6 Ways to Optimize Your Database for Performance
Knowing how to optimize your organization’s database for maximum performance can lead to greater efficiency, productivity,…
CAP Theorem
Understanding databases for storing, updating and analyzing data requires the understanding of the CAP Theorem. This is the…
Graphical understanding of dynamic programming and the Bellman equation: taking a typical approach at first
This is the second article of the series My elaborate study notes on reinforcement learning.
1, Before getting down on business
As…
ACID vs BASE Concepts
Understanding databases for storing, updating and analyzing data requires the understanding of two concepts: ACID and BASE.…
Data Warehousing Basiscs
Data Warehousing is applied Big Data Management and a key success factor in almost every company. Without a data warehouse,…
Why Do Companies Use Data Lakes?
Modern enterprise-level computing operations have to capture a truly monumental amount of information every single hour.…
Understanding the "simplicity" of reinforcement learning: comprehensive tips to take the trouble out of RL
This is the first article of my article series "My elaborate study notes on reinforcement learning."
*I adjusted mathematical…
Process Mining mit Fluxicon Disco – Artikelserie
Dieser Artikel der Artikelserie Process Mining Tools beschäftigt sich mit dem Anbieter Fluxicon. Das im Jahr 2010 gegründete…
Why Your Data Science Team Needs Separate Testing, Validation & Training Sets
Automated testing of machine learning models can help to dramatically reduce the amount of time and effort that your team…
6 Ways to Optimize Your Database for Performance
/0 Comments/in Data Engineering /by Shannon FlynnKnowing how to optimize your organization’s database for maximum performance can lead to greater efficiency, productivity, and user satisfaction. While it may seem challenging at first, there are a few easy performance tuning tips that you can get started with. 1. Use Indexing Indexing is one of the core ways to give databases a performance […]
CAP Theorem
/0 Comments/in Big Data, Business Intelligence, Data Engineering, Data Warehousing, Main Category /by Benjamin AunkoferUnderstanding databases for storing, updating and analyzing data requires the understanding of the CAP Theorem. This is the second article of the article series Data Warehousing Basics. Understanding NoSQL Databases by the CAP Theorem CAP theorem – or Brewer’s theorem – was introduced by the computer scientist Eric Brewer at Symposium on Principles of Distributed computing in […]
Graphical understanding of dynamic programming and the Bellman equation: taking a typical approach at first
/0 Comments/in Artificial Intelligence, Data Mining, Data Science, Deep Learning, Main Category /by Yasuto TamuraThis is the second article of the series My elaborate study notes on reinforcement learning. 1, Before getting down on business As the title of this article suggests, this article is going to be mainly about the Bellman equation and dynamic programming (DP), which are to be honest very typical and ordinary topics. One typical […]
ACID vs BASE Concepts
/0 Comments/in Big Data, Business Intelligence, Data Engineering, Data Warehousing, Database, Main Category /by Benjamin AunkoferUnderstanding databases for storing, updating and analyzing data requires the understanding of two concepts: ACID and BASE. This is the first article of the article series Data Warehousing Basics. The properties of ACID are being applied for databases in order to fulfill enterprise requirements of reliability and consistency. ACID is an acronym, and stands for: […]
Data Warehousing Basiscs
/0 Comments/in Big Data, Business Intelligence, Data Engineering, Data Migration, Data Warehousing, Database, ETL, Graph Database, InMemory, Main Category /by Benjamin AunkoferData Warehousing is applied Big Data Management and a key success factor in almost every company. Without a data warehouse, no company today can control its processes and make the right decisions on a strategic level as there would be a lack of data transparency for all decision makers. Bigger comanies even have multiple data […]
Why Do Companies Use Data Lakes?
/0 Comments/in Data Engineering, Data Warehousing /by Philip PileticModern enterprise-level computing operations have to capture a truly monumental amount of information every single hour. As the scale of data has grown almost exponentially over the years, so has the scale and complexity of data stores. Traditional databases couldn’t possibly keep up with the massive numbers of records that have to be created today, […]
Understanding the “simplicity” of reinforcement learning: comprehensive tips to take the trouble out of RL
/0 Comments/in Uncategorized /by Yasuto TamuraThis is the first article of my article series “My elaborate study notes on reinforcement learning.” *I adjusted mathematical notations in this article as close as possible to “Reinforcement Learning:An Introduction.” This book by Sutton and Barto is said to be almost mandatory for those who studying reinforcement learning. Also I tried to avoid as […]
The Basics of Logistic Regression in Data Science
/0 Comments/in Data Science /by Shannon FlynnData science is a field that is growing by leaps and bounds. A couple decades ago, using a machine learning program to make predictions about datasets was the purview of science fiction. Today, all you need is a computer, some solid programming, and a bit of patience, and you, too, can have your own digital […]
Process Mining mit Fluxicon Disco – Artikelserie
/0 Comments/in Business Analytics, Business Intelligence, Main Category, Process Mining, Tool Introduction, Tools /by Benjamin AunkoferDieser Artikel der Artikelserie Process Mining Tools beschäftigt sich mit dem Anbieter Fluxicon. Das im Jahr 2010 gegründete Unternehmen, bis heute geführt von den zwei Gründern Dr. Anne Rozinat und Dr. Christian W. Günther, die beide bei Prof. Wil van der Aalst in Eindhoven promovierten, sowie einem weiteren Mitarbeiter, ist eines der ersten Tool-Anbieter für Process […]
Why Your Data Science Team Needs Separate Testing, Validation & Training Sets
/0 Comments/in Data Science /by Philip PileticAutomated testing of machine learning models can help to dramatically reduce the amount of time and effort that your team has to dedicate to debugging them. Not applying these techniques properly, however, could potentially do a great deal of harm if the process is completely unmonitored and doesn’t adhere to a list of best practices. […]