Oliver’s business is now growing. He built a company and has sales agents and sales supervisors working for him all over England. He realizes using google spreadsheets is not sustainable. He wants to invest in a scalable system and he is thinking to recruit a software engineer and a data engineer (you). The software engineer has to come up with a mobile app that allows the sales agents to report their sales from the app. The app should be connected with a data pipeline that would eventually make the data ready for analysis. – What would be your strategy to build a strong and reliable data pipeline and how will you structure it ? Explain the different steps for processing the data, what makes each step important? Mention which tools would you use for each of these steps. Feel free to argument as much as you feel it relevant You’re free to make assumptions about the mobile app e.g. The raw data is stored in a No-Sql database…

Building a strong and reliable data pipeline is crucial for ensuring that the data collected from the mobile app is processed and prepared for analysis in an efficient and accurate manner. In this scenario, where the raw data is stored in a No-SQL database, it is important to consider the following steps for processing the data and the tools that can be used for each step.

1. Data Ingestion: The first step is to retrieve the raw data from the No-SQL database and bring it into the data pipeline for further processing. In this case, we can use tools like Apache Kafka or Apache Nifi, which are widely used for data ingestion and provide reliable and scalable mechanisms for collecting data from various sources.

2. Data Cleaning: Once the data is ingested, it is necessary to clean and transform it to ensure its quality and consistency. This step involves removing irrelevant or inconsistent data, fixing errors, and standardizing the data format. Tools like Apache Spark or Apache Beam can be used for data cleaning and transformation tasks as they provide powerful processing capabilities and a wide range of built-in functions.

3. Data Integration: After cleaning the data, it may be necessary to integrate it with other datasets or sources to enrich the analysis. This step involves combining data from different sources into a unified format. Tools like Apache Hadoop or Apache Flink can be used for data integration tasks as they support distributed processing and provide efficient ways to merge and combine large volumes of data.

4. Data Storage: The processed and integrated data needs to be stored in a suitable format for further analysis. Depending on the specific requirements of the analysis, different storage options can be considered. For example, structured data can be stored in a relational database like MySQL or PostgreSQL, while unstructured data can be stored in a data lake using technologies like Apache Hadoop or Apache Hive.

5. Data Transformation: In some cases, it may be necessary to perform additional transformations on the data to meet the specific analysis requirements. This step involves applying statistical or mathematical operations, aggregating data, or creating derived variables. Tools like Python or R can be used for data transformation as they provide a wide variety of libraries and functions for data manipulation and analysis.

6. Data Analysis: The final step in the data pipeline is to perform the actual analysis on the processed data. This can involve various statistical or machine learning techniques depending on the specific objectives of the analysis. Tools like Python or R, along with libraries such as Pandas or Scikit-Learn, can be used for data analysis as they provide a rich set of functions and algorithms for statistical modeling and machine learning.

Overall, building a strong and reliable data pipeline involves careful consideration of each step in the data processing workflow. It requires selecting the appropriate tools for each step based on their capabilities, performance, and compatibility with the existing infrastructure. By following these steps and utilizing the right tools, Oliver can ensure that the data collected from the mobile app is processed efficiently and prepared for analysis in a reliable manner.

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