Blog

Low Code Analytics at Top Speed
The Megaladata platform employs methods that ensure high-speed processing, efficient memory use, and easy scaling. Let's take a deeper look at what enables the platform's high performance and explore the...
Megaladata: What's under the hood?
The article describes the technological basis of the low code Megaladata platform — its components, architecture, frontend and backend, functionality, and performance. We intend to answer the most...
Cohort Analysis: Why Is It Valuable?
Every entrepreneur striving for effective business management comes to ask themself a question: What is the income brought by one client?
Coding in Megaladata: Python vs. JavaScript
The article covers the technical aspects of using programming languages in Megaladata. We compare the two languages, JavaScript and Python, analyze their limitations, and provide guidance on how to use...
Shewhart Charts as a Tool to Control Business Processes
Every business experiences failures in business processes from time to time. Finding and eliminating the causes takes not only time but also money. It is crucial to perform real-time monitoring of the...
Advanced Analytics Platforms and BI Systems — What's the Difference?
Advanced analytics platforms and Business Intelligence systems employ different approaches when dealing with data. Although data analysis takes place in both cases, there are significant differences between...
Release notes 7.1.4
Fixed: critical error in Calculator, Megaladata Integrator issues, visual problems in the workflow area. Package installation optimized.
Missing Data Imputation
Missing data is a very common issue in real-world data processing. The reasons may vary — data entry errors, information hiding, fraud, and so on. In this article, we discuss cases where incorrect handling...
Release notes 7.1.2
In the Server editions, it is now possible to connect to SAP HANA through ODBC. Fixed: Megaladata Integrator, Calculator, and some data source connections. Platform performance increased, logging...
Data leakage in machine learning
In machine learning, data leakage refers to a situation where one or more input features used during the model's learning process become unavailable when the model is applied in practice. Data leakage...
Release notes 7.1.1
Fixes made to visualizers, PostgreSQL connection, and some components. Megaladata Desktop Edition performance improved.
Theory of constraints: how to expand bottlenecks in data analysis
When implementing analytical projects, it is not too efficient to try to improve the system as a whole. Let’s take a look at the theory of constraints to see how low code development helps reduce the...
Release notes 7.1.0
In the new version, we made fixes to the application, the Export group components, database Connections, Association rules, and other components.
The Apriori algorithm for association rule learning
Apriori is one of the most popular algorithms for determining association rules. Employing the property of anti-monotonicity, Apriory is able to process large volumes of data within a reasonable amount of...
What's new in Megaladata 7.1
The First Major Update of the 7th Megaladata Version While working on the release, considerable attention was paid to the requests that came from our users. The main changes affected safety and usability...
Data quality criteria
Data quality is a generalized concept that describes the degree to which information is suitable for analysis. There are a number of criteria used to assess the correctness, completeness, accuracy and...
Scalable CLOPE algorithm for clustering categorical data
Splitting of categorical and transactional data arrays into groups with similar attributes is the most important task of data mining. In most cases, traditional clustering algorithms are not effective when...
Smart sales analysis for wiser business management
The combined use of ABC and XYZ analysis will help you to better navigate the product range, optimize logistics and warehouse stocks, segment customers and partners, and properly set up interactions. ...
Implementing a decision support system: A case of one financial organization
How to migrate to a modern analytical platform and replace the database without stopping the work of the decision making pipeline. An actual case of a leader of the microfinance market.
Release notes 7.0.4
Several regression errors were corrected, including in Connection to databases. Errors were corrected in Megaladata Integrator, ARIMAX and other components.
Clustering algorithms in data mining
This article is an attempt to systematize the latest achievements in the development of data clustering techniques. The purpose of this article is not to providea detailed description of all clustering...