Author: Data Team
Here's how we use the data protection features within SQL Server to protect confidential data and make it available to only those authorized to see it.
We dig into Realtime dataset options (including Pushed, Streaming and PubNub). storage approaches for high-volume/velocity data, and cloud-based data integrity.
When neither Realtime nor Near-Realtime data processing meets your needs, there is a third option. This low-latency option is ideal for transactional data.
What factors go into handling these data sources? How do we process them? How do we provide data visualizations based on those sources to meet business needs?
We custom-made an IoT framework with Azure components and Power BI to implement a scalable solution to source, stream and show machine performance in real-time.
When low-quality data strikes, Fuzzy Logic can simplify matching, deduplication and data cleansing. With this tool, you can ensure big data can be clean data.
Depending on your use case, and especially for simple matching scenarios, the Fuzzy Lookup Excel Add-In can be used to help solve your fuzzy lookup problems.
Data “in the wild” is often raw and insights can be lost. But data visualization makes the data more consumable, easier to understand, faster to act upon.
By designing and implementing a dynamic scaling plan for your Azure solution, you can get the resources you need while reducing cost at the same time.
Learn about Fuzzy Grouping and its similarities to Fuzzy Lookups, as well as how you can use the Grouping transformation to perform data deduplication.