Meet me desktop
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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. The Data Science team at MeetMe needed to collect and store approximately 0.
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