Summary

Throughout this chapter, we covered essential technologies in modern computing and data management. We began by discussing batch ingestion, a method whereby large volumes of data are collected and processed at scheduled intervals, offering efficiency and cost-effectiveness for organizations with predictable data flows. In contrast, we explored streaming ingestion, which allows data to be processed in real-time, enabling immediate analysis and rapid response to changing conditions. We followed with streaming services such as Kafka for real-time data processing. We moved to SQL and NoSQL databases—such as PostgreSQL, MySQL, MongoDB, and Cassandra—highlighting their strengths in structured and flexible data storage, respectively. We explored APIs such as REST for seamless system integration. Also, we delved into file systems, file types, and attributes, alongside cloud storage solutions such as Amazon S3 and Google Cloud Storage, emphasizing scalability and data management strategies. These technologies collectively enable robust, scalable, and efficient applications in today’s digital ecosystem.

In the upcoming chapter, we will dive deep into the critical aspects of data quality and its significance in building reliable data products. We’ll explore why ensuring high data quality is paramount for making informed business decisions, enhancing customer experiences, and maintaining operational efficiency.