The Enterprise Data Warehouse (EDW) offload is a widespread big data use case. This is certainly because traditional data warehouse and related etl processes are struggling to keep the pace in the big data integration context. Many organisations are looking to integrate new big data sources that come with the following constraints: volume, velocity and […]
Data Integration
In our previous blog post, we’ve discussed the implementation of a framework built on top of Spark to enable agile and iterative data discovery between legacy systems and new data sources generated by IoT devices (smart city data set). We will now explore in detail, the components of this framework. The framework is composed by […]
In this series of blog posts, we will outline and explain in detail the implementation of a framework built on top of Spark to enable agile and iterative data discovery between legacy systems and new data sources generated by IoT devices. The internet of things (IoT) is certainly bringing new challenges for data practitioners. It’s […]
This post is meant to help you making your first step into data processing with Apache Spark using python API. In the age of Big Data processing, Hadoop map reduce (open source implementation of google map reduce model) has set down the foundation for processing “embarrassingly parallel” operations on distributed machines. Sadly, it shows programmability limitations and degradation in […]
In the previous post, we discussed data warehouse concept and emphasis key aspects such as data consistency, data history, complexity of the data integration process as the number of data sources grows and time to market. The enterprise data warehouse is built for analytical purpose and stored mainly structured data. It gives a competitive advantage […]
Over the past weeks I was involved in few discussions where it appeared that the difference between enterprise data warehouse and data lake is not crystal clear for some decision makers. Let’s explore in this series of two posts the key differences between these two concepts and how they can be bound together. The enterprise […]
It’s widely acknowledged that data scientists spend most of their time on data integration (curation) therefore focusing less on their core activities. Let’s have a look at an appealing tentative to mitigate this effort. Most companies tend to integrate more and more data sources in their data warehouse over the past years. It has been […]
