watermark picture

Blog

Microsoft Azure Data Factory vs. on-premise ETL – tools: key features compared

A classic ETL-tool is a desktop tool which requires a good-sized server where the tool needs to be installed on. The server will also have to be managed and a basic monitoring needs to be set up to check the health of the system. This also means that the size of the server will have to be determined at front. The ETL-tool will have to be maintained and upgraded when necessary. ADF is a cloud-based, serverless service and does not require any hardware or any installation. Since no server is required, no monitoring or managing of it is needed. Updates of ADF are fully managed by Microsoft and will be implemented automatically.

When using a classic ETL-tool, scaling can be a bottleneck for an agile business. Classic ETL-tools are not always created to handle big volumes of data and the type of server cannot always be switched easily, which will result in a higher time to market. This is one of the main reasons why cloud is so conductive: your system can be upgraded in no time to each desired level.

Read More

Microsoft Azure Data Factory as a must-consider alternative for on-premise ETL – tools

When using cloud-based technology your data is processed, stored and maintained in the cloud and not on a physical server at your organization.

This means that no infrastructure is necessary for the set-up and you don’t need to worry about system maintenance. This helps in saving resources (time and money) at the start of a project which can be used to understand requirements of the business. Your cloud solution will also be more adaptable to changing situations : newer features can be added easily and up-scaling is only one click away. By the hand of the different data security protocols and features, you can sleep soundly that your data will be secure in the cloud.

Read More

AI for everyone? It’s no alchemy run by wizards!

We should use AI to make our organization smarter! Chances are you’ve recently heard this statement and see organizations acting to it. Leading companies are using AI across departments to increase productivity. Customer care organizations are using chatbots and speech recognition in their customer contacts. Marketing departments are predicting churn and segmenting their customer base.

Venturing into the field of AI may seem daunting at first. While AI has been hyped immensely in the last decade, it is a deeply technical field. Gartner even made a prediction that 80% of AI will remain alchemy run by wizards whose talents won’t scale in the organization. Your organization probably doesn’t have the scale and technological know-how of Google, Apple or Facebook. This might lead you to conclude that developing AI models isn’t for you. You are right, partially.

Read More

Let’s gear up your Data Intelligence program!

Imagine, you have launched your Data Governance program 5 years ago. Over those years a lot happened: your team designed and implemented a complete Data Governance strategy. You can proudly call yourself now GDPR compliant, your knowledge workers (e.g. data scientists) have access to data they trust and data is managed on a daily basis. You might think of taking the foot off the accelerator, but why not gear up? Automated data quality checks, data monetization opportunities and/or making your data management more efficient. Think big!

Read More