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Cloud

Deliver Analytics-Ready Data to the Cloud With Snowflake and Hitachi Vantara

One of the toughest challenges for data professionals today is migrating data from on-premises environments to the cloud. Many companies still lack the tools and infrastructure to ingest and process complex datasets to achieve critical business outcomes. Tune in for a joint-session with Snowflake and Hitachi Vantara as we discuss best practices to address common edge-to-multicloud issues and how our joint offering can dramatically simplify data preparation, migration and analytics tasks to help deliver analytics-ready data in the cloud.

Real-Time Cost Alerts and Forecasts for AWS

For many companies, cloud costs are among the top investments these days. With a growing number of services, instances and regions, cloud cost optimization is becoming increasingly painful. Companies use cloud management platforms to optimize costs and increase cloud visibility and security. But staying on top of AWS budgets requires proficiency, agility and time—especially when any glitch can result in massive cost bleeds.

Cloud, big data analytics & AI are driving change in Finance

How Cloud Computing is evolving alongside Big Data, Analytics, and AI in Financial Services. New technology like Artificial Intelligence (AI), Cloud Computing, big data, and prescriptive analytics are changing the way the Financial Services sector does business. With evolving tech comes both new opportunities as well as different risks, and companies within the space must innovate and embrace new ideas as shifting business conditions and changing consumer preferences dictate new norms.

What is Serverless Computing?

The shift to cloud computing fundamentally changed the way software is built and consumed by developers. Multiple code snippets or functions are logically connected to form a complex application. Since the platform deals with one function at a time, and functions are the fundamental deployment units, this model is often called as Functions as a Service (FaaS).

Amazon EMR Insider Series: Optimizing big data costs with Amazon EMR & Unravel

Data is a core part of every business. As data volumes increase so do costs of processing it. Whether you are running your Apache Spark, Hive, or Presto workloads on-premise or on AWS, Amazon EMR is a sure way to save you money. In this session, we’ll discuss several best practices and new features that enable you to cut your operating costs and save money when processing vast amounts of data using Amazon EMR.

Adoption of a Cloud Data Platform, Intelligent Data Analytics While Maintaining Security, Governance and Privacy

“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.

It Takes Two to Kafka: AWS MSK + DataOps

I ordered a ride share recently from a beach; the app struggled to find a car, so I had to make several requests. After the fourth or fifth attempt, my bank alerted me to possible fraudulent activity on my credit card via SMS. Each time I ordered a ride, the service put a pending charge on my card. After I texted back that it was just me, the bank reactivated my account. Though the process was annoying, I felt reassured my bank could detect possible fraudulence that quickly.

Good Catch: Cloud Cost Monitoring

Aside from ensuring each service is working properly, one of the most challenging parts of managing a cloud-based infrastructure is cost monitoring. There are countless services to keep track of—including storage, databases, and computation—each with their own complex pricing structure. Monitoring cloud costs is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time and accurately forecast monthly costs.