The Need for Data Warehouse in 2021

on Engineering • October 6th, 2021 • Write for Hevo

Table of Contents

What is a Data Warehouse?

Data Warehouse is the Central Data Store within your company. There is a need for Data Warehouse for all the enterprises that want to make data-driven decisions because a Data Warehouse is the “Single Source of Truth” for all the data in the 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.

This is where companies understood the need for Data Warehouse that 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.

  1. Measure Performance – To evaluate how various activities are performing within the company. E.g. Measuring the Sales Performance across different geographies.
  2. 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. As the need for Data Warehouse increases, various 3rd party Data Warehouse service providers started emerging that offer Cloud Data Warehouse and On-premise Data Warehouse solutions to enterprises.

Data Warehouse Basics

Data Warehouse is a similar or better alternative for Databases that is a permanent storage space with higher computational power to process and run analysis on data stored. The need for Data Warehouse is to generate reports, feed data to Business Intelligence (BI) tools, forecast trends, and train Machine Learning models.

Data Warehouse stores data from multiple sources such as APIs, Databases, Cloud Storage, etc., using the ETL (Extract Load Transform) process. There are many tools like Hevo available that load data to Data Warehouse and automatically transform it.

The data needed to provide reports, dashboards, analytic applications, and ad-hoc queries all exist within the production applications inside your company, so why not use the BI tools directly against this data? Well, there are many reasons why you would want to use a data warehouse instead of the “direct access” approach:

The need for Data Warehouse to deliver business reports, analysis, data to BI tools, and perform ad-hoc queries becomes an essential part of efficient processing. BI tools can not be used directly against data because the data is not available in the analysis-ready form in the data sources. There are still many reasons that state the need for Database Warehouse. A few of the reasons are listed below:

  • Multiple data sources store data in different formats, and a company needs to club data from many sources to generate valuable insights from data. Data Warehouse stores data in a common schema that allows companies, Data Scientists, Data Analysts, and BI tools to get all the data in one place.
  • It saves lots of time because it eliminates the need for retrieving data from multiple data sources and transform it according to the requirement.
  • There is also a need for Data Warehouse to store historical data even though one doesn’t use source transactional data.
  • If data is direct access by BI tools then there is a risk that BI tools can misuse data. But Data Warehouse hides personal information related to user’s info, payments, etc., for maintaining privacy.
  • Data Warehouse is also used as a common place to create metadata that helps Data Warehouse users understand data.
  • The need for Data Warehouse is to store clean data that can be directly used by Data Analysts, companies, Data Scientists, and other team members.
  • One can restructure and rename table names in the Data Warehouse that makes more sense to the users.
  • The need for Data Warehouse is to deliver faster query processing, and an architecture can be created for Data Warehouse for best performance instead of using structure used for transactional Databases.

Why You Need a Data Warehouse?

The first question that arises is, what is the need for Data Warehouse and spending lots of money and time on it when you can feed the transaction system direct to it, and we have BI tools. But there are many limitations to this approach, and gradually enterprises came to understand the need for Data Warehouse. Let’s see some of the points that make using a Data Warehouse so important for Business Analytics.

  • It serves as a Single Source of Truth for all the data within the company. Using a Data Warehouse eliminates the following issues:
    • Data quality issues
    • Unstable data in reports
    • Data Inconsistency
    • Low query performance
  • Data Warehouse gives the ability to quickly run analysis on huge volumes of datasets.
  • If there is any change in the structure of the data available in the operational or transactional Databases. It will not break the business reports running on top of it because they are not directly connected to BI tools or Reporting tools.
  • Cloud Data Warehouse (such as Amazon Redshift and Google BigQuery) offer an added advantage that 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 on Amazon Redshift vs Google BigQuery for a comparison of the two.
  • When companies want to make the data available for all, they will understand the need for Data Warehouse. You can expose the data within the company for analysis. While you do so you can hide certain sensitive information (such as PII – Personally Identifiable Information about your customers, or Partners).
  • There is always the need for Data Warehouse as the complexity of queries increases and users need faster query processing. Because the transactional Databases are built to store a store in a normalized form whereas fast query processing can be achieved by denormalized data that is available in Data Warehouse.

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 tighter control over what data is streaming and monitoring the data quality.

Also, the data that comes from various sources is often not very structured and clean that can be directly used for analysis. There is an intermediate stage of cleaning and transforming the data before it goes into the Data Warehouse that is the ETL process.

The structure of the data available in transactional systems is highly normalized as it is optimized for faster writes. But since a large amount of data in the Data Warehouse is to be used for analysis, it has to be optimized for 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 pre-computed 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 handle huge volumes of data to ensure business teams always have the most recent and accurate data in the analysis-ready form.

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 make the decision.

Conclusion

In this article, you read about what is the need for Data Warehouse and how companies can use Data Warehouse to make better data-driven business decisions quicker. It saves a lot of time and resources for companies. As the data complexity and other data issues become difficult for companies to work efficiently, they understood the need for Data Warehouse is important to stay ahead in business. 

There are several other challenges in setting up a Data Warehouse and Hevo Data can help you with loading and transforming data. We built the 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.

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