One of the advantages of running databases on Azure SQL Database is the ability to dynamically manage them to adapt to changing workload demands. Autoscaling databases is the most cost effective way to increase performance of data operations. In this guide we'll show you how to auto scale your database using Azure Data Factory.
No one likes misunderstandings. We've all been in a "the Italian man who went to Malta" situation before. Annoyingly enough, this situation happens in a lot of businesses too. Many discussions between departments have the same root challenge: different people speaking different 'languages'. In this article, we’ll set the stage to take on this challenge.
A thriving Collibra instance is populated with a wide spectrum of data such as business terms, policies, code values, metadata of schemas, tables & columns, …. To keep Collibra relevant within an organization, this data needs to be accurate and up-to-date. Collibra provides tools like Collibra Catalog and the Collibra API to automatically import data with a set frequency.
Choosing the right ETL tool for your company is a complex task. Both Azure Data Factory and on-premise tools have their strengths and weaknesses. It's important to understand the parameters and nuances involved to pick the right tool for the job. In this article we'll clarify the key differences and help you make the right decision for your business.
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.
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.
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 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!
Many organizations struggle with ongoing data issues and are missing the opportunity to use data as a means to profoundly impact their business. What's usually missing is a comprehensive data strategy. Let's have a look at the 5 ingredients a successful long-term data strategy can't do without.
How to Analytics & Automation can save time (and help you to play more padel)
