x******r
发帖数: 367 | 1 大家好！
请问大家做大数据报表都用什么工具？Cognos, Tableau, Baidu报表，R，还是其他工
具？你使用这个工具的原因是什么？ | l****g
发帖数: 761 | 2 big data 就是个虚词
一百个公司一百种定义方法
最后 cs 的人基本不做报表 | x******r
发帖数: 367 | 3 大家好！
请问大家做大数据报表都用什么工具？Cognos, Tableau, Baidu报表，R，还是其他工
具？你使用这个工具的原因是什么？ | l****g
发帖数: 761 | 4 big data 就是个虚词
一百个公司一百种定义方法
最后 cs 的人基本不做报表 | z***a
发帖数: 5 | 5 big data engineer和data scientist做的是不一样的工作。可以看下面的解释，来源
是https://bigdatauniversity.com/blog/data-scientist-vs-data-engineer/
对CS、大数据感兴趣的话, 我们有一个资料分享群，交流心得和资源哈。我的微信号是
（不想被搜索引擎弄走）："ada"+"da"+两个zebra的"z"。所有引号里的连起来没有空
格没有加号就好啦！
Data Engineer
Data Engineers are the data professionals who prepare the “big data”
infrastructure to be analyzed by Data Scientists. They are software
engineers who design, build, integrate data from various resources, and
manage big data. Then, they write complex queries on that, make sure it is
easily accessible, works smoothly, and their goal is optimizing the
performance of their company’s big data ecosystem.
They might also run some ETL (Extract, Transform and Load) on top of big
datasets and create big data warehouses that can be used for reporting or
analysis by data scientists. Beyond that, because Data Engineers focus more
on the design and architecture, they are typically not expected to know any
machine learning or analytics for big data.
Skills and tools: Hadoop, MapReduce, Hive, Pig, MySQL, MongoDB, Cassandra,
Data streaming, NoSQL, SQL, programming.
Data Scientist
A data scientist is the alchemist of the 21st century: someone who can turn
raw data into purified insights. Data scientists apply statistics, machine
learning and analytic approaches to solve critical business problems. Their
primary function is to help organizations turn their volumes of big data
into valuable and actionable insights.
Moreover, Data Scientists are also expected to interpret and eloquently
deliver the results of their findings, by visualization techniques, building
data science apps, or narrating interesting stories about the solutions to
their data (business) problems.
Data Scientists may sometimes be presented with big data without a
particular business problem in mind. In this case, the curious Data
Scientist is expected to explore the data, come up with the right questions,
and provide interesting findings! This is tricky because, in order to
analyze the data, a strong Data Scientists should have a very broad
knowledge of different techniques in machine learning, data mining,
statistics and big data infrastructures.
They should have experience working with different datasets of different
sizes and shapes, and be able to run his algorithms on large size data
effectively and efficiently, which typically means staying up-to-date with
all the latest cutting-edge technologies. This is why it is essential to
know computer science fundamentals and programming, including experience
with languages and database (big/small) technologies.
Skills and tools: Python, R, Scala, Apache Spark, Hadoop, data mining tools
and algorithms, machine learning, statistics. |
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