The skyrocketing value of data has created a global supply and demand for data, data applications, and data services. This new data economy is powered by technologies that enable data access and sharing, including cloud platforms, exchanges, and marketplaces.
In the exponentially growing data warehousing space, it is very important to capture, process and analyze the metadata and metrics of the jobs/queries for the purposes of auditing, tracking, performance tuning, capacity planning, etc. Historically, on-premise (on-prem) legacy data warehouse solutions have mature methods of collecting and reporting performance insights via query log reports, workload repositories etc. However all of this comes with an overhead of cost-storage & cpu.
The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another. But instead of getting bogged down in the “what” of observability, I think it’s more valuable to address the “why.” What are we trying to accomplish with observability? What is the end goal?
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Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.
Communications service providers (CSPs) are rethinking their approach to enterprise services in the era of advanced wireless connectivity and 5G networks, as well as with the continuing maturity of fibre and Software-Defined Wide Area Network (SD-WAN) portfolios.
