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Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.
With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc.
Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.
Programming Languages (Python, R, SQL) Proficiency in programming languages is crucial. SQL is indispensable for database management and querying. This knowledge allows the design of experiments, hypothesistesting, and the derivation of conclusions from data.
Here are some of the most common backgrounds that prepare you well: Mathematics and Statistics These disciplines provide a rock-solid understanding of data analysis, probability theory, statistical modelling, and hypothesistesting – all essential tools for extracting meaning from data.
Accordingly, you need to make sense of the data that you derive from the various sources for which knowledge in probability, hypothesistesting, regression analysis is important. You should be skilled in using a variety of tools including SQL and Python libraries like Pandas.
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