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How to reduce the disruption created by big data migrations
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Quick guide to Amazon EC2 R7i for memory-intensive workloads
Are you familiar with the Amazon Elastic Compute Cloud (Amazon EC2) R7i instance?
This blog offers a quick guide to the instance, which is powered by Intel processors, and how it can support memory-intensive workloads.
Keep reading to discover:
- 4 acceleration features of the R7i instance
- Trends in the in-memory database market
- Benefits that the instance can deliver, including lower TCO
- Where R7i instances are currently available
- And more
These are also closely related to: "How to reduce the disruption created by big data migrations"
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The complete guide to becoming a data-driven organization
Today, organisations are struggling with the performance and capability disparities that exist between legacy technologies and a modernised cloud. Companies know they must modernise. But they often choose to make incremental changes that can take months or years to implement.
By contrast, businesses that want to become data-driven enterprises must evolve quickly. In fact, rapid modernisation in the cloud is the most direct path to reaping the benefits of the analytics and machine learning technologies that can position an organisation as a market leader.
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Spark Muscles Into Big Data Processing
It comes as no great surprise that the Apache Spark architecture has been horning in on the batch processing domain once controlled by Hadoop's MapReduce. But that's only part of the story. With data processing, streaming and machine learning capabilities on its résumé, the open source engine is learning to get along entirely without Hadoop in certain applications. In fact, one industry analyst cautiously sees a day when Spark could declare total independence, potentially bust up Hadoop cluster dominance and link separately with other Apache technologies.
In this three-part handbook, senior news writer Jack Vaughan examines the distinct advantages the Apache Spark architecture has over MapReduce. Also highlighted is how Spark's ability to process and analyze streaming data is helping detect fraudulent activities at a major banking and credit-card company. Next, Spark 2.0's upcoming upgrades to analytics speed, machine learning libraries, SQL support and stream processing are detailed. To close, Vaughan and senior news writer Ed Burns look at combining Spark and NoSQL databases in operational analytics applications, which could help broaden the use of both technologies.
