Thought for a long time, ready to write a series of articles, record over the years the thought, feel more content don’t know where to start, draw a mind map to determine the direction of the big, big data is known to all the mainstream technology of iterative change quickly, there will be new things to join, so the picture content will be added constantly depending on the situation. I will decide the details as I write. You can also give me some suggestions. I will update this map and the following table of contents in real time according to the content I write.

The grouping of big data components above is actually quite tricky, especially as a programmer with obsessive-compulsive disorder, some components seem to be ok in other groups, and I don’t want to be divided into too many groups, it will look messy, so the grouping method in the above picture is a little subjective. Grouping is by no means absolute. For example, message queues like Kafka are not usually grouped together with other databases or file systems like HDFS, but they all have distributed persistence, so they are grouped together. And openTsDB this temporal database, a database is really just an application based on HBase, I think this thing is more focused on the query and with what kind of way to store, and is not in the store itself, so it subjectively on distributed computing and query this category, and OLAP tools are also in this group. The same situation still exists a lot, everybody has the objection also can speak out to discuss.

We all know that the technology of big data is changing rapidly, and to stay competitive as a programmer you have to keep learning. The purpose of writing these articles is relatively simple. First, it can be used as a note to comb the knowledge points. Second, I hope to help some people understand and learn big data. Each essay should be short and keep your reading time to 5-10 minutes. My public account is updated synchronously. Like to see the public number of students can pay attention to the article, the length of the article will not be too long, will not take up too much of your reading time, spend a little time every day to learn, long-term accumulation will always have harvest.

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