What is a Data Warehouse?
Data Warehouse is the Central Data Store within your company. It is the “Single Source of Truth” for all the data in your company.
During the early days, you may be using your regular database to run SQL queries for analytics. But, with the increase in the size of the data and individuals using the data to perform various analysis, your regular database becomes extremely slow in query processing.
Data Warehouse (software) on the other hand, is designed to handle huge volumes of data. It allows you to swiftly Filter, Sort, Aggregate, and Analyze the data.
This allows Business/Data Analyst teams to make use of all the data available within the company. Generally, there are two primary use cases for data analysis.
- Measure Performance – To evaluate how various activities are performing within the company. E.g. Measuring the Sales Performance across different geographies.
- Validate Certain Hypothesis – This is to discover certain insights which were not previously known or to validate certain possibilities. E.g. Do the users who were acquired through Facebook tend to stay/buy more than the users who were acquired through Google.
A warehouse comprises of data from various sources such as Internal Databases (multiple Databases from different systems and Microservices), Behavioral Data (data on how users are interacting with your offering across various digital mediums), Various Third party SaaS Applications – Google Analytics, SalesForce, ZenDesk etc.
Here are the core advantages of setting-up a Data Warehouse for a company-wide analytics:
Serves as a Single Source of Truth for all the data within the company.
Gives the ability to quickly run analysis on huge volumes of the data.
Any change in the structure of the data in the operational or transactional database doesn’t break the business reports running on top of it.
Cloud Data Warehouse (such as Amazon Redshift and Google BigQuery) offer an added advantage, you need not invest in them upfront. Instead, you pay as you go as the size of your data increases. You can refer to this article onAmazon Redshift vs Google BigQuery for comparison of the two.
Let’s you make the data available for all. You can expose the data within the company for the analysis. While you do so you can hide certain sensitive information (such as PII – Personally Identifiable Information about your customers, or Partners).
The most challenging part of setting up a Data Warehouse
Bringing data from multiple sources in real-time is quite challenging, as the data sources keep changing from time-to-time. Even the structure of the data that comes from these sources keeps changing. It becomes essential to have a tighter control over what data is streaming and monitoring the data for quality.
Also, the data that comes from various sources is often not very structured and clean to be directly used for analysis. There is an intermediate stage of cleaning and transforming the data before it goes into the Data Warehouse.
The structure of the data available in transactional systems is highly normalized as it is optimized for the faster writes. But since a large amount of data in the warehouse is to used for analysis, it has to be optimized for the faster query response times. Hence this data needs to be denormalized.
You may also need to create and store certain aggregate views (called Materialized views) with precomputed metrics – such as Life Time Value (LTV) of each customer.
You need the most recent and accurate data in your Data Warehouse.
This step introduces a delay to when the data is available in the Data Warehouse for analysis. As the data volume grows the latency increases. Hence, it is essential to have a system that can auto-scale to huge volumes of data to ensure business teams always have the most recent and accurate data – which is ready for analysis.
Having a reliable and robust system to bring data from multiple sources is the most critical step in making the users in your company trust the data to take the decision.
What is the easiest and reliable way to bring data from multiple sources?
We built Hevo Data Integration platform to simplify the complex task of bringing data from multiple sources into your warehouses such as Amazon Redshift and Google BigQuery.
With Hevo you can start bringing your data from any source in minutes, without having to write any code. This saves you from writing difficult to manage custom ETL scripts.
Hevo comes with a 14-Day Free Trial. Get started here.