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Next, we have **image compression**. Image compression is the process of reducing an image's file size. This can be done in two ways: lossy compression and lossless compression. Lossy compression, as mentioned before, reduces file size by discarding some image data. This is great for photographs because you can often reduce the file size significantly without noticeably affecting the image quality. Lossless compression, on the other hand, preserves all the image data, which is ideal for images with sharp lines, text, and transparency. Tools like TinyPNG and ImageOptim allow you to compress images without losing too much quality. Make sure you play around with the compression settings to find the right balance for your images.
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Now, let's move on to Transformations. A transformation is an operation that creates a new RDD from an existing RDD. Transformations are lazy, meaning they are not executed immediately. Instead, Spark builds up a lineage graph of transformations, which is a record of all the transformations that need to be applied to the data. This lineage graph is used to optimize the execution plan and recover from failures.
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