Category: Amazon Redshift

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. Data warehouses are mostly built using the dimensional model approach, which has consistently met business needs. Loading complex multi-point datasets into a […]

This post was co-written with Django Bouchez, Solution Engineer at Toucan. Business intelligence (BI) with dashboards, reports, and analytics remains one of the most popular use cases for data and analytics. It provides business analysts and managers with a visualization of the business’s past and current state, helping leaders make strategic decisions that dictate the […]

This is a guest post by Miguel Chin, Data Engineering Manager at OLX Group and David Greenshtein, Specialist Solutions Architect for Analytics, AWS. OLX Group is one of the world’s fastest-growing networks of online marketplaces, operating in over 30 countries around the world. We help people buy and sell cars, find housing, get jobs, buy […]

This post was co-written with Amit Shah, Principal Consultant at Atos. Customers across industries seek meaningful insights from the data captured in their Customer Relationship Management (CRM) systems. To achieve this, they combine their CRM data with a wealth of information already available in their data warehouse, enterprise systems, or other software as a service […]

It’s common to ingest multiple data sources into Amazon Redshift to perform analytics. Often, each data source will have its own processes of creating and maintaining data, which can lead to data quality challenges within and across sources. One challenge you may face when performing analytics is the presence of imperfect duplicate records within the source data. This post presents one possible approach to addressing this challenge in an Amazon Redshift data warehouse using fuzzy matching.

Serverless applications are a modernized way to perform analytics among business departments and engineering teams. Business teams can gain meaningful insights by simplifying their reporting through web applications and distributing it to a broader audience. Use cases can include the following: Dashboarding – A webpage consisting of tables and charts where each component can offer […]

In 2021 and 2020, we told you about the new features in Amazon Redshift that make it easier, faster, and more cost-effective to analyze all your data and find rich and powerful insights. In 2022, we are happy to report that the Amazon Redshift team was hard at work. We worked backward from customer requirements […]

Amazon Redshift is a fully managed data warehousing service that is currently helping tens of thousands of customers manage analytics at scale. It continues to lead price-performance benchmarks, and separates compute and storage so each can be scaled independently and you only pay for what you need. It also eliminates data silos by simplifying access […]

This is a guest post co-authored by Mahesh Vandi Chalil, Chief Technology Officer of BookMyShow. BookMyShow (BMS), a leading entertainment company in India, provides an online ticketing platform for movies, plays, concerts, and sporting events. Selling up to 200 million tickets on an annual run rate basis (pre-COVID) to customers in India, Sri Lanka, Singapore, […]

Extract, Load, and Transform (ELT) is a modern design strategy where raw data is first loaded into the data warehouse and then transformed with familiar Structured Query Language (SQL) semantics leveraging the power of massively parallel processing (MPP) architecture of the data warehouse. When you use an ELT pattern, you can also use your existing […]