There are some gaps in data management and maintenance space in Azure. Following are the two things that I feel are missing from the current landscape of Azure and will hopefully be addressed soon
The key reasons for the need of good data lake structure are: 1) Security: need of role-based security on the lake for read access. 2) Extendibility: it should be easy to extend the lake after first round and more systems can be added 3) Usability: it should be easy to use and find the data in the lake and the users should not get lost 4) Governance: it should be simple to apply governance practices to the lake in terms of quality, metadata management and ILM
From technology point of view Databricks is becoming the new normal in data processing technologies, in both Azure and AWS. This post provides a view of lambda architecture and uses Databricks at front and center. Databricks has capabilities to replace multiple tools and those are described in bit detail below
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods. This approach of architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data.
Cost Management solution in Azure helps in monitoring, optimizing and controlling costs of Azure Resources in the subscription and Resource Groups. Cost Management shows organizational cost and usage patterns with advanced analytics.
Databricks has become the new normal in the data processing in cloud. If you are using or plan to use Azure Databricks, this post is will guide you on some interesting things that you can plan to investigate as you start.
Distributed computing technology enables the compute load to be spread, or distributed, across multiple nodes (computers) connected via a network.