Upon completing the course, you will:
Master Kafka, Spark Streaming, Flink, NiFi, and Storm for advanced real-time big data ingestion, query, and aggregation.
Utilize HBase, Cassandra, Hive, and HDFS for big data storage.
Establish Machine Learning frameworks using Spark MLlib, CoreNLP, Algebird, and TensorFlow.
Build up real-time large-scale data processing and recommendations.
Build an end-to-end, real-time streaming data analytics and Machine Learning reference data pipeline project based on the lambda architecture
It is hard, but you can do it. Plus, we are here to help.
Principal Big Data Engineer
Bin is a principal big data engineer at one of the big financial groups in Toronto. He has also worked with Invesco, RBC, CIBC, CGI, and ING to lead teams in creating enterprise application architecture: Portal, SOA, ESB, EAI, BPM, and ECM/CMS. With over 15 years of work experience in all phases of software development, he is now a big data practitioner in architecting and implementing real-time advanced big data analytics applications and recommendation engines. He has accumulated extensive experience in building an enterprise big data platform with the Hadoop ecosystem and delivering big data solutions. Essentially, Bin is a very skilled architect with cross-industry, cross-functional, and cross-domain know-how.
At WeCloudData, we believe that when you fail fast, you learn faster. WeCloudData’s teaching assistants and program coordinator are committed to providing guidance and support throughout and after the course.