Modern businesses have vast amounts of data at their fingertips and are acutely aware of how enterprise data strategies positively impact business outcomes. Despite this, only a handful of organisations interact with all stages of the data life cycle process to truly distill information that distinguishes future-ready businesses from the rest.
Today’s enterprise IT organizations are experiencing a massive upheaval due to pressure from employee forces. It’s a familiar story. Just think of the turmoil caused by the dawning of the bring-your-own-device (BYOD) era, with employees demanding to use their beloved personal mobile phones for work.
Hitachi Vantara’s latest improvements to Pentaho make it significantly easier for organizations to move data workloads from on premises to the cloud and back again. The new Pentaho 9.3 Long-Term Support (LTS), part of Hitachi’s Lumada portfolio, offers a cloud deployment option that we anticipate will be a critical accelerant of data-driven transformation.
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?
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.
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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.
