Intro to Data Warehousing and Analytics - Liquid Technologies

Intro to Data Warehousing and Analytics

A data warehouse is a data management system specifically designed to facilitate and support businesses with their business intelligence (BI) actions and analytics. Data warehouses handle queries and support analytics and typically contain large quantities of historical information. The data within the data warehouse services are usually drawn from various sources, including transaction applications, log files and external data sources. 

It synthesizes and amalgamates huge amounts of data coming from different sources. The analytical potential of data warehousing enables companies and organizations to draw important business insights from their data. Analyzing this improves their decision-making and creates a bulk of historical data that comes in handy for business analytics consulting and data scientists. The inclusive qualities make data warehousing a perfect benchmark for any company to set up before leveraging their data for insights.

Advantages of Data Warehousing

Data warehouses deliver a broad spectrum of exclusive benefits, allowing businesses to analyze large quantities of different data, extract substantial value, and maintain historical records. Here are four characteristics that allow data warehousing to give out stunning advantages.

  • It is Integrated. Data warehouses create uniformity & integration among various types of data from different sources.
  • It is subject-oriented. It can analyze the data on a specific field or subject, such as sales.
  • Improves Data Quality. It Improves Data Quality. A good data Warehouse implementation cleans up a lot of dirty data.
  • It is Nonvolatile. Nonvolatile means the previous data is not erased when new data is added. A data warehouse is kept separate from the operational database, and therefore frequent changes in the operational database are not reflected in the data warehouse.

A properly designed data warehouse will have fast query response times, deliver a high volume of data, and allow users to narrow/drill-down & slice, and dice data to meet their analytical needs. It is the foundation for BI (business intelligence) environments that create reports, dashboards, and other interfaces that users can access.

The Architecture of Data Warehouse

Defining the architecture of a Data Warehouse is key when aligning business goals with technology goals. Each organization has its architecture based on its business needs and requirements. The most common architectural characteristics include: 

  • Basic Design: The data warehouse generally shares a similar and basic design. The data warehouse design has three main sections: the summary data, metadata, and raw data. The summary data is the most important part of the warehouse because it’s what everyone looks at first. This is where you’ll see all your metrics and analytics—you can use it to see how well your website is doing and what kinds of products are selling best.
  • Running a Hub and Spoke: Data marts are an integral part of a data warehouse, allowing organizations and companies to customize their data utility. This would cater to different requirements and use cases that the business comes up with. Once the data is ready to use, it is passed through a data mart and used by the external reporting tools.
  • Simplify data with a staging area: The bulk of data needs to be cleaned and processed before going to the warehouse production layer. Most organizations already have a staging area for their data to increase the quality of the data. 

Use of sandboxes: Sandboxes are useful for creating secure and safe data layers that allow organizations to rapidly and informally access new datasets which have not been formalized into the production layer yet.

How did Data Warehousing evolve from Analytics to

AI-Based machine learning?

Data warehouses initially appeared in prominence in the late 1980s. Their primary function was to facilitate the flow of data from operational systems to decision-support systems (DSSs). The first data warehouses needed huge amounts of redundancy. The majority of enterprises had multiple DSS environments, which served different users. Even though the DSS environments shared a lot of the same information, the collection, cleaning, and integration of data were usually duplicated for every environment.

As data warehouses improved efficiency, they developed from data stores that served traditional BI platforms to broad analytics infrastructures that can support many applications, like performance management and operational analytics.

The iterations of the data warehouse have been improved over time and promise to deliver additional incremental value to the enterprise industry of data warehouse.

The following steps helped change the outlook of data warehousing and shift it to a broad spectrum of the marketplace. 

Support the five phases required using a broader array of data sources. The last three steps warrant the need for a greater variety of analytics and data capabilities.

AI and machine learning transform almost every field, service and corporate utility. Data warehouses are also no exception. The growing use of big data and digital technology has triggered changes in data warehouses’ needs and capabilities. An independent database is the latest stage in this direction, allowing companies to extract the most value from their data while reducing costs and improving the data warehouse’s performance and reliability.

Open AI: Pushing the Boundaries of AI and Revolutionizing the Industry for a Better Future

What is Open AI? In the early 2000s, the global growth of Artificial Intelligence (AI) raised concerns about the potential for an uncontrolled and emotionless intelligence explosion which could pose an existential threat to humanity. Recognizing this challenge, Sam Altman and Elon Musk took the lead in promoting the safe and open development of AI. […]

Read More

How to pick the right tech partner

Creating your own, custom software is not an easy task, especially if you don’t really have any experience with tech. But should that be the case? Building a product, growing a community around it, and spearheading it into success is not the easiest of processes. You can’t possibly get the most benefit out of the […]

Read More

Intro to Data Warehousing and Analytics

A data warehouse is a data management system specifically designed to facilitate and support businesses with their business intelligence (BI) actions and analytics. Data warehouses handle queries and support analytics and typically contain large quantities of historical information. The data within the data warehouse services are usually drawn from various sources, including transaction applications, log […]

Read More

Your guide to an MVP

Growing a business is like climbing a mountain. You have a vision of where you want to go, but the path isn’t always clear. You take one step at a time, learning as you go.

Read More

Artificial Intelligence and it’s Efficacies

Before delving into how Artificial Intelligence is used in Pakistan, it is important to first understand what exactly Artificial Intelligence (AI) is.

Read More

A Step-by-Step Guide to Creating a Power BI Dashboard

Data visualization has grown to become an integral part of our work lives, be it personal or corporate.

Read More

Serverless Development

There was a time when developers and companies invested a lot in hardware and places to store them.

Read More

Hybrid vs Native App Development

Application Development is an ever-growing industry. The ease with which the Mobile Apps have created has led to an increase in the frameworks and technology spurring its growth with the need going up

Read More

Data Driven Dashboards

Liquid Technologies is a Data Consulting, Data Visualization and Insights Gathering Company with a focus on Predictive Intelligence and scalable software solutions on the cloud.

Read More

Predictive Intelligence Training

For starters, Predictive Intelligence goes by several names, most notably Predictive Analytics and Predictive Recommendations. Each has a similar meaning—for our purposes, we’ll use Predictive Intelligence.

Read More

Business Intelligence And Analytics In Retail Industry

In today’s world, Data plays an important role in how we do business, more and more data driven companies are coming up the block.

Read More

4 Main Ways to use Business Intelligence in your Business

In this article, we will discuss how we can use BI in our day to day businesses in order to achieve success

Read More

How Business Intelligence is Disrupting the Retail Industry!

Business Intelligence has transformed the way we do business over the years, by dishing out billions of data points from multiple data sources and impacting different industries.

Read More

Liqteq’s BI Restructuring Data-Driven Decision Making In The Pharma Industry

Today’s pharmaceutical companies are large and complex with a critical need for information and data.

Read More

Banks – How liqteq’s BI insights are taking Banks to the next level

Business Intelligence (BI) is responsible for enhancing the banking operations like identifying patterns, analyzing connections, addressing and resolving issues in real-time etc.

Read More

Why Your Company Needs To Care About Big Data

First of all, lets clear out the buzzwords, which are big data, artificial intelligence and digital automation.

Read More

2021

Top Mobile App Development Company On Clutch

2021

Top Mobile App Development Company On Good Firms

2021

Top Mobile App Development Company On Futura

2022

Top Mobile App Development Company On UpCity

Schedule a call with our Growth Advisors

Contact Us

Trusted by

Copyright © 2023 Liquid Technologies | All Rights Reserved.