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.
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.
Over the last 25 years, I have an unparalleled front seat to the digital transformation that is now accelerating in the connected manufacturing and automotive industry. Not many people have had the opportunity to witness the transformation and be as active in this area as I have; I consider myself lucky.
Most of us know the story of “The Tortoise and the Hare.” It is one of Aesop’s classic fables in which a speedy, overconfident hare becomes complacent and realizes, all too late, that the tortoise, although outmatched, has managed to beat him in a race. It teaches us lessons about overconfidence and perseverance and has caused phrases like “slow and steady wins the race” to creep into our everyday language.
“You cannot be the same, think the same and act the same if you hope to be successful in a world that does not remain the same.” This sentence by John C. Maxwell is so relevant to rapidly changing cloud hosting technology. Businesses understand the added value and are looking at cloud technologies to handle both operational and analytical workloads.
