“The COVID pandemic has spoiled many forecasting models. We can’t use historical data to predict a scenario that has never happened before. Many companies have not included data from 2021 to train their models because it’s biased”.
Category: Cloud
According to the National Institute of Standards and Technology (NIST), the Cloud is a model. It enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. Those resources are networks, servers, storage, applications, and services. The Cloud allows quickly provisioning the resources with minimal effort or interaction.
In my opinion, the main benefit of the Cloud is that it allows innovation with native technologies. For example, several Machine Learning services are not available on-prem.
It also improves collaboration by making access to the data easier for anyone who needs it – anywhere, anytime within an organisation.
Moreover, the Cloud services perform many operations under the covers, so you can spend more time doing more practical tasks for your business, rather than just making maintenance tasks. Most of the services already do many of those tasks for you, such as scaling up and down automatically or collecting statistics in some databases.
With the increasing adoption of the Cloud paradigm, it has become critical to design and maintain hybrid architectures. In these scenarios, organisations perform part of their operations on-prem, streaming data to the Cloud. I explain different situations, use cases, and solutions in this category.
Additionally, it is pretty frequent that companies migrate their databases. For me, the secret sauce on an excellent migration is to plan carefully. I dedicate some posts on different aspects of database migration. However, I always insist on the tips to architect and design the migrations.
Equally important is keeping an analytical ecosystem in the Cloud. So I also have some posts on best practices for these ecosystems and how to leverage their benefits and native technologies.
Fuzzy Matching Demo: Inconsistent Company Names
Fuzzy matching use case. We improve data quality when loading data into BigQuery using Trifacta software, which simplifies the process.
Customer Churn Demo
This post explains how to predict customer churn, a typical Machine Learning use case, using Google Cloud services.
A brief introduction to BigQuery’s architecture
This post helps to understand BigQuery’s internal design and how efficiently to load, replicate or migrate data into it.
Qlik Replicate: Optimising the extraction of data from Salesforce with PK Chunking
This post explains how to get the best performance when replicating data from Salesforce. Leverage Salesforce’s chunkSize parameter to extract large amounts of data.
Data Replication & Qlik Replicate
This post explains what Data Replication is and how it differentiates from ETL/ ELT, what Change Data Capture (CDC) is, and a few Qlik Replicate use cases.