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For investors in the currency markets, fundamental analysis means analyzing the market using non-technical sources like news and other events. Furthermore, in today's FX Market, one of the most consistency news pieces that resort in a very sharp movement in the forex market is the US Non-farm payroll(NFP).
In this big data project, we want to measure by how much NFP has triggered moves in past markets. We will analyze the news numbers versus the price movement that it resulted in. This information can be very useful to gauge by how much the market will move in the event of any future news. This will enable investors to make splits seconds decision as to where to enter the market and what target points should be expected in the event of NFP news.
While the data prepared from this big data project using apache spark will be a basis for a machine learning predictive system, we will be focusing on the machine learning portion of this class in other upcoming projects. This class will be on how to gather and prepare data for this purpose of inference and prediction.
In this project, we will look at running various use cases in the analysis of crime data sets using Apache Spark.
In this project, we will look at Cassandra and how it is suited for especially in a hadoop environment, how to integrate it with spark, installation in our lab environment.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.