Are you sure your Hive queries are performing at their best? You might be surprised. Apache Hive is the most prevalent query engine used in many of the largest enterprise environments today, but that doesn’t mean it works optimally automatically. To get the most out of the engine and achieve Hive query optimization, it’s important to tune its performance. But before we dive into that, let’s cover the basics of Hive performance tuning. What is Hive performance tuning?

Ask anyone who’s managing any cloud environment of any size, and they will undoubtedly bring up the stress they experience on a regular basis over tags. Since the dawn of cloud, every cloud user has struggled with the challenge of tagging — essentially the only mechanism that exists for you to apply metadata to your cloud environment. The fact that tags themselves are an imperfect tool — both inflexible and hard to manage — makes no difference.

Organizations often assume that switching to Amazon Web Services (AWS) will automatically reduce their on-premises spending. Certainly, cloud-based services like AWS' Infrastructure-as-a-Service (IaaS) can offer significant cost savings. Yet overspending on AWS is quite common. How can you tell if moving to AWS is the right decision? What figures would you use to quantify and justify that choice to decision-makers? Enter Total Cost of Ownership (TCO) on AWS.

Amazon Simple Storage Service (S3) is an essential cornerstone of AWS and among its most popular service offerings. S3 allows tenants to store, secure, and retrieve data from S3 buckets on demand. It is widely used for its high availability, scalability, and performance. It supports six storage classes and several use cases, including website hosting, backups, application data storage, and data lake storage. There are two primary components of Amazon S3: Buckets and Objects.

The widespread adoption of digital transformation triggered by the global health crisis has created a boom. For some businesses, the transition was smooth, but for others, it was an aggressive shift. Out of all the challenges posed by the transition to digital environments, messy cloud cost management and rocketing cloud bills are the most taxing. According to Gartner, through 2024, 60% of infrastructure and operations leaders will bear cloud costs that hurt their on-premises budgets.

Many companies don’t know where to begin when migrating to AWS. Some worry their data will leak, while others don't know the most efficient migration strategy for AWS. Another group worries about overspending when moving to AWS. The migration strategy involved plays a crucial part in all of these concerns. This guide will discuss more than just AWS cloud migration strategies.

The best budgets aren't roadblocks. They're guardrails: boundaries for quick, collaborative work. Historically, when finance and engineering teams have discussed cloud cost, they’ve run into an obstacle: They don’t speak the same language. At the end of each month, finance gets an ever-changing cloud bill, and engineering explains that, whatever the total, it represents what they need to do their work. Stalemate.

Not all cloud cost management tools are equal. Whether you’re in the process of evaluating cloud cost management vendors or already have a tool in place, here are 19 questions you should ask to ensure you have all the capabilities needed to maximize performance and minimize cost of your hybrid cloud deployment across the following.

Moving large amounts of data to the cloud can be arduous and time-consuming. A cloud migration would take years if engineers manually moved data from assessment through mobilization and migration phases. An effective cloud migration also requires adequate data encryption, fast data transfer speeds, and constant monitoring. Migrating workloads to AWS requires you to monitor costs in real-time as well to avoid overspending.

As today’s IT infrastructure becomes more complex, a continued reliance on legacy systems can become costly as IT operations get bogged down in inefficiencies and hampered by unreliability.