Factors to Consider When Choosing MongoDB for Big Data Applications
Technology advancements have brought about advantages than need to be exploited by business organizations for maximum profit value and reduced operational cost. Data has been the backbone for these technological advancements from which sophisticated procedures...
Running a Data Warehouse on PostgreSQL
When you need to implement an analytics system for a company there is often the question of where the data should be stored. There is not always a perfect option for all the requirements and...
Running Big Data Analytics Queries Using SQL and Presto
Presto is an open-source, parallel distributed, SQL engine for big data processing. It was developed from the ground-up by Facebook. The first internal release took place in 2013 and was quite a revolutionary solution for...
Big Data with PostgreSQL and Apache Spark
PostgreSQL is well known as the most advanced opensource database, and it helps you to manage your data no matter how big, small or different the dataset is, so you can use it to manage...
An Introduction to Data Lakes
The increase of unstructured data is a severe challenge to enterprises. Over the past decade, we could observe the rapid growth of data being produced and innovative changes to the way information is processed. With...
Handling Large Data Volumes with MySQL and MariaDB
Most databases grow in size over time. The growth is not always fast enough to impact the performance of the database, but there are definitely cases where that happens. When it does, we often wonder...
Big Data Integration & ETL – Moving Live Clickstream Data from MongoDB to Hadoop for Analytics
MongoDB is great at storing clickstream data, but using it to analyze millions of documents can be challenging. Hadoop provides a way of processing and analyzing data at large scale. Since it is a...