operational systems into a structure that is easy to query and understand. As Data warehouses don’t need to follow the same data structure you may be using operational databases, you can restructure the schema to simplify relationships, and consolidate several tables into one. You can also use column names that make sense to business users and analysts.

You may not want to give business analysts or report builders access to your operational systems. You may also need to keep historical data separate from the source transaction systems for performance reasons. By providing a centralised cloud based location using with a common data model, data warehouses make it easy to access historical data from multiple locations.

Data Bear has an experience team of database or data warehousing developers. Regardless of the amount of data, type or where the data is stored, our team will help you with a best practice warehousing solution tailored to your specific needs. There are many different types of database structures, we focus on developing a data warehousing solution which is optimised for organisation reporting needs, be it ad hoc analysis of raw data driven by end user needs or formal reports designed for decision makers.

If a organisation does not already have their own data warehousing environment, we recommend a cloud base data warehousing solution. Platforms like Microsoft Azure offer a wide range of cutting edge tools and integrates seamlessly with Power BI. Built for the cloud, Azure SQL Database is an scalable, intelligent, relational database service. Optimise durability and performance with out of the box automated AI-powered features that always stay up to date. By automatically scale resources on demand, you’re free to focus on building new applications or reporting solutions without worrying about storage size or resource management.

The Data Bear team helps provide a unified experience and a full range of deployment options from edge to cloud. Our Azure SQL database services provide an intelligent and secure ways to:

• Migrate your SQL workloads with SQL Server on Azure Virtual Machines
• Modernise your existing applications with Azure SQL Managed Instance
• Support modern cloud applications with Azure SQL Database
• Use your existing SQL skills in the cloud on the same SQL Server engine
• Extend your applications to IoT edge gateways and devices with Azure SQL Edge
• Keep making the most of your current licence investments

Within this context, our team provides the following services:

• Plan and implement Analytical data warehouse using Azure SQL Database.
• Plan and implement Analytical data warehouse using Azure Synapse.
• Plan and implement complex analytical solutions using Azure platform and Power BI:

1. Fetching data from external systems using Azure Data Factory or custom solutions
2. Data cleaning and enriching using Data Factory
3. Creating analytical data warehouse using Azure SQL or Azure Synapse
4. Building bespoke interactive reports using Microsoft Power BI 

We automate the process of extracting, cleaning, consolidating and restructuring your data from heterogeneous sources so that it delivers excellent query performance, even for complex analytic queries, without impacting transactional or operational systems.

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