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Modern Big Data systems often include structures called Data Lakes. In the industry vernacular, a Data Lake is a massive storage and processing subsystem capable of absorbing large volumes of structured and unstructured data and processing a multitude of concurrent analysis jobs. Amazon Simple Storage Service S3 is a popular choice nowadays for Data Lake infrastructure as it provides a highly scalable, reliable, and low-latency storage solution with little operational overhead. However, while S3 solves a number of problems associated with setting up, configuring and maintaining petabyte-scale storage, data ingestion into S3 is often a challenge as types, volumes, and velocities of source data differ greatly from one organization to another. In this blog, I will discuss our solution, which uses Amazon Kinesis Firehose to optimize and streamline large-scale data ingestion at MeetMe, which is a popular social discovery platform that caters to more than a million active daily users.
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I encourage them to go in to as much detail as they would like. The answers let me know how well they understand and can explain a complex concept. Some candidates will sum up the entire process in a sentence or two. Others have filled an entire whiteboard with detailed diagrams. At a very simple level, here are the steps that I like to see If you know much about HTTP, you know that I have failed to mention the SSL handshake, cookies, response codes, caches, content distribution networks, and all sorts of other details: A TCP connection is made to the remote server.
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New – HTTP/2 Support for Amazon CloudFront
Distinctive features of our service are the following: You can spend your remuneration any way you like! You can either use it to buy services on Get-SMS or request a withdrawal into your electronic purse. The link, which you can use to invite users on the service, is in in your personal account.
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