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Ever wondered how a platform like LinkedIn handles the sheer scale of its data - enough to fill over a billion USB drives? Behind the scenes, they’ve been pushing Hadoop’s limits, managing an exabyte of data while tackling challenges like keeping things fast, secure, and endlessly scalable. From clever tweaks to Java heap management to building tools like Wormhole for lightning-fast data transfers, their journey offers a fascinating look into the art (and science) of mastering Big Data. Curious to dive deeper? Let’s unravel the story!
slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
By Jakub SlysEver wondered how a platform like LinkedIn handles the sheer scale of its data - enough to fill over a billion USB drives? Behind the scenes, they’ve been pushing Hadoop’s limits, managing an exabyte of data while tackling challenges like keeping things fast, secure, and endlessly scalable. From clever tweaks to Java heap management to building tools like Wormhole for lightning-fast data transfers, their journey offers a fascinating look into the art (and science) of mastering Big Data. Curious to dive deeper? Let’s unravel the story!
slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.