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two comments, each part is around 100 words Part 1: As the data analytics manager, my first task would be to review the data elements that will be involved in this proposed system. The requirements ask for health data trends of patients but a lot of that information will be protected by HIPAA rules. So we’ll have to first identify the PII data elements and design appropriate data governance processes to protect them. My proposed solution for these requirements would be to build a dashboard and accompanying reports for the health trend data. The steps involved to build such a system are: 1. Build a Spark process that periodically reads data from the MySQL corporate database and write it to HDFS.(An alternative approach would be to leave the data where it is and use Apache Presto or other data federation tools to query as needed at runtime to visualize the dashboard) 2. The patient monitoring equipment in the facilities can either be sourced through a REST interface or through a setup like MQTT broker and ingested in to Kafka topics which can then operated on by Spark ETL processes that transform them in to structured data sets with pre-defined schemas and written in to HDFS. (Kai Wehner, 2019) 3. A different Spark Process can then produce periodic reports (daily/weekly etc.) with appropriate level of aggregation to track health trends among the facilities. This process would join the patient history corporate dataset with the actual health metrics from the monitoring equipment and identify health trends among multiple dimensions (age, gender, race etc.) This would help provide the required information to the customers without exposing any of the PII data. 4. Additionally, the system can also include a visualization component based on Apache Superset or other similar BI software to let the customers build interactive dashboards and other visualizations on the dataset. My most important considerations for the solution would be data security, reliability and providing the end-user with a self-service analytics platform. Part 2: As the data analytics manager, I will architect a fact-based model based on the lambda architecture to store and analyze the data for solving the current as well as any future problems. I will use Apache Sqoop for the initial import of MySQL data into Hadoop and then tracking each filed of the user data in a separate table along with the initial timestamp. This will be my master dataset. I will deprecate the MySQL database from this point on. All the future transactions will be carried out on my Bigdata solution. The master dataset will be immutable. All the subsequent updates to the patient information, along with vital stats readings, will be appended to the respective field tables along with the timestamp of the update. This will create a new record every time the Patient information changes. We can query for the latest patient information and get the result by combining the results with the latest timestamps from all the field tables. This system has the following advantages: When the patient record changes, we only update the changed field tables. We do not have to update the entire patient record for every new vital stats measurement. It requires less space as we are just storing the delta. Each data entry is true in perpetuity. It is human fault-tolerant as we can simply delete the erroneous record. The previous entry will ensure that data integrity is not lost. I will create interactive dashboards by querying the field tables to see past trends and predict future trends in the patient’s vital stats. We can query specific historical data and create a snapshot of patient health at a particular point in time by querying the field tables with appropriate time-based queries.


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