Tachyon is a memory-centric distributed storage system enabling reliable data sharing at memory-speed across cluster frameworks, such as Spark and MapReduce. It achieves high performance by leveraging lineage information and using memory aggressively. Tachyon caches working set files in memory, thereby avoiding going to disk to load datasets that are frequently read. This enables different jobs/queries and frameworks to access cached files at memory speed.
Lambda Architecture proposes a simpler, elegant paradigm designed to store and process large amounts of data. In this article, author Daniel Jebaraj presents the motivation behind the Lambda Architecture, reviews its structure with the help of a sample Java application.
The job of a search engine is, first and foremost, to provide answers to user’s queries. In response to each query, a search engine returns links to web pages it finds in its index – a database of web pages known to this particular search engine. Thus, an answer to the user’s query comes in the form of search results – a list of hyperlinks to web pages, whose content matches this query.
This post was written by the team behind DataCamp, the online interactive learning platform for data science. After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting […]
This a guest post by Anshu Prateek, Tech Lead, DevOps at Aerospike and Rajkumar Iyer, Member of the Technical Staff at Aerospike.
In our first post we busted the myth that cloud != high performance and outlined the steps to 1 Million TPS (100% reads in RAM) on 1 Amazon EC2 instance for just $1.68/hr. In this post we evaluate the performance of 4 Amazon instances when running a 4 node Aerospike cluster in RAM with 5 different read/write workloads and show that the r3.2xlarge instance delivers the best price/performance.
The Apache HBase community has released Apache HBase 1.0.0. Seven years in the making, it marks a major milestone in the Apache HBase project’s development, offers some exciting features and new API’s without sacrificing stability, and is both on-wire and on-disk compatible with HBase 0.98.x.
At TechEd Europe 2014, Microsoft announced the preview of Azure Stream Analytics. Stream Analytics is a real-time event processing engine that helps uncover insights from devices, sensors, infrastructure, applications, and data. With out-of-the-box integration to Event Hubs, the combined solution can both ingest millions of events as well as do analytics to better understand patterns, power a dashboard, detect anomalies, and kick off an action while data is being streamed in real-time.
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