Navistar is a leading global manufacturer of commercial trucks. With a fleet of 350,000 vehicles, unscheduled maintenance and vehicle breakdowns created ongoing disruption to their business. Navistar required a diagnostics platform that would help them predict when a vehicle needed maintenance to minimize downtime.
Strong investments in an organization’s data pipeline result in greater business outcomes. Few would dispute this claim, reflected in the massive growth in the big data and analytics market which continues to fuel many organizations’ ambition to become data-driven.
“Quality is “value to some person” (quoting Jerry Weinberg) and as such is highly subjective. Because of that, there is rarely a set of metrics that can be used as a recipe for most cases; which in turn makes indicators of readiness to deploy, data to be collected, etc.
Apache Hive supports transactional tables which provide ACID guarantees. There has been a significant amount of work that has gone into hive to make these transactional tables highly performant. Apache Spark provides some capabilities to access hive external tables but it cannot access hive managed tables. To access hive managed tables from spark Hive Warehouse Connector needs to be used.
As a result of the COVID-19 pandemic, organizations around the world have had to transform overnight. Businesses that had been delaying digital transformation, or that hadn’t been thinking about it at all, have suddenly realized that moving their data analytics to the cloud is the key to coping with and surviving the COVID-19 disruption. The next phase is about rebounding and thriving in a post-COVID-19 world.
Being data-driven helps businesses to cut costs and produce higher returns on investments, increasing their financial viability in the fight for a piece of the market pie. But *becoming* data-driven is a more labor-intensive process. In the same way that companies must align themselves around business objectives, data professionals must align their data around data models. In other words: if you want to run a successful data-driven operation, you need to model your data first.
