This course serves as an introduction to the technical and engineering aspects of near real-time data analytics.
The core idea of data analytics is to generate relevant information and insights from unrefined organizational data. Data analytics focuses on guiding the decision-making process, i.e. data-driven decision making - using data and facts, rather than intuition. The concepts related to data analytics include data warehouses, data lakes and big data.
We are seeing data grow in size, speed and variety. At the same time, organizations have an expectation that time-to-insight should accelerate. It is no longer enough to have analytics available from the previous day - decision-makers want to see data in near real-time. The use cases for applying near real-time analytics include handling user location data, fraud detection, marketing, Internet-of-Things and any other field where integrating and analyzing data in motion generates business value.
Basic knowledge of SQL and Python/Java is beneficial for taking this course.
Kristo Raun, Junior Research Fellow of Big Data
Students who complete the course will have:
– An understanding of how to setup stream processing and near real-time analytics services
– Practical experience with state-of-the-art big data tools and frameworks, such as Kafka and Flink.
– Knowledge about different solution architectures and known tradeoffs.
Fee info: EUR 300
For further information, please click the "LINK TO ORIGINAL" button below.