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?

Data visualization is a core element and key capability of embedded analytics. Embedded data visualization tools create real-time visualizations (charts, graphs, etc) and can be embedded into any software application or integrated into a cloud or standalone app, and provides every user quick and easy ways to visualize information. This article explores the benefits of using embedded data visualization tools in your applications, and discusses how it can improve upon the traditional analytics experience.

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.

As data takes the center stage for many organizations, data experts and business leaders are figuring out how to analyze it all. The total amount of data created, consumed, and copied globally has reached 64.2 zettabytes in 2020 and forecasted a CAGR of 19.2% from 2022 to 2025. In other words, we need better tools to leverage it! Organizations today leverage their data to provide better services, build better products, and create new revenue streamlines.

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.

In a recent webinar, Functional (& Funny) Strategies for Modern Data Architecture, we combined comedy and practical strategies for migrating from Hadoop to AWS. Unravel Co-Founder and CTO Shivnath Babu moderated a discussion with AWS Principal Architect, Global Specialty Practice, Dipankar Ghosal and WANdisco CTO Paul Scott-Murphy. Here are some of the key takeaways from the event.

One of the highlights of the DataOps Unleashed 2022 virtual conference was a roundtable panel discussion on building versus buying when it comes to your data stack. Build versus buy is a question for all layers of the enterprise infrastructure stack. But in the last five years — even in just the last year alone — it’s hard to think of a part of IT that has seen more dramatic change than that of the modern data stack.

The pandemic changed our healthcare behaviors. Planned hospital and doctor visits were reduced while telemedicine, for physical and mental health, increased. As healthcare providers and insurers/payers worked through mass amounts of new data, our health insurance practice was there to help.

Data holds incredible untapped potential for Australian organisations across industries, regardless of individual business goals, and all organisations are at different points in their data transformation journey with some achieving success faster than others. To be successful, the use of data insights must become a central lifeforce throughout an organisation and not just reside within the confines of the IT team. More importantly, effective data strategies don’t stand still.